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Data is the new oil and is just as crude as unrefined oil. To do anything meaningful - modeling, visualization, machine learning, for predictive analysis – you first need to wrestle and wrangle with data. We recently interviewed Dr. Tirthajyoti Sarkar and Shubhadeep Roychowdhury, the authors of the course Data Wrangling with Python. They talked about their new course and discuss why do data wrangling and why use Python to do it. Key Takeaways Python boasts of a large, broad library equipped with a rich set of modules and functions, which you can use to your advantage and manipulate complex data structures NumPy, the Python library for fast numeric array computations and Pandas, a package with fast, flexible, and expressive data structures are helpful in working with “relational” or “labeled” data. Web scraping or data extraction becomes easy and intuitive with Python libraries, such as BeautifulSoup4 and html5lib. Regex, the tiny, highly specialized programming language inside Python can create patterns that help match, locate, and manage text for large data analysis and searching operations Present interesting, interactive visuals of your data with Matplotlib, the most popular graphing and data visualization module for Python. Easily and quickly separate information from a huge amount of random data using Pandas, the preferred Python tool for data wrangling and modeling. Full Interview Congratulations on your new course ‘Data wrangling with Python’. What this course is all about? Data science is the ‘sexiest job’ of 21st century’ (at least until Skynet takes over the world). But for all the emphasis on ‘Data’, it is the ‘Science’ that makes you - the practitioner - valuable. To practice high-quality science with data, first you need to make sure it is properly sourced, cleaned, formatted, and pre-processed. This course teaches you the most essential basics of this invaluable component of the data science pipeline – data wrangling. What is data wrangling and why should you learn it well? “Data is the new Oil” and it is ruling the modern way of life through incredibly smart tools and transformative technologies. But oil from the rig is far from being usable. It has to be refined through a complex processing network. Similarly, data needs to be curated, massaged and refined to become fit for use in intelligent algorithms and consumer products. This is called “wrangling” and (according to CrowdFlower) all good data scientists spend almost 60-80% of their time on this, each day, every project. It generally involves the following: Scraping the raw data from multiple sources (including web and database tables), Inputing, formatting, transforming – basically making it ready for use in the modeling process (e.g. advanced machine learning), Handling missing data gracefully, Detecting outliers, and Being able to perform quick visualizations (plotting) and basic statistical analysis to judge the quality of your formatted data This course aims to teach you all the core ideas behind this process and to equip you with the knowledge of the most popular tools and techniques in the domain. As the programming framework, we have chosen Python, the most widely used language for data science. We work through real-life examples and at the end of this course, you will be confident to handle a myriad array of sources to extract, clean, transform, and format your data for further analysis or exciting machine learning model building. Walk us through your thinking behind how you went about designing this course. What’s the flow like? How do you teach data wrangling in this course? The lessons start with a refresher on Python focusing mainly on advanced data structures, and then quickly jumping into NumPy and Panda libraries as fundamental tools for data wrangling. It emphasizes why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of specialized pre-built routines in Python. Thereafter, it covers how using the same Python backend, one can extract and transform data from a diverse array of sources - internet, large database vaults, or Excel financial tables. Further lessons teach how to handle missing or wrong data, and reformat based on the requirement from a downstream analytics tool. The course emphasizes learning by real example and showcases the power of an inquisitive and imaginative mind primed for success. What other tools are out there? Why do data wrangling with Python? First, let us be clear that there is no substitute for the data wrangling process itself. There is no short-cut either. Data wrangling must be performed before the modeling task but there is always the debate of doing this process using an enterprise tool or by directly using a programming language and associated frameworks. There are many commercial, enterprise-level tools for data formatting and pre-processing, which does not involve coding on the part of the user. Common examples of such tools are: General purpose data analysis platforms such as Microsoft Excel (with add-ins) Statistical discovery package such as JMP (from SAS) Modeling platforms such as RapidMiner Analytics platforms from niche players focusing on data wrangling such as – Trifacta, Paxata, Alteryx At the end of the day, it really depends on the organizational approach whether to use any of these off-the-shelf tools or to have more flexibility, control, and power by using a programming language like Python to perform data wrangling. As the volume, velocity, and variety (three V’s of Big Data) of data undergo rapid changes, it is always a good idea to develop and nurture significant amount of in-house expertise in data wrangling. This is done using fundamental programming frameworks so that an organization is not betrothed to the whims and fancies of any particular enterprise platform as a basic task as data wrangling. Some of the obvious advantages of using an open-source, free programming paradigm like Python for data wrangling are: General purpose open-source paradigm putting no restriction on any of the methods you can develop for the specific problem at hand Great eco-system of fast, optimized, open-source libraries, focused on data analytics Growing support to connect Python for every conceivable data source types, Easy interface to basic statistical testing and quick visualization libraries to check data quality Seamless interface of the data wrangling output to advanced machine learning models – Python is the most popular language of choice of machine learning/artificial intelligence these days. What are some best practices to perform data wrangling with Python? Here are five best practices that will help you out in your data wrangling journey with Python. And in the end, all you’ll have is clean and ready to use data for your business needs. Learn the data structures in Python really well Learn and practice file and OS handling in Python Have a solid understanding of core data types and capabilities of Numpy and Pandas Build a good understanding of basic statistical tests and a panache for visualization Apart from Python, if you want to master one language, go for SQL What are some misconceptions about data wrangling? Though data wrangling is an important task, there are certain myths associated with data wrangling which developers should be cautious of. Myths such as: Data wrangling is all about writing SQL query Knowledge of statistics is not required for data wrangling You have to be a machine learning expert to do great data wrangling Deep knowledge of programming is not required for data wrangling Learn in detail about these misconceptions. We hope that these misconceptions would help you realize that data wrangling is not as difficult as it seems. Have fun wrangling data! About the authors Dr. Tirthajyoti Sarkar works as a Sr. Principal Engineer in the semiconductor technology domain where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. Shubhadeep Roychowdhury works as a Sr. Software Engineer at a Paris based Cyber Security startup. He holds a Master Degree in Computer Science from West Bengal University Of Technology and certifications in Machine Learning from Stanford. 5 best practices to perform data wrangling with Python 4 misconceptions about data wrangling Data cleaning is the worst part of data analysis, say data scientists

IntroductionIn the ever-evolving world of web development, the need for rapid prototyping and efficient application creation has never been more paramount. "Web App Development Made Simple with Streamlit" is a book born out of this necessity. Streamlit, a powerful and intuitive tool, has transformed the way developers approach web app development, making it accessible to both seasoned programmers and beginners alike. This article delves into the core technologies discussed in the book, explores adjacent topics, and shares my personal journey in writing it.In-Depth Analysis of Streamlit TechnologyStreamlit is an open-source Python library that simplifies the development of web applications. Unlike traditional frameworks such as Django or Flask, Streamlit focuses on ease of use and rapid development. It allows developers to build interactive, data-driven web applications with minimal code.Key Features of Streamlit1. Simplicity and Speed: Streamlit's API is designed to be straightforward. With just a few lines of code, you can create a fully functional web app. This speed is crucial for data scientists and analysts who need to visualize data quickly. 2. Interactive Widgets: Streamlit provides a variety of widgets (e.g., sliders, buttons, and text inputs) that make it easy to create interactive applications. These widgets are essential for building user-friendly interfaces. 3. Real-Time Updates: Streamlit apps update in real-time as you modify the code, providing immediate feedback. This feature is particularly useful during the development phase, allowing for rapid iteration. 4. Integration with Python Ecosystem: Streamlit seamlessly integrates with popular Python libraries such as NumPy, Pandas, and Matplotlib. This compatibility ensures that developers can leverage existing tools and libraries.Solving Real-World ProblemsStreamlit addresses several challenges in web app development: Rapid Prototyping: Traditional web development frameworks often require significant setup and boilerplate code. Streamlit eliminates this overhead, enabling developers to focus on the core functionality of their applications. Data Visualization: Streamlit excels in creating data-driven applications. Its integration with visualization libraries allows developers to build insightful dashboards and reports with ease. - Accessibility: Streamlit's simplicity makes web development accessible to non-experts. Data scientists and analysts, who may not have extensive web development experience, can create powerful applications without needing to learn complex frameworks.Adjacent Topics: Best Practices and Emerging TrendsWhile Streamlit is a powerful tool, understanding best practices and emerging trends in web development is crucial for maximizing its potential.Best Practices1. Modular Code: Break your application into smaller, reusable components. This modular approach makes the codebase more manageable and easier to maintain. 2. Version Control: Use version control systems like Git to track changes and collaborate with others. Streamlit's simplicity should not deter you from following industry standard practices. 3. Testing: Implement automated testing to ensure your application behaves as expected. Libraries like pytest can be integrated with Streamlit applications for this purpose.Emerging Trends1. Low-Code/No-Code Platforms: Streamlit is part of a broader trend towards low-code and no-code platforms, which aim to democratize software development. These platforms enable users to build applications without extensive programming knowledge. 2. AI and Machine Learning Integration: Streamlit's ease of use makes it an ideal platform for deploying machine learning models. Expect to see more integration with AI tools and libraries in the near future. 3. Collaborative Development: Tools that facilitate collaboration among developers, data scientists, and domain experts are gaining traction.Streamlit's simplicity and real-time updates make it a natural fit for collaborative projects.My Writing JourneyWriting "Web App Development Made Simple with Streamlit" was a journey filled with both challenges and triumphs. The idea for the book stemmed from my own experiences as a developer struggling with the complexities of traditional web development frameworks. Iwanted to create a resource that would simplify the process and empower others to build their applications.Challenges 1. Staying Updated: The world of technology is constantly evolving. Keeping up with the latest updates and features in Streamlit was a significant challenge. I spent countless hours researching and experimenting to ensure the book was up-to-date. 2. Balancing Depth and Accessibility: Striking the right balance between providing in-depth technical information and making the content accessible to beginners was a delicate task. I aimed to cater to a broad audience without overwhelming them with jargon. 3. Time Management: Writing a book while juggling professional and personal commitments required meticulous time management. Setting realistic deadlines and sticking to a writing schedule was crucial.Overcoming Challenges1. Community Support: The Streamlit community was an invaluable resource. Engaging with other developers, participating in forums, and attending webinars helped me stay informed and inspired. 2. Iterative Writing: I adopted an iterative writing process, revisiting and refining chapters based on feedback from beta readers. This approach ensured the content was both accurate and user-friendly. 3. Personal Passion: My passion for simplifying web development kept me motivated. The thought of helping others overcome the same challenges I faced was a powerful driving force.Problem-Solving with StreamlitThe primary problem I aimed to solve with Streamlit was the steep learning curve associated with traditional web development frameworks. Many developers, especially those transitioning from data science or academia, struggle with the intricacies of setting up servers, managing databases, and creating user interfaces.Significance of the ProblemBarrier to Entry: The complexity of traditional frameworks can deter aspiring developers from pursuing web development. Streamlit lowers this barrier, making it accessible to a wider audience. Inefficiency: Time spent on boilerplate code and setup can be better utilized for developing core functionalities. Streamlit's simplicity allows developers to focus on solving real-world problems.SolutionStreamlit provides a streamlined approach to web development. By abstracting away the complexities, it empowers developers to create applications quickly and efficiently. My book offers practical examples and step-by-step guides to help readers harness the full potential of Streamlit.Unique Insights and Experiences"Web App Development Made Simple with Streamlit" offers several unique insights, drawn from my personal experiences and expertise.Key Insights 1. Real-World Applications: The book features case studies and real-world examples that demonstrate how Streamlit can be used to solve practical problems. These examples provide readers with a clear understanding of the tool's capabilities. 2. Hands-On Approach: Each chapter includes hands-on exercises and projects, encouraging readers to apply what they've learned. This interactive approach ensures a deeper understanding of the material. 3. Community Contributions: The book highlights contributions from the Streamlit community, showcasing innovative applications and best practices. This collaborative spirit reflects the open-source nature of Streamlit.Personal ExperiencesLearning from Mistakes: My journey with Streamlit was not without its mistakes. I share these experiences in the book, offering readers valuable lessons and tips to avoid common pitfalls. Continuous Learning: Writing the book was a learning experience in itself. I discovered new features, explored advanced use cases, and deepened my understanding of web development.Conclusion"Web App Development Made Simple with Streamlit" is more than just a technical guide; it's a testament to the power of simplicity and accessibility in web development. Streamlit has revolutionized the way we approach application development, making it possible for anyone to create powerful, interactive web apps. Through this article and my book, I hope to inspire and empower others to embark on their own development journeys, armed with the knowledge and tools to succeed.Author BioRosario Moscato has a master's degree in electronic engineering, a second level master in internet software design and a first level master's in science and faith. In about 25 years of experience, he has worked on innovative technology development in Europe and Asia. Recently, his interests have been focused exclusively on AI, pursuing the goal of making every business extremely competitive and analyzing the ethical implications deriving from the new scenarios that these disciplines open. Rosario has authored two books, and he is a speaker at international research centres and conferences as well as a trainer and technical/scientific consultant. Currently, he is working as CTO with one of the oldest AI companies in Italy.

IntroductionChatGPT is a revolutionary new technology that is making a large impact on society.  The full impact of ChatGPT cannot be fully known at the time of writing this article because of how novel the technology is.  However, what can be said is that since its introduction many industries have been trying to leverage it and increase productivity.  Simultaneously, everyday people are trying to learn to leverage it as well.  Overall, ChatGPT and similar systems are very new and the full impact of how to leverage them will take some more time to fully manifest.  This article is going to explore how ChatGPT can be used for everyday life by exploring a few use cases.What is ChatGPT?       Before we begin, it is important to understand what ChatGPT is and what it isn’t.  To begin ChatGPT is in a lay sense a super advanced chatbot.  More specifically, ChatGPT is known as a generative AI that uses Natural Language Processing (NLP) to create a dialog between a user and itself.  ChatGPT and similar systems are what are known as Large Language Models (LLMs).  In short, for AI models to work they have to be trained using data.  To train LLMs engineers use vast amounts such as books, articles, journals, and so on.  The result is a system like ChatGPT that has a vast knowledge base on many different subjects.  Before we can explore how to use ChatGPT for everyday life we need to explore how NOT to use ChatGPT. How not to use ChatGPT?ChatGPT is very powerful and can be used for many different things; however, is important to understand that ChatGPT is not a sage nor infallible.  Remember ChatGPT only knows about what it was trained on.  This means if the information it was taught was wrong or outdated so too will be the response it provides.  This means that as of writing this article ChatGPT cannot and should not be used as a replacement for humans.  Even with the answer ChatGPT gives, a decent level of domain knowledge is still required properly format and use its response.  As such, it is important to take suggestions with a certain amount of skepticism.  The best way to think of ChatGPT is as an extremely smart friend with a wide range of knowledge.  Though that friend may be very knowledgeable they can and will be wrong from time to time.  With that how does one use ChatGPT?How to use ChatGPT for everyday life?Using ChatGPT starts with what is called a prompt.  A prompt can be thought of as a query that the user feeds into ChatGPT.  This prompt is kind of like a Google search.  It will be a statement that asks ChatGPT a question or to perform a certain task.  Since ChatGPT used NLP, the prompt does not have to be formatted a certain way nor does it require a specific syntax.  In other words, a prompt can be entered the same way a user would pose the instruction to another person.  However, it is good to provide ChatGPT will enough information to adequately describe the problem.  An example, of a prompt is as follows:How can ChatGPT be used for everyday life?Once the prompt is run ChatGPT will produce text with suggestions, answers, programming software, or whatever was asked for.  That output that is produced is called a response which will have the answer to the request/question. There are a lot of ways ChatGPT can be used for everyday life.  In terms of professional life, ChatGPT can make an excellent assistant.  For most “text-based” jobs, that is jobs that don’t require design such as drawing diagrams, ChatGPT can be an excellent assistant.  For example, suppose a programmer needs a small piece of software for a project, assume they need a SQL query that can pull all the rows between two dates.  The user could use a prompt like the following:Write a SQL query that will pull all rows between January 3 and March 31.This prompt will produce the following response:To pull all rows between January 3 and March 31 from a table, you'll need to use the WHERE clause to filter the results based on the date column. Assuming you have a date column named "date_column" in your table, here's the SQL query: sqlCopy code SELECT * FROM your_table WHERE date_column >= '2023-01-03' AND date_column <= '2023-03-31'; Replace "your_table" with the actual name of your table, and "date_column" with the name of the column that contains the dates you want to filter. The query will select all rows where the "date_column" is on or after January 3, 2023, and on or before March 31, 2023.For those that are not familiar with SQL, the prompt produced a simple query with generic placeholders that will pull the correct data out of a database. Not everyone is a programmer, so how could a non-programmer use ChatGPT as an assistant?  One area in which ChatGPT can be used is by composing emails.  Suppose a salesperson needs to inform Tom, their boss, that a $1 million deal was closed today via email.  They could use a prompt like the following:Write Tom an email saying that the deal with Company X went through, and we made $1 million.The response would be something along the lines of:Subject: Successful Deal with Company X - $1 Million Revenue Achieved Hi Tom, I hope this email finds you well. I am thrilled to share some exciting news with you! The deal with Company X has successfully gone through, and I am delighted to inform you that we have made an astounding $1 million in revenue from this collaboration. This achievement wouldn't have been possible without the hard work and dedication of our entire team. It reflects the trust and confidence our partners have in our capabilities, and I believe it marks a significant milestone for our company's growth and success. I want to express my gratitude to you for your valuable contributions and expertise that played a crucial role in making this deal a reality. Your efforts have been commendable, and I am truly grateful for your commitment to the success of this venture. Let's take a moment to celebrate this triumph together. Your exceptional skills and commitment to excellence have made this accomplishment possible, and I am genuinely proud to have you as part of our team. Looking forward to even more prosperous ventures in the future. Once again, congratulations to you and the entire team! Best regards, [Your Name] [Your Title/Position] [Company Name] [Email] [Phone Number]In this case, all the users would have to do is proofread the email, copy it, and send it.  Now, for applications such as this proofreading is very important.  As stated before, ChatGPT is not a mind reader nor a sage, as such it may produce emails with wrong information, illogical wording, or anything else.  So, it’ll pay to read the message before sending it. Learning ToolA very powerful use of ChatGPT is as a learning tool.  For example, students can use this tool to help them master a new subject, generate practice problems, and even check answers to an extent.  Assume the user needs a few extra practice problems for their English homework, they could enter a prompt like the following:Generate some grammar practice problems for a 1st year English classWhich would yield the following output:Of course! Here are some grammar practice problems suitable for a 1st-year English class: 1. Subject-Verb Agreement: Choose the correct verb form that agrees with the subject in each sentence: a) The cat (purr/purrs) when it is happy. b) My friend and I (is/are) going to the movies tonight. c) Neither the students nor the teacher (understands/understand) the assignment. 2. Pronoun Usage: Select the appropriate pronoun to complete each sentence: a) She invited Sarah and (I/me) to the party. b) My sister and (her/she) are going shopping. c) The book is for (he/him) and his brother. 3. Articles (a/an/the): Insert the correct article (a, an, the, or no article) in each sentence: a) Can you pass me (a/an) apple, please? b) I need to buy (the) new shoes for the dance. c) My friend has (no article) car. 4. Prepositions: Choose the correct preposition to complete each sentence: a) We arrived (in/on/at) the airport fifteen minutes early. b) The cat is sleeping (under/below/above) the table. c) She's going to the store (by/with/for) her mom. 5. Verb Tenses: Identify the correct verb tense used in each sentence: a) They (will be/was) studying for the exam all night. b) By the time we arrived, the party (had started/has started). c) I (am/was) watching a movie right now. 6. Adjective vs. Adverb: Decide whether the word in parentheses should be an adjective or adverb in the sentence: a) She sings (beautiful/beautifully). b) The (happy/happily) children played in the park. c) The (quick/quickly) fox jumped over the fence. 7. Negation: Add the necessary negative word to each sentence: a) I have (no/not) seen him today. b) She does (not/none) like coffee. c) We will (not/none) be attending the party. 8. Passive Voice: Rewrite the following sentences in the passive voice: a) The chef cooked the meal. b) The teacher is explaining the lesson. c) The storm destroyed the old building. 9. Comparative and Superlative Forms: Complete each sentence with the correct comparative or superlative form of the adjective: a) This book is (interesting/more interesting/most interesting) than the last one. b) She is (tall/taller/tallest) than her sister. c) It was the (big/biggest/bigger) cake at the party. 10. Reported Speech: Rewrite the following sentences using reported speech: a) Mary said, "I am going to the store." b) "I will call you later," said John. c) "They have arrived," she said. These grammar practice problems aim to reinforce essential grammar rules and concepts, helping 1st-year English students develop their language skills and proficiency.As can be seen, ChatGPT generated a number of questions that one would usually find in an English 101 class.  Now, if the user wanted a more specific answer that could reword the prompt to accommodate. ChatGPT is also a great teaching tool as well.  Teachers can also use this tool to generate lesson plans, write intros for learning modules, generate problems, and more.  Overall, ChatGPT can be used as an excellent tool for academia in general.Now, it is important to remember that ChatGPT is not all-knowing.  It can only generate responses based on the data that it was trained on.  This means if the user is asking questions about a subject where there is little or no publicly available data the response probably isn’t going to be of the highest quality.  For example, if someone askedHow do I build a working time machine?They’re going to get the following:Building a working time machine, as depicted in science fiction, is currently beyond our technological capabilities and goes against the known laws of physics. Time travel, as popularly imagined, involves traveling backward or forward in time, similar to how we move through space. As of my last update in September 2021, time travel remains a concept explored mainly in theoretical physics, and no practical time machine has been built or proven to be possible. Several theories, such as the theory of relativity, suggest that time travel could be theoretically feasible under certain conditions, but these ideas are still purely theoretical and have not been put into practice. It's important to remember that building a time machine would present enormous scientific, technological, and ethical challenges. It could raise paradoxes, such as the "grandfather paradox," where a time traveler could potentially alter past events and create inconsistencies in history. As of now, time travel remains a fascinating topic in science fiction and theoretical physics, but it is not something that can be practically accomplished with our current understanding and technology. The scientific community continues to explore the nature of time and the universe, but building a working time machine as portrayed in movies and books is not yet a reality.ChatGPT gave some general information on time travel but unfortunately, it didn’t generate any schematics.  Again, this goes back to ChatGPT not being a sage as it only knows what it was “taught”.  Since there is no data on how to build a working time machine ChatGPT could not fully answer the prompt. Now, If one looks at the English question prompt above, one will notice that practice questions were quite broad.  It is common to have to “dig” with ChatGPT.  In other words, a person may have to refine the queriers to get what they need.  To get some practice try re-wording the prompt to generate practice questions for only passive writing.  SummaryChatGPT is a tool, and like any other tool, what it’s used for is up to the user.  As was seen in this article, ChatGPT is an excellent tool for helping a person through their day by generating software, emails, and so on.  ChatGPT can also be used as a great learning or teaching device to help students and teachers generate practice problems, create lesson plans, and so much more.  However, as was stated so many numerous times.  Unless ChatGPT has been trained on something it does not know about it.  This means that asking it things like how to build a time machine or domain specific concepts aren’t going to return quality responses.  Also, even if ChatGPT has been trained on the prompt, it may not always generate a quality response.  No matter the use case, the response should be vetted for accuracy.  This may mean doing a little extra research with the response given, testing the output, or whatever needs to be done to verify the response. Overall, ChatGPT at the time of writing this article is less than a year old.  This means that the full implication of using ChatGPT are not fully understood.  Also, how to fully leverage ChatGPT is not understood yet either.  What can be said is that ChatGPT and similar LLM systems will probably be the next Google.  In terms of everyday use, the only true inhibitors are the user's imagination and the data that was used to train ChatGPT.Author BioM.T. White has been programming since the age of 12. His fascination with robotics flourished when he was a child programming microcontrollers such as Arduino. M.T. currently holds an undergraduate degree in mathematics, and a master's degree in software engineering, and is currently working on an MBA in IT project management. M.T. is currently working as a software developer for a major US defense contractor and is an adjunct CIS instructor at ECPI University. His background mostly stems from the automation industry where he programmed PLCs and HMIs for many different types of applications. M.T. has programmed many different brands of PLCs over the years and has developed HMIs using many different tools.Author of the book: Mastering PLC Programming

As per a recent IDC study, the forecast for new jobs demanding Salesforce skills shows a huge surge from last year. The numbers reveal that the demand is set to create 3.3 million jobs in the Salesforce ecosystem by 2022.  Additionally, among Indeed’s top 10 best jobs include Salesforce-specific, Salesforce Administrator ranking 4th and Salesforce Developer ranking at 6th place. Though Salesforce admins are not developers, but they create easy-to-use dashboards, intelligent workflows and applications for any project. They keep the Salesforce users happy and business processes smart, hence they are high in demand. Companies, especially in the US, know the potential and value Salesforce admins bring and are making serious human capital investments. We recently interviewed, Enrico Murru, a Solution and Technical Architect, a platinum Salesforce partner and Salesforce MVP to discuss the Salesforce ecosystem, his Salesforce expert journey, various certifications for Salesforce admins, and how they enhance their careers. Enrico is the author of the latest edition of our book, Salesforce Advanced Administrator Guide. This guide extends beyond being an administrator certification and covers advanced platform features and functions such as configuration, automation, security, and customization. It is packed with exam-oriented questions and mock tests to help you earn advanced administrator credentials. On the Salesforce ecosystem and Enrico’s journey to becoming a Salesforce MVP As per a recent 10K Advisors research, the Salesforce ecosystem is innovating faster than the talent can keep pace. This has resulted in great career opportunities but also introduced challenges for Salesforce end-users. How is Salesforce dealing with the challenges? How can administrators and developers leverage growth opportunities in Salesforce? When I started working with Salesforce about 10 years ago, I had never heard about the Salesforce ecosystem in my life: honestly Italy was not a hot market at that time, that’s why my (small at the time) company had a chance to work with big customers...we were among the few Salesforce system integrators in our whole country, after all. About 4 to 5 years ago things changed dramatically and Italy finally aligned with the rest of the world: Salesforce was in high demand among all kinds of companies (small or huge, no difference). The Italian market is one of the fastest growing; we started growing more and more due to increasing number of customers joining us but we started suffering from lack of professionals. We built an internal academy but it wasn’t enough, we still needed (and currently need) more developers, administrators and business analysts, the demand has exceeded the supply! The amount of “free to access documentation” is huge, the Salesforce Ohana has produced tons of content with blogs, webinars and tens of books. When Salesforce delivered Trailhead to the world we all had a boost in training: learning Salesforce became ever easier! No surprise the number of people getting certified has increased drastically, and it’s not uncommon now to see people with 5, 10 or 20 certifications on their career backpack: you don’t need to stay hours and hours with your head in a book, now you can learn 15 minutes a time when you are free between your working tasks. This is a HUGE revolution: learn a bit often and you keep yourself always on the trail, for free! From now on, anyone can become a Salesforce trailblazer and start building their trail: a lot of people have decided to change jobs and dipped into the Salesforce world with few to no experience in computer science. However when it’s time to get a certification, especially when it is your first certification, Trailhead is not enough: you need some real-world experience (no Trailhead can prepare you enough, experience is an amazing fuel for increasing your overall knowledge). A book can be a good compromise to boost your knowledge while giving you the right amount of experience that the author melt on each topic, and that’s why I chose to start this amazing trail with Packt: I wanted to do something I’ve never done before (writing a book) while delivering then Ohana more chances to pass a certification...I guess this is a win-win situation! How did you start your journey of becoming a Salesforce expert? Did being a Java developer, help you in some way? What motivated you to make the choice? Good question and the answer is that I have to thank the randomness that we can encounter daily on our lives (we can call it destiny, if you prefer). I started working as a Java developer (I came from an Electronic Engineering MSc) for a small company in my local town (Cagliari, Italy). After a while I got bored of what I was doing (boredom is a fuel for me) then I decided to move to Ireland. I got immediately the day after I landed in Cork a new job with a great income (compared to what I was earning in Italy)...but I was not 100% sure if I wanted to move abroad and that’s why I rejected that position and got back to Italy (some say it was an act of cowardice, I partially agree but I was not ready to change my life so much at that time). After just 2 months from my return home, my boss told me about a new opportunity: moving to northern Italy to join WebResults, a small company (we were just 15 people, including the CEO and CTO) that worked with something called “Salesforce”. I accepted the challenge and moved for 6 months with my spouse-to-be to WebResults headquarters: I discovered the world of Salesforce and I immediately fell in love with it. In a few weeks I learnt all that I needed to start my journey as a Salesforce developer. Years to come, I’m still working with WebResults (that in the meanwhile has been acquired by Engineering Spa, the greatest Italian consultant company) as a Salesforce Solution and Technical Architect (the amount of time I spend on coding at work has dramatically dropped unfortunately) and with the honorable Salesforce MVP title I try to evangelize my company and all the Salesforce Ohana buddies anyway I can! So if you ask me if my Java dev position helped me to arrive where I am, the answer is “definitely yes” but there is a lot more in the story! On various Salesforce certifications and why he wrote a book There are many certifications available for beginners as well as for experienced CRM developers. How should one go about choosing them? How do different Salesforce certification programs enhance a developer’s career? If you want to start your journey with Salesforce you have to choose primarily among the following paths (more details at https://trailhead.salesforce.com/credentials/administratoroverview, but you can build your own trail!): Administrator Developer Marketer Consultant Architect In my experience any aspiring Salesforce consultant should start from bases, even though she is a skilled business analysts with 20 years of experience: you need to know how the Salesforce Lightning Platform works and the best way is to get your hands dirty. Whether you wanna start as an administrator or a developer, I always recommend you face administrator skills at the beginning: a good developer should be a good administrator as well! As far as Marketer and Consultant paths are concerned, they are more related to your knowledge of specific products of the platform such as Marketing Cloud, Pardot, Field Service, Community Cloud, Einstein Analytics and many others. The Architect path brings you to the Mount Olympus of all certifications - the Technical Architect certification, which any Salesforce trailblazer aspire to get one day (and I’m one of them). Some think that owning a Salesforce certification doesn’t necessarily indicate your proficiency in the technologies involved but I do not agree with them. When I tried to get the Salesforce Advanced Administrator exam I really thought I had the required skills to pass but I failed...why? Because I didn’t study some of the topics and I wasn’t that skilled on such topics either (you’ll read this story in the book as well). That’s why I needed hours of study to pass the exam, and thanks to that deep study I learnt new Salesforce stuff and increased my proficiency in features I hadn’t actually ever used, making me the “most skilled” guy in my company regarding Omni-Channel or Salesforce Knowledge. This is an absolute win for both you and your company: certifications are meant to make you a trailblazer. Needless to say headhunters really love Salesforce certifications (my owning 20 certifications  attracts tens of contact requests on my social channels). Your book, Salesforce Advanced Administrator Certification Guide promises to give administrators a deeper knowledge of advanced Salesforce features for administrators. Why should one read this book? How is it different from other available Salesforce certification guides in the market? At first I want to say that the Salesforce Advanced Administrator Certification is a bit mistreated by administrators (as far as I’ve seen in my career): it is usually considered too hard or too complex for the skills you earn…”after all I’m already an administrator why should I become an advanced administrator”? You should my friend, the amount of things you learn is really huge, you’ll keep playing with features such as Lightning Knowledge, Omni-Channel, Live Chat, Lightning Content, features that maybe you’ve never used before, or exploring in depth the world of Salesforce automation with Process Builder, Lightning Flows, Entitlements and Approvals or knowing everything related to security and sharing of records (and many many more). Why should you choose this book? It covers extensively all required topics for the Salesforce Advanced Administrator certification keeping in mind the requirements for the exam as well. While the number of topics is too large for us to cover anything and everything for each topic, you’ll get a good amount of knowledge, suggestions and external references to ensure you reach the Salesforce Advanced Administrator certification goal. On the challenges faced by Salesforce administrators What are some of the challenges faced by Salesforce administrators today? How is Salesforce as a platform helping overcome these challenges? Can Salesforce administrators become developers too and vice versa? What is next for Salesforce? The biggest challenge that Salesforce admins face day after day is keeping pace with the extraordinarily growing Salesforce ecosystems: new companies join the Lightning Platform and new features are delivered release after release. It is more than mandatory that consultant companies and, in general, IT divisions reserve a percentage of their employees time for continuous learning, to allow Salesforce admins and devs to stay on track with the changing environment. Learning is a cost for sure, when you study you are not productive, but the benefits of a skilled and always on top employee overtakes its cost. And I see no obstacles for administrators to start their developer path as well: all they need is passion, curiosity and patience, Rome wasn’t built in a day and your developer skills won’t for sure. Trailhead is the starting point for any career path and I guess in the coming years we’ll see artificial intelligence absolutely stealing the show in Salesforce world and so admins should be prepared for the revolution that is taking place year after year. On making an impact in the Salesforce community You have created highly popular Salesforce browser extensions like ORGanizer. Tell us about how this came about? What does it take to build such successful products? Are you working on or planning to work on similar projects now? I said that boredom is my fuel: when I get bored I usually start a new project or a new hobby, and ORGanizer for Salesforce Chrome & Firefox extension (available at https://organizer.enree.co) is no different. It started as a personal project to ease my daily work with Salesforce projects, by adding little features that could speed up my administrative and coding tasks, while increasing my overall productivity. Then I thought, why not deliver this cool thing to my Salesforce Ohana? That’s where I believe the community took notice of me and it has remained one of the main reasons for my Salesforce MVP nomination. After the cool experience of writing a book, which is something that has been on my check list since I was a child, I have a few side projects related to Salesforce with some trailblazer friends, that I believe will have a great impact on the Ohana. And, why not, perhaps another book in 2020? Author Bio Enrico Murru is a Solution and Technical Architect at WebResults (an engineering company), an Italian platinum Salesforce partner, and an Independent Software Vendor (ISV). He has completed his MSc in Electronic Engineering at the University of Cagliari in 2007. In 2013, he launched a blog named Nerd @ Work. In 2016, he was nominated as the first Italian Salesforce MVP for his commitment to the Salesforce community. Then over the course of 3 years, Murru gained 20 Salesforce certifications, including the Salesforce Technical Architect certification. In 2016, he started one of the most popular projects, the ORGanizer for Salesforce Chrome and Firefox extension. You can follow him on Twitter @Enreeco, LinkedIn, GitHub, Trailblazer Community as well as on his personal blog page. Are you planning to embark on the journey of being a Salesforce Advanced Administrator? Confused about the various Salesforce certification programs and don’t know what to choose? Grab this book right now! The Salesforce Advanced Administrator Certification Guide will help you master data access security, monitoring and auditing, and understanding best practices for handling change management and data across organizations. What makes Salesforce Lightning Platform a powerful, fast and intuitive user interface What are the challenges of adopting AI-powered tools in Sales? How Salesforce can help Salesforce is buying Tableau in a $15.7 billion all-stock deal Salesforce’s open sourcing Centrifuge: A library for accelerating JVM restarts Build a custom Admin Home page in Salesforce CRM Lightning Experience

Key-takeaways:   Data scientists need a tailored portfolio of projects that they own and manage to have a sense of autonomy.  The top skill or personality trait a successful data scientist can possess (and should possess) is curiosity.  Managing a successful analytics team and individual analytics professionals is different than managing any other type of team.  Data and analytics will be ubiquitous in the very near future. Analytics teams are different than any other team in the organization and analytics professionals are unique variant of creative professionals. Providing challenging, interesting and valuable work in the form of a personal project portfolio of work for a data scientist can be done and needs to be done to ensure productivity, job satisfaction, value delivery, and retention.  We interviewed Analytics Leader, and bestselling author, John K Thompson on data analytics, the future of analytics and his recent book, Building Analytics Teams. The interview in detail:  1. What are the fundamental concepts of building and managing a high-performing analytics team?  It is critically important to remember that data scientists are creative and intelligent people. They cannot be managed well in a command-and-control environment.  Data scientists need a tailored portfolio of projects that they own and manage to have a sense of autonomy. If they have a portfolio of projects and can manage their time and effort, the productivity of the team will be much higher than what is typically seen in teams managed in a traditional manner.  The relationship of the analytics leader with their peers and executives of the company is critically important to the success of the analytics team.  It is very important to realize that most analytics project fail at the point of where analytical models are to be implemented in production systems. 2. Tell us about your book, Building Analytics Teams. How is your book new and/or different from other books on Data Analytics?   Building Analytics Teams is focused on the practical challenges faced by people who are building and managing high performance analytics teams and the staff members who make up those analytics teams.    The book is different from other books in that it examines the process of building and managing a team from a holistic view.  The book considers the organization framework, the required processes, the people, the projects, the problems, and pitfalls.    The content of the book guides the reader through how to navigate these challenges and provides illustrations and examples of how to be successful.  The book is a “how to” guide on how to successfully manage the analytics process in a large corporate environment.  3. What was the motivation behind writing this book?   I have not seen a book like this, and I wish I had a book like this earlier in my career.  I have built a number of analytics teams. While building and growing those teams, I noticed certain recurring patterns. I wanted to address the misconceptions and the misperceptions people hold about analytics teams.    Analytics teams are unique. The team members who are successful have a different mindset and attitude toward project work and team work. I wanted to communicate the differences inherent in a high-performance analytics team when compared to other teams.  Also, I wanted to communicate that managing a successful analytics team and individual analytics professionals is different than managing any other type of team.    I wanted to write a guide for managers and analytics professionals to help them understand how the broader organization views them and how they can interface and interact with their peers in related organizational functions to increase the probability of joint success.  4. What should be the starting point for data analytics enthusiasts aiming to begin their journey in Data Analytics? How do you think your book will help them in their journey?  It depends on where they are starting their journey.    If they are in the process of completing their undergraduate or graduate studies, I would suggest that they take classes in programming, data science or analytics.    If they are professionals, I would suggest that they take classes on Coursera, Udemy or any other on-line educational platform to see if they have a real interest in, and affinity for, analytics.  If they do have an interest, then they should start working on analytics for themselves to test out analytical techniques, apply critical thinking and try to understand what they can see or cannot see in the data.  If that works out and their interest remains, they should volunteer for projects at work that will enable them to work with data and analytics in a work setting.  If they have the education, the affinity and the skill, then apply for a data science position.  Grab some data and make a difference!  5. What are the key skills required for someone to be successful working in Data Analytics? What are the pain points/challenges one should know?  The top skill or personality trait a successful data scientist can possess (and should possess) is curiosity. Without curiosity, you will find it difficult to be successful as a data scientist.  It helps to be talented and well educated, but I have met many stellar data scientists that are neither.  Beyond those traits, it is more important to be diligent and persistent.  The most successful business analysts and data scientists I have ever worked with were all naturally and perpetually curious and had a level of diligence and persistence that was impressive.  As for pain points and challenges; data scientists need to work on improving their listening skills, their written & verbal communication and presentation skills.  All data scientists need improvement in these areas.  6. What is the future of analytics? What will we see next?  I do believe that we are entering an era where data and analytics will be increasing in importance in all human endeavors. Certainly, corporate use of data and analytics will increase in importance, hence the focus of the book.    But beyond corporations, the active and engaged use of data and analytics will increase in importance and daily use in managing multiple aspects of - people’s personal lives, academic pursuits, governmental policy, military operations, humanitarian aid, tailoring of products and services; building of roads, towns and cities, planning of traffic patterns, provisioning of local federal and state services, intergovernmental relationships and more.    There will not be an element of human endeavor that will not be touched and changed by data and analytics. Data is ubiquitous today and data and analytics will be ubiquitous in the very near future.  We will see more discussions on who owns data and who should be able to monetize data.  We will experience increasing levels of AI and analytics across all systems that we interact with, and most of it will be unnoticed and operate in the background for our benefit.  About:  John K. Thompson is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics and Artificial Intelligence team and efforts at CSL Behring. 

This is Part 2 of the interview with our two Kali Linux experts, Wolf Halton, and Bo Weaver, on using Kali Linux for pentesting. In their section, we talk about the role of pentesting in cybersecurity. Previously, the authors talked about why Kali Linux is the premier platform for testing and maintaining Windows security. They talked about the advantages and disadvantages for using Kali Linux for pentesting. There also talked about their love for the Kali platform. Wolf says, “Kali is a stable platform, based upon a major distribution with which I am very familiar.  There are over 400 security tools in the Kali repos, and it can also draw directly from the Debian Testing repos for even more tools.” Here are a few more questions, we asked them about what they think about pentesting in cybersecurity, in general. Can you tell us about the role of pentesting in cybersecurity? According to you, how has pentesting improved over the years? Bo Weaver: For one thing, pentesting has become an accepted and required practice in network security.  I do remember the day when the attitude was “It can’t happen here. so why should you break into my network?  Nobody else is going to.” Network security, in general, wasn’t even thought by most companies and spending money on network security was seen as a waste. The availability of tools has also grown in leaps and bounds.  Also, the availability of documentation on vulnerabilities and exploits has grown, and the awareness in the industry of the importance of network security has grown. Wolf Halton: The tools have gotten much more powerful and easier to use.  A pentester will still be more effective if they can craft their own exploits, but they can now craft it in an environment of shared libraries such as Metasploit, and there are stable pentesting platforms like Kali Linux Rolling (2018) that reduces the learning curve to being an effective pentester. Pentesting is rising as a profession along with many other computer-security roles.  There are compliance requirements to do penetration tests at least annually or when a network is changed appreciably. What aspects of pentesting do you feel are tricky to get past? What are the main challenges that anyone would face? Bo Weaver: Staying out of jail.  Laws can be tricky. You need to know and fully understand all laws pertaining to network intrusion for both, the State you are working in and the Federal laws.  In pen testing, you are walking right up to the line of right and wrong and hanging your toes over that line a little bit. You can hang your toes over the line but DON’T CROSS IT!  Not only will you go to jail but you will never work in the security field again unless it is in some dark corner of the NSA. [box type="shadow" align="" class="" width=""]Never work without a WRITTEN waiver that fully contains the “Rules of Engagement” and is signed by the owner or “C” level person of the company being tested.[/box] Don’t decide to test your bank’s website even if your intent is for good.  If you do find a flaw and report it, you will not get a pat on the back but will most likely be charged for hacking.  Especially banks get real upset when people poke at their networks. Yes, some companies offer Bug Bounty programs. These companies have Rules of Engagement posted on their site along with a waiver to take part in the program.  Print this and follow the rules laid out. Wolf Halton: Staying on the right side of the law.  Know the laws that govern your profession, and always know your customer.  Have a hard copy of an agreement that gives you permission to test a network.  Attacking a network without written permission is a felony and might reduce your available career paths. Author Bio Wolf Halton is an Authority on Computer and Internet Security, a best selling author on Computer Security, and the CEO of Atlanta Cloud Technology. He specializes in—business continuity, security engineering, open source consulting, marketing automation, virtualization and data center restructuring, network architecture, and Linux administration. Bo Weaver is an old school ponytailed geek. His first involvement with networks was in 1972 while in the US Navy working on a R&D project called ARPA NET. Bo has been working with and using Linux daily since the 1990's and a promoter of Open Source. (Yes, Bo runs on Linux.) He now works as the senior penetration tester and security researcher for CompliancePoint a Atlanta based security consulting company. Pentest tool in focus: Metasploit Kali Linux 2018.2 released How artificial intelligence can improve pentesting

To celebrate the launch of Python Interviews, we ran a Q&A session on Twitter with some of the key contributors to the book. Author and interviewer Mike Driscoll (@driscollis), and experienced Python contributors Steve Holden (@holdenweb), and Alex Martelli (@aleaxit) got together to respond to your questions. Here's what happened... https://twitter.com/PacktPub/status/979055321959358465 https://twitter.com/aleaxit/status/979055993874104321 https://twitter.com/holdenweb/status/979056136199593984 https://twitter.com/driscollis/status/979056963987361793 The future of Python We then asked Mike, Steve and Alex what they thought the future of Python is going to look like. https://twitter.com/aleaxit/status/979057847660003328 https://twitter.com/holdenweb/status/979059669699309569 https://twitter.com/holdenweb/status/979059813459034112 https://twitter.com/driscollis/status/979059276017815554 How to get involved with the Python community We then asked what our experts think is the best way for someone new to the Python community to get involved. With the language growing at an immense rate, more people are (hopefully) going to want to contribute to the project. https://twitter.com/aleaxit/status/979059707389231105 https://twitter.com/holdenweb/status/979060708276137985 Advice for anyone new to programming Programmings popularity as a career choice is growing. That's not just true of new graduates but people looking to retrain and take on a new challenge in their career. But what should anyone new to programming know when starting out? https://twitter.com/aleaxit/status/979063034202107905 https://twitter.com/holdenweb/status/979061878554054658 https://twitter.com/driscollis/status/979061529575346177 Switching from Python 2.7 to Python 3 There's been considerable discussion within the community on the merits of shifting from Python 2.7 to Python 3. But whatever the obvious advantages are, there will always be resistance to change when it requires an investment of time and effort. And if you don't need to switch then why would you? Here's what Mike, Steve and Alex had to say... https://twitter.com/aleaxit/status/979063346665107457 https://twitter.com/holdenweb/status/979062974450192384 https://twitter.com/driscollis/status/979062547935571969 What gives Python an advantage over other programming languages? Why is Python so popular exactly? If it's growing at such a fantastic rate, why are developers and engineers turning to it? What does it have that other languages don't? https://twitter.com/aleaxit/status/979063792276471808 https://twitter.com/holdenweb/status/979064210608001025 https://twitter.com/driscollis/status/979063896173699072 Future Python releases If Python's going to remain popular, it's going to need to adapt and evolve with the needs of the developers of the future. So what capabilities and features would our experts like to see from Python in the future? https://twitter.com/driscollis/status/979064329864695813 https://twitter.com/aleaxit/status/979064880757063680 https://twitter.com/holdenweb/status/979064474496913408 What problems does Python face as a language? https://twitter.com/driscollis/status/979065953949552640 https://twitter.com/aleaxit/status/979065864539357184 https://twitter.com/holdenweb/status/979066065706725376 Why is Python so useful for AI and Machine learning? AI is a growing area that has expanded beyond the confines of data science into just about every corner of modern software engineering. Python has been a core part of this, and in part it explains part of the rise of Python's popularity - people want to build algorithms in a way that's relatively straightforward. https://twitter.com/driscollis/status/979066778771914752 https://twitter.com/holdenweb/status/979069094862389253 https://twitter.com/holdenweb/status/979069100831006721

This year's Skill Up survey produced some interesting results when it came to the best front end frameworks. Angular remains the most established tool with 40% of web developers reporting that they used it regularly. React is actually a little further behind, with 25% using it regularly. Similarly, Vue.js is growing but used by 20% of respondents. However, opinion was a little different when we asked what front end frameworks should win the battle of the 3 big front end tools. Respondents were split on Angular and React, with both JavaScript tools winning 34% of the vote. Vue wasn't far behind, at just over 30%. With the web development world apparently split over what framework is going to define the future of the field, how are we to pick them apart? Or do we even really need to worry? Read the report in full. Sign up to our weekly newsletter and download the PDF for free. The fact that we have three great front end tools jostling for developer attention is surely a good thing, right? To help us make sense of these trends, we caught up with Angular expert Minko Gechev to find out what he makes of web development in 2018, and what front end developers should be learning. Minko Gechev is the author of Switching Angular. You can find the latest edition here on the Packt Store. Which front end framework should you learn: Angular, React, or Vue? Respondents to the Skill Up survey were evenly split between Angular, React, and Vue in the 'battle of the frameworks'. Which do you think developers should learn, and why? In all of them, there are unique and interesting ideas which are worth exploring. I truly believe that learning all the major frameworks can help developers go to the next level! This doesn’t necessary mean to be proficient in all of them. Having a high-level understanding of how the frameworks work and how to use them is completely enough and will allow you to adapt according to the projects’ requirements. This is similar to learning programming languages from different paradigms – it helps you discover how problems are being solved in different ways. For the past a couple of years, the redux pattern became the de facto standard for state management in the modern front-end development. The good thing about redux is that it’s view agnostic so you can use it with any framework – Vue, React, Angular, etc. Angular has its own redux alternative called ngrx which empowers a declarative approach with RxJS but in general, it follows the same underlying pattern. My recommendation would be to understand how to manage the state of our applications because that’s probably the most complex problem that we’re solving in our day to day development process. Once we have a solid understanding of this, we can easily switch between different frameworks depending on the problems we’re solving, what the rest of the team is using, and the project’s requirements. A very interesting characteristic of learning Angular is that if we get comfortable with the framework we’d be also familiar with TypeScript, RxJS, and techniques such as dependency injection. This may look like an initial overhead but it’s a great long-term investment which pays off really well in large projects. How important is TypeScript to front end development? How important is TypeScript to modern web development? Why? Over the past a couple of years I see a strong increase in the excitement around the language, not only in the Angular world but also in React and Vue. I’m personally using TypeScript for a few projects – a platform that we built with React and an educational application written in Angular. I see a lot of value in using TypeScript. Recently I haven’t started any project with JavaScript – for everything new I’m using TypeScript and I’m trying to migrate, as many of my existing projects as possible.There are a few reasons for this: TypeScript provides great development experience! Especially, combined with VSCode, you can instantly notice when you’ve misspelled a property, method, you’re trying to access a property of a nullable value, etc. It gives you a sense of security that your program is correct to given extent. Of course, TypeScript cannot save us from logical mistakes but if we use its type system wisely, we can get great benefits. You might be curious what benefits? Well, TypeScript can help us reduce the number of bugs in our programs. In the study “To Type or Not to Type: Quantifying Detectable Bugs in JavaScript” the authors shown that the average JavaScript program can benefit with 15% bug reduction if it uses the type system of TypeScript. The study was using TypeScript version 2.0; with the latest features of the language the number of detectable bugs is growing dramatically. Since recently webpack is leveraging TypeScript as well because it helps discover already existing issues in the codebase. Web developers and JavaScript fatigue Do you think we're past web developers experiencing 'JavaScript fatigue'? JavaScript is very dynamic and it moves very quickly. There are a lot of potential issues which could be caused by a variety of reasons. With semantic versioning and powerful type systems (such as the type system of TypeScript), we’re walking in the right direction but we definitely have a long way to go until the ecosystem matures. Web development over the next 12 months: WebAssembly and machine learning What do you think will be the most important thing for developers to learn in the next 12 months? There are a lot of exciting things happening nowadays! Web browsers are getting more and more powerful, exposing hundreds of APIs and opportunities. WebAssembly is moving very quickly and I believe that together with Rust it has a lot of potential in future. On the other hand, Google recently announced TensorFlow.js. This is a library which allows us to use machine learning (ML) in the browser. In the next years, ML is going to take a larger portion of our development process (directly or indirectly) for: Implementing features in our applications Improving development process I’m specifically interested in the second point – improving our development process by using ML. Together with Addy Osmani, Kyle Mathews, and Katie Hempenius, we’ve been working on a toolkit called Guess.js. It aims to provide predictive bundling and pre-fetching based on ML techniques in order to let us develop faster Angular/React/Vue/etc. applications. I’m really excited about what’s coming up in near future! So are we! Thanks for taking the time to speak to us, Minko!

Machine learning has shown immense promise across domains and industries over the recent years. From helping with the diagnosis of serious ailments to powering autonomous vehicles, machine learning is finding useful applications across a spectrum of industries. However, the actual process of delivering business outcomes using machine learning currently takes too long and is too expensive, forcing some businesses to look for other less burdensome alternatives. MissingLink.ai is a recently-launched platform to fix just this problem. It enables data scientists to spend less time on the grunt work by automating and streamlining the entire machine learning cycle, giving them more time to apply actionable insights gleaned from the data. Key Takeaways Processing and managing the sheer volume of data is one of the key challenges that today’s AI tools face Yosi thinks the idea of companies creating their own machine learning infrastructure doesn’t make a lot of sense. Data professionals should be focusing on more important problems within their organizations by letting the platform take care of the grunt work. MissingLink.ai is an innovative AI platform born out of the need to simplify AI development, by taking away the common, menial data processing tasks from data scientists and allowing them to focus on the bigger data-related issues; through experiment management, data management and resource management. MissingLink is a part of the Samsung NEXT product development team that aims to help businesses automate and accelerate their projects using machine learning We had the privilege of interviewing Mr. Yosi Taguri, the founder and CEO of MissingLink, to know more about the platform and how it enables more effective deep learning. What are the biggest challenges that companies come across when trying to implement a robust Machine Learning/Deep Learning pipeline in their organization? How does it affect their business? The biggest challenge, simply put, is that today’s AI tools can’t keep up with the amount of data being produced. And it’s only going to get more challenging from here! As datasets continue to grow, they will require more and more compute power, which means we risk falling farther behind unless we change the tools we’re using. While everyone is talking about the promise of machine learning, the truth is that today, assessing data is still too time-consuming and too expensive. Engineers are spending all their time managing the sheer volume of data, rather than actually learning from it and being empowered to make changes. Let’s talk about MissingLink.ai, the platform you and your team have launched for accelerating deep learning across businesses. Why the name MissingLink? What was the motivation to launch this platform? The name is actually a funny story, and it ties pretty neatly into why we created the platform. When we were starting out three years ago, deep learning was still a relatively new concept and my team and I were working hard to master the intricacies of it. As engineers, we primarily worked with code, so to be able to solve problems with data was a fascinating new challenge for us. We quickly realized that deep learning is really hard and moves very, very slow. So we set out to solve that problem of how to build really smart machines really fast. By comparison, we thought of it through the lens of software development. Our goal was to accelerate from a glacial pace to building machine learning algorithms faster -- because we felt that there was something missing, a missing link if you will. MissingLink is a part of the growing Samsung NEXT product development team. How does it feel? What role do you think MissingLink will play in Samsung NEXT’s plans and vision going forward? Samsung NEXT’s broader mission is to help startups reach their full potential and achieve their goals. More specifically, Samsung NEXT discovers and backs the engineers, innovators, builders, and entrepreneurs who will help Samsung define the future of software and services. The Samsung NEXT product development team is focused on building software and services that take advantage of and accelerate opportunities related to some of the biggest shifts in technology including automation, supply and demand, and interfaces. This will require hardware and software to seamlessly come together. Over the past few years, nearly all startups are leveraging AI for some component of their business, yet practical progress has been slower than promised. MissingLink is a foundational tool to enable the progress of these big changes, helping entrepreneurs with great use cases for machine learning to accelerate their projects. Could you give us the key features of Missinglink.ai that make it stand out from the other AI platforms available out there? How will it help data scientists and ML engineers build robust, efficient machine learning models? First off, MissingLink.ai is the most comprehensive AI platform out there. It handles the entire deep learning lifecycle and all its elements, including code, data, experiments, and resources. I’d say that our top features include: Experiment Management: See and compare the entire history of experiments. MissingLink.ai auto-documents every aspect Data Management: A unique data store tracks data versions used in every experiment, streams data, caches it locally and only syncs changes Resources Management: Manages your resources with no extra infrastructure costs using your AWS or other cloud credentials. It grows and shrinks your cloud resources as needed. These features, together with our intuitive interface, really put data scientists and engineers in the driver's seat when creating AI models. Now they can have more control and spend less energy repeating experiments, giving them more time to focus on what is important. Your press release on the release of MissingLink states “the actual process of delivering business outcomes currently takes too long and it is too expensive. MissingLink.ai was born out of a desire to fix that.” Could you please elaborate on how MissingLink makes deep learning less expensive and more accessible? Companies are currently spending too much time and devoting too many resources to the menial tasks that are necessary for building machine learning models. The more time data scientists spend on tasks like spinning machines, copying files and DevOps, the more money that a company is wasting. MissingLink changes that through the introduction of something we’re calling DeepOps or deep learning operations, which allows data scientists to focus on data science and let the machine take care of the rest. It’s like DevOps where the role is about how to make the process of software development more efficient and productionalized, but the difference is no one has been filling this role and it’s different enough that its very specific to the task of deep learning. Today, instead of data scientists working on their models and advancing AI, they are spending their time doing this DeepOps work. MissingLink reduces load time and facilitates easy data exploration by eliminating the need to copy files through data-management in a version-aware data store. Most of the businesses are moving their operations on to the cloud these days, with AWS, Azure, GCP, etc. being their preferred cloud solutions. These platforms have sophisticated AI offerings of their own. Do you see AI platforms such as MissingLink.ai as a competition to these vendors, or can the two work collaboratively? I wouldn’t call cloud companies our competitors; we don’t provide the cloud services they do, and they don’t provide the DeepOps service that we do. Yes, we all are trying to simplify AI, but we’re going about it in very different ways. We can actually use a customer’s public cloud provider as the infrastructure to run the MissingLink.ai platform. If customers provide us with their cloud credentials, we can even manage this for them directly. Concepts such as Reinforcement Learning and Deep Learning for Mobile are getting a lot of traction these days, and have moved out of the research phase into the application/implementation phase. Soon, they might start finding extensive business applications as well. Are there plans to incorporate these tools and techniques in the platform in the near future? We support all forms of Deep Learning, including Reinforcement Learning. On the Deep Learning for Mobile side, we think the Edge is going to be a big thing as more and more developers around the world are exposed to Deep Learning. We plan to support it early next year. Currently, data privacy and AI ethics have become a focal point of every company’s AI strategy. We see tech conglomerates increasingly coming under the scanner for ignoring these key aspects in their products and services. This is giving rise to an alternate movement in AI, with privacy and ethics-centric projects like Deon, Vivaldi, and Tim Berners-Lee’s Solid. How does MissingLink approach the topics of privacy, user consent, and AI ethics? Are there processes/tools or principles in place in the MissingLink ecosystem or development teams that balance these concerns? When we started MissingLink we understood that data is the most sensitive part of Deep Learning. It is the new IP. Companies spend 80% of their time attending to data, refining it, tagging it and storing it, and therefore are reluctant to upload it to a 3rd party vendor. We have built MissingLink with that in mind - our solution allows customers to simply point us in the direction of where their data is stored internally, and without moving it or having to access it as a SaaS solution we are able to help them manage it by enabling version management as they do with code. Then we can stream it directly to the machines that need the data for processing and document which data was used for reproducibility. Finally, where do you see machine learning and deep learning heading in the near future? Do you foresee a change in the way data professionals work today? How will platforms like MissingLink.ai change the current trend of working? Right now, companies are creating their own machine learning infrastructure - and that doesn’t make sense. Data professionals can and should be focusing on more important problems within their organizations. Platforms like MissingLink.ai free data scientists from the grunt work it takes to upkeep the infrastructure, so they can focus on bigger picture issues. This is the work that is not only more rewarding for engineers to work on, but also creates the unique value that companies need to compete.  We’re excited to help empower more data professionals to focus on the work that actually matters. It was wonderful talking to you, and this was a very insightful discussion. Thanks a lot for your time, and all the best with MissingLink.ai! Read more Michelangelo PyML: Introducing Uber’s platform for rapid machine learning development Tesseract version 4.0 releases with new LSTM based engine, and an updated build system Baidu releases a new AI translation system, STACL, that can do simultaneous interpretation

Python is currently one of the most popular and desired programming languages. Primarily for its simplicity, agility and ability to be used across a variety of software development projects. Although an Object Oriented language, Python supports an array of programming paradigms, including Functional Programming. Functional Programming is a paradigm that treats computation as the evaluation of math functions. It’s quite advantageous as it allows for efficient parallel programming and error-free code. We recently interviewed Steven Lott, a true Python professional and best-selling author of a number of Python books. Steven talks a bit about modern Python and how the language adapts well to the Functional paradigm, offering developers a range of solutions to build modern, cloud based applications. He also talks about his most recent book, the second edition of his best seller, Functional Python Programming, and how it will benefit developers looking to enter the world of functional programming with Python. About Steven Steven F. Lott has been programming since the 70s, when computers were large, expensive, and rare. As a contract software developer and architect, he has worked on hundreds of projects, from very small to very large. He's been using Python to solve business problems for over 10 years. Steven is a technomad who lives in various places on the east coast of the U.S. Follow his technology blog to stay updated on the latest trends in tech. You may also connect with him on LinkedIn. Key Takeaways Why learn Python? One of the major reasons developers appreciate Python, is because it’s simple, and has the ability to create succinct and expressive programs. For example, data scientists prefer Python because they can build sophisticated analytical tools using simple functions and produce useful results. Why Functional programming? The functional programming paradigm forms the perfect foundation for developers and architects to build and design modern architectures like Serverless. But Python isn’t inherently functional. Although an object oriented language at heart, Python can create higher order functions and other functional features. Python 3 has made this easier. Steven’s Python 4 wish list: In future versions of Python, Steven hopes to see a wider use of Unicode operator characters like × in addition, * for multiplication, and ÷ in addition to / for division. Also, he expects PyPy and RPython projects to be more widely used; future Pythons versions will benefit from optimizations and restructuring the interpreter. One of the most helpful features of Python 3 are type hints, which allow one to write clear and implicitly documented code while preventing the invoking of methods with wrong data types. Steven’s latest edition of Functional programming with Python, explores type hints in depth along with core language features such as lambdas, generator expressions, functions, and callable objects. Full Interview Python is one of the top programming languages. List down top 3 features of Python that make programmers love it.. It seems like programmers love Python primarily because it allows them to create succinct, expressive programs. Before long, they learn the vast library of code - is another reason for Python's immense popularity. Many people adopt Python because of the low barrier to entry: it really is as simple as download and start working. You've been working with Python for over a decade. How has your experience been with Python as a primary development language? Over my 40-year career, I've used a variety of languages. And I've found Python to be extremely productive. A team can build and deploy microservices-based applications at a tremendous pace. Data scientists can build sophisticated analytical tools using simple functions to produce useful results without the overheads of complex compile and build environments. Back when Python 3 just came into existence, we saw certain resistance to the notion of Python being apt for functional programming. How would you say Python has progressed since then? At its core, Python is an object-oriented language. Consequently some functional features aren't central. One of the essential functional design tools -- creating higher-order functions -- has always been part of Python. The wider use of generator functions in Python 3 has made functional Python programming much more common. Many Python applications are hybrids, mixing object-oriented and functional features of the language. Now that type hints are available, it becomes practical to use mypy to confirm that the code is very likely to work properly. For a developer who's picking up the Functional Programming paradigm for the first time, what do you think are the prerequisites? Functional programming is closely aligned to the core mathematical ideas of functions and functional composition. As a consequence, a minimal background in programming could be advantageous to help leverage essential function definitions and avoid needless state change. For programmers already heavily invested in procedural programming, it may be helpful to set the idea of stateful objects aside. In the above context, how does your book, Functional Python Programming, Second Edition, prepare its readers to be industry ready? What are the key takeaways for readers from your title and how does it help with the learning curve? The examples in the book are related to exploratory data analysis, an important skill in the broader area of data science. They also focus on the standard library, allowing someone to apply the functional design approach to other libraries and tools. I think a focus on the core language features (e.g., lambdas, generator expressions, functions, callable objects) provide a foundation that allows a programmer to apply the core ideas more widely to different kinds of problems and other software packages. What new and updated content is available in this edition, for developers who've purchased your previous book? Almost all of the examples have been rewritten to include type hints. This can be an important quality check helping to ensure the Python code works. When used with doctest examples, it becomes relatively easy to provide reliable, correct code. In a few cases, external packages (i.e., the pymonad library) don't have type hints and the examples reflect this gap. Can you throw some light on Functional Reactive Programming and how FRP with Python is boosting the implementation of modern architectures like Cloud Native and Serverless? The central idea of serverless programming -- a collection of isolated functions -- fits the functional programming paradigm very elegantly. The processing is generally stateless, with stand-alone functions waiting for their inputs. Ideas like "choreography" of web services work with this idea of stateless functions that respond to an input by producing an output. This leads to careful separation of persistence and state change from the other transformational processing. This helps create software with easy-to-understand behavior and implementation code that's very expressive of the algorithm. As Python inches towards a 4.0, what do you think should/can be changed/rectified in the language, for the better? At some point, I expect the PyPy and RPython projects to create some optimizations leading to a fundamental restructuring of the interpreter. Perhaps these changes could remove the need for the GIL (Global Interpreter Lock) by exposing a minimal kernel of code that requires exclusive access to internal data structures. Of more general interest, I'd hope to see wider use of Unicode operator characters like × in addition to * for multiplication, and ÷ in addition to / for division. Perhaps ∻ could be adopted for truncated division. This could also lead to use of ℝ instead of float and ℤ instead of int providing a more mathematical look to type hints. It would be nice to expand the use of the Unicode character set to create more readable programs. List down 3 reasons for developers to choose your book as the book of choice for Functional programming in Python. Programmers who want to create succinct and expressive code often find functional design to help them fulfill this. The Functional Python Programming book provides extensive examples that show multiple ways to achieve this goal. In many cases, generator functions and lazy processing can be a large performance improvement. A generator function can use less memory than a large data collection, and this change can be helpful. This book will provide number of examples of lazy processing to avoid creating large, in-memory collections. It can be difficult to get started with type hints. The use case in this book show type hints in somewhat more complex real-world situations. If you enjoyed this interview, head over to check out Steven’s latest edition of Functional Python Programming, He is leveraging Python to implement microservices and ETL pipelines. His other titles with Packt Publishing include Python Essentials, Mastering Object-Oriented Python, Functional Python Programming, and Python for Secret Agents. What is the difference between functional and object oriented programming? Building functional programs with F# Seven wrongs don’t make the one right: Solving a problem with Functional Javascript

IntroductionIt is a well-established fact that your machine-learning model is only as good as the data it is fed. ML model trained on bad-quality data usually has a number of issues. Here are a few ways that bad data might affect machine-learning models -1. Predictions that are wrong may be made as a result of errors, missing numbers, or other irregularities in low-quality data. The model's predictions are likely to be inaccurate if the data used to train is unreliable.2. Bad data can also bias the model. The ML model can learn and reinforce these biases if the data is not representative of the real-world situations, which can result in predictions that are discriminating.3. Poor data also disables the the ability of ML model to generalize on fresh data. Poor data may not effectively depict the underlying patterns and relationships in the data.4. Models trained on bad-quality data might need more retraining and maintenance. The overall cost and complexity of model deployment could rise as a result.As a result, it is critical to devote time and effort to data preprocessing and cleaning in order to decrease the impact of bad data on ML models. Furthermore, to ensure the model's dependability and performance, it is often necessary to use domain knowledge to recognize and address data quality issues.It might come as a surprise, but gold-standard datasets like ImageNet, CIFAR, MNIST, 20News, and more also contain labeling issues. I have put in some examples below for reference -The above snippet is from the Amazon sentiment review dataset , where the original label was Neutral in both cases, whereas Cleanlab and Mechanical turk said it to be positive (which is correct).The above snippet is from the MNIST dataset, where the original label was marked to be 8 and 0 respectively, which is incorrect. Instead, both Cleanlab and Mechanical Turk said it to be 9 and 6 (which is correct).Feel free to check out labelerrors to explore more such cases in similar datasets.Introducing CleanlabThis is where Cleanlab can come in handy as your best bet. It helps by automatically identifying problems in your ML dataset, it assists you in cleaning both data and labels. This data centric AI software uses your existing models to estimate dataset problems that can be fixed to train even better models. The graphic below depicts the typical data-centric AI model development cycle:Apart from the standard way of coding all the way through finding data issues, it also offers Cleanlab Studio - a no-code platform for fixing all your data errors. For the purpose of this blog, we will go the former way on our sample use case.Getting Hands-on with CleanlabInstallationInstalling cleanlab is as easy as doing a pip install. I recommend installing optional dependencies as well, you never know what you need and when. I also installed sentence transformers, as I would be using them for vectorizing the text. Sentence transformers come with a bag of many amazing models, we particularly use ‘all-mpnet-base-v2’ as our choice of sentence-transformers for vectorizing text sequences. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search. Feel free to check out this for the list of all models and their comparisons.pip install ‘cleanlab[all]’ pip install sentence-transformersDatasetWe picked the SMS Spam Detection dataset as our choice of dataset for doing the experimentation. It is a public set of labeled SMS messages that have been collected for mobile phone spam research with total instances of roughly ~5.5k. The below graphic gives a sneak peek of some of the samples from the dataset.Data PreviewCodeLet’s now delve into the code. For demonstration purposes, we inject a 5% noise in the dataset, and see if we are able to detect them and eventually train a better model.Note: I have also annotated every segment of the code wherever necessary for better understanding.import pandas as pd from sklearn.model_selection import train_test_split, cross_val_predict       from sklearn.preprocessing import LabelEncoder               from sklearn.linear_model import LogisticRegression       from sentence_transformers import SentenceTransformer       from cleanlab.classification import CleanLearning       from sklearn.metrics import f1_score # Reading and renaming data. Here we set sep=’\t’ because the data is tab       separated.       data = pd.read_csv('SMSSpamCollection', sep='\t')       data.rename({0:'label', 1:'text'}, inplace=True, axis=1)       # Dropping any instance of duplicates that could exist       data.drop_duplicates(subset=['text'], keep=False, inplace=True)       # Original data distribution for spam and not spam (ham) categories       print (data['label'].value_counts(normalize=True))       ham 0.865937       spam 0.134063       # Adding noise. Switching 5% of ham data to ‘spam’ label       tmp_df = data[data['label']=='ham']               examples_to_change = int(tmp_df.shape[0]*0.05)       print (f'Changing examples: {examples_to_change}')       examples_text_to_change = tmp_df.head(examples_to_change)['text'].tolist() changed_df = pd.DataFrame([[i, 'spam'] for i in examples_text_to_change])       changed_df.rename({0:'text', 1:'label'}, axis=1, inplace=True)       left_data = data[~data['text'].isin(examples_text_to_change)]       final_df = pd.concat([left_data, changed_df])               final_df.reset_index(drop=True, inplace=True)       Changing examples: 216       # Modified data distribution for spam and not spam (ham) categories       print (final_df['label'].value_counts(normalize=True))       ham 0.840016       spam 0.159984    raw_texts, raw_labels = final_df["text"].values, final_df["label"].values # Converting label into integers encoder = LabelEncoder() encoder.fit(raw_train_labels)       train_labels = encoder.transform(raw_train_labels)       test_labels = encoder.transform(raw_test_labels)       # Vectorizing text sequence using sentence-transformers transformer = SentenceTransformer('all-mpnet-base-v2') train_texts = transformer.encode(raw_train_texts)       test_texts = transformer.encode(raw_test_texts)       # Instatiating model instance model = LogisticRegression(max_iter=200) # Wrapping the sckit model around CL cl = CleanLearning(model) # Finding label issues in the train set label_issues = cl.find_label_issues(X=train_texts, labels=train_labels) # Picking top 50 samples based on confidence scores identified_issues = label_issues[label_issues["is_label_issue"] == True] lowest_quality_labels =       label_issues["label_quality"].argsort()[:50].to_numpy()       # Beauty print the label issue detected by CleanLab def print_as_df(index):    return pd.DataFrame(              {    "text": raw_train_texts,              "given_label": raw_train_labels,           "predicted_label": encoder.inverse_transform(label_issues["predicted_label"]),       },       ).iloc[index]       print_as_df(lowest_quality_labels[:5]) As we can see, Cleanlab assisted us in automatically removing the incorrect labels and training a better model with the same parameters and settings. In my experience, people frequently ignore data concerns in favor of building more sophisticated models to increase accuracy numbers. Improving data, on the other hand, is a pretty simple performance win. And, thanks to products like Cleanlab, it's become really simple and convenient.Feel free to access and play around with the above code in the Colab notebook hereConclusionIn conclusion, Cleanlab offers a straightforward solution to enhance data quality by addressing label inconsistencies, a crucial step in building more reliable and accurate machine learning models. By focusing on data integrity, Cleanlab simplifies the path to better performance and underscores the significance of clean data in the ever-evolving landscape of AI. Elevate your model's accuracy by investing in data quality, and explore the provided code to see the impact for yourself.Author BioPrakhar has a Master’s in Data Science with over 4 years of experience in industry across various sectors like Retail, Healthcare, Consumer Analytics, etc. His research interests include Natural Language Understanding and generation, and has published multiple research papers in reputed international publications in the relevant domain. Feel free to reach out to him on LinkedIn

With numerous high profile security breaches in recent years, cybersecurity feels like a particularly urgent issue. But while the media - and, indeed, the wider world - loves stories of modern vulnerabilities and mischievous hackers, there's often very little attention paid to what causes insecurity and what can practically be done to solve such problems. To get a better understanding of cybersecurity in 2019, we spoke to Adrian Pruteanu, consultant and self-identifying hacker. He told us about what he actually does as a security consultant, what it's like working with in-house engineering teams, and how red team/blue team projects work in practice. Adrian is the author of Becoming the Hacker, a book that details everything you need to know to properly test your software using the latest pentesting techniques.          What does it really mean to be a hacker? In this podcast episode, we covered a diverse range of topics, all of which help to uncover the reality of working as a pentester. What it means to be a hacker - and how it's misrepresented in the media The biggest cybersecurity challenges in 2019 How a cybersecurity consultant actually works The most important skills needed to work in cybersecurity The difficulties people pose when it comes to security Listen here: https://soundcloud.com/packt-podcasts/a-hacker-is-somebody-driven-by-curiosity-adrian-pruteanu-on-cybersecurity-and-pentesting-tactics

Introduction In the rapidly evolving field of Natural Language Processing (NLP), staying ahead of technological advancements while mastering foundational principles is crucial for professionals aiming to drive innovation. "Mastering NLP from Foundations to LLMs" by Packt Publishing serves as a comprehensive guide for those seeking to deepen their expertise. Authored by leading figures in Machine Learning and NLP, this text bridges the gap between theoretical knowledge and practical applications. From understanding the mathematical underpinnings to implementing sophisticated NLP models, this book equips readers with the skills necessary to solve today’s complex challenges. With insights into Large Language Models (LLMs) and emerging trends, it is an essential resource for both aspiring and seasoned NLP practitioners, providing the tools needed to excel in the data-driven world of AI. In-Depth Analysis of Technology NLP is at the forefront of technological innovation, transforming how machines interpret, generate, and interact with human language. Its significance spans multiple industries, including healthcare, finance, and customer service. At the core of NLP lies a robust integration of foundational techniques such as linear algebra, statistics, and Machine Learning. Linear algebra is fundamental in converting textual data into numerical representations, such as word embeddings. Statistics play a key role in understanding data distributions and applying probabilistic models to infer meaning from text. Machine Learning algorithms, like decision trees, support vector machines, and neural networks, are utilized to recognize patterns and make predictions from text data. "Mastering NLP from Foundations to LLMs" delves into these principles, providing extensive coverage on how they underpin complex NLP tasks. For example, text classification leverages Machine Learning to categorize documents, enhancing functionalities like spam detection and content organization. Sentiment analysis uses statistical models to gauge user opinions, helping businesses understand consumer feedback. Chatbots combine these techniques to generate human-like responses, improving user interaction. By meticulously elucidating these technologies, the book highlights their practical applications, demonstrating how foundational knowledge translates to solving real-world problems. This seamless integration of theory and practice makes it an indispensable resource for modern tech professionals seeking to master NLP. Adjacent Topics The realm of NLP is witnessing groundbreaking advancements, particularly in LLMs and hybrid learning paradigms that integrate multimodal data for richer contextual understanding. These innovations are setting new benchmarks in text understanding and generation, driving enhanced applications in areas like automated customer service and real-time translation. "Mastering NLP from Foundations to LLMs" emphasizes best practices in text preprocessing, such as data cleaning, normalization, and tokenization, which are crucial for improving model performance. Ensuring robustness and fairness in NLP models involves techniques like resampling, weighted loss functions, and bias mitigation strategies to address inherent data disparities. The book also looks ahead at future directions in NLP, as predicted by industry experts. These include the rise of AI-driven organizational structures where decentralized AI work is balanced with centralized data governance. Additionally, there is a growing shift towards smaller, more efficient models that maintain high performance with reduced computational resources. "Mastering NLP from Foundations to LLMs" encapsulates these insights, offering a forward-looking perspective on NLP and providing readers with a roadmap to stay ahead in this rapidly advancing field. Problem-Solving with Technology "Mastering NLP from Foundations to LLMs" addresses several critical issues in NLP through innovative methodologies. The book first presents common workflows with LLMs such as prompting via APIs and building a Langchain pipeline. From there, the book takes on heavier challenges. One significant challenge is managing multiple models and optimizing their performance for specific tasks. The book introduces the concept of using multiple LLMs in parallel, with each model specialized for a particular function, such as a medical domain or backend development in Python. This approach reduces overall model size and increases efficiency by leveraging specialized models rather than a single, monolithic one. Another issue is optimizing resource allocation. The book discusses strategies like prompt compression for cost reduction, which involves compacting input prompts to minimize token count without sacrificing performance. This technique addresses the high costs associated with large-scale model deployments, offering businesses a cost-effective way to implement NLP solutions. Additionally, the book explores fault-tolerant multi-agent systems using frameworks like Microsoft’s AutoGen. By assigning specific roles to different LLMs, these systems can work together to accomplish complex tasks, such as professional-level code generation and error checking. This method enhances the reliability and robustness of AI-assisted solutions. Through these problem-solving capabilities, "Mastering NLP from Foundations to LLMs" provides practical solutions that make advanced technologies more accessible and efficient for real-world applications. Unique Insights and Experiences Chapter 11 of "Mastering NLP from Foundations to LLMs" offers a wealth of expert insights that illuminate the future of NLP. Contributions from industry leaders like Xavier Amatriain (VP, Google) and Nitzan Mekel-Bobrov (CAIO, Ebay) explore hybrid learning paradigms and AI integration into organizational structures, shedding light on emerging trends and practical applications. The authors, Lior Gazit and Meysam Ghaffari, share their personal experiences of implementing NLP technologies in diverse sectors, ranging from finance to healthcare. Their journey underscores the importance of a solid foundation in mathematical and statistical principles, combined with innovative problem-solving approaches. This book empowers readers to tackle advanced NLP challenges by providing comprehensive techniques and actionable advice. From addressing class imbalances to enhancing model robustness and fairness, the authors equip practitioners with the skills needed to develop robust NLP solutions, ensuring that readers are well-prepared to push the boundaries of what’s possible in the field. Conclusion "Mastering NLP from Foundations to LLMs" is an 11-course meal that offers a comprehensive journey through the intricate landscape of NLP. It serves as both a foundational text and an advanced guide, making it invaluable for beginners seeking to establish a solid grounding and experienced practitioners aiming to deepen their expertise. Covering everything from basic mathematical principles to advanced NLP applications like LLMs, the book stands out as an essential resource. Throughout its chapters, readers gain insights into practical problem-solving strategies, best practices in text preprocessing, and emerging trends predicted by industry experts. "Mastering NLP from Foundations to LLMs" equips readers with the skills needed to tackle advanced NLP challenges, making it a comprehensive, indispensable guide for anyone looking to master the evolving field of NLP. For detailed guidance and expert advice, dive into this book and unlock the full potential of NLP techniques and applications in your projects. Author BioLior Gazit is a highly skilled Machine Learning professional with a proven track record of success in building and leading teams drive business growth. He is an expert in Natural Language Processing and has successfully developed innovative Machine Learning pipelines and products. He holds a Master degree and has published in peer-reviewed journals and conferences. As a Senior Director of the Machine Learning group in the Financial sector, and a Principal Machine Learning Advisor at an emerging startup, Lior is a respected leader in the industry, with a wealth of knowledge and experience to share. With much passion and inspiration, Lior is dedicated to using Machine Learning to drive positive change and growth in his organizations.Meysam Ghaffari is a Senior Data Scientist with a strong background in Natural Language Processing and Deep Learning. Currently working at MSKCC, where he specialize in developing and improving Machine Learning and NLP models for healthcare problems. He has over 9 years of experience in Machine Learning and over 4 years of experience in NLP and Deep Learning. He received his Ph.D. in Computer Science from Florida State University, His MS in Computer Science - Artificial Intelligence from Isfahan University of Technology and his B.S. in Computer Science at Iran University of Science and Technology. He also worked as a post doctoral research associate at University of Wisconsin-Madison before joining MSKCC.

Quickbooks, the accounting software package developed and marketed by Intuit is targeted towards small and medium-sized businesses. It offers on-premises accounting applications and cloud-based versions that can undertake remote access capabilities like remote payroll assistance and outsourcing, electronic payment functions, online banking, and reconciliation, mapping features, and more. To know more about Quickbooks’ latest features and its learning curve for beginners, we did a quick interview with Crystalynn Shelton, a certified QuickBooks ProAdvisor and author of the book ‘Mastering QuickBooks 2020’. With more than 10 years of experience in Quickbooks, Shelton says Quickbooks is not only user-friendly but also cost-effective. Further, when asked about her views on QuickBooks online, Shelton points out that its live unlimited technical support is one of its main features  On Quickbooks, its benefits and use cases What are some of the advantages of Quickbooks that sets it apart from its competitors?  QuickBooks has a number of advantages that set them apart from its competitors. First, it is affordable for most small businesses. Whether you purchase an Online subscription (starting at $20/month) or a desktop product (starting at a one-time fee of $199), there is something for every budget. Another benefit of using QuickBooks is the program is very user-friendly. Most small business owners purchase the software and are able to set it up without having an IT person on staff.  In addition, there are a number of training videos, an extensive help menu within the program not to mention live tech support if you need it. Because QuickBooks is the most widely used accounting software program used by small businesses, most accountants and CPAs are familiar with the program. Some of these folks are certified ProAdvisors (like myself). They can offer consulting, training, and even bookkeeping services to small business owners who use QuickBooks. Can you elaborate on how small businesses can take benefit from Quickbooks? Also, how does Quickbooks simplifies tasks for them?  While there are numerous reasons why small businesses decide to use QuickBooks, there are five that tend to be the most common reasons: small businesses who can’t afford to hire a bookkeeper, small businesses who have outgrown the use of Excel spreadsheets and need a more sophisticated way to track income and expenses, small businesses who need financial statements in order to apply for a line of credit or business loan, small businesses whose tax professional will no longer accept a shoebox of receipts to file taxes. QuickBooks simplifies bookkeeping by allowing you to track all aspects of the business in one place: accounts payable, accounts receivable, income, and expenses. It uses simple language such as “people who owe you” (aka accounts receivable) or “what you owe to others” (aka accounts payable) to help business owners without prior bookkeeping knowledge comprehend the program. QuickBooks allows you to accept credit card payments from customers so you can get paid faster and easily reconcile payments to open invoices. Not to mention you can reduce (if not eliminate) manual data entry by connecting all of your business bank and credit card accounts to QBO.  Can you elaborate on how your book ‘Mastering QuickBooks 2020’ will prepare bookkeepers and accounting students in learning the ropes of QuickBooks? Also, how does the learning curve look like for users who have no bookkeeping knowledge and no experience with QuickBooks? This book was written with the assumption that the reader has no experience or knowledge of bookkeeping. We use simple language to explain how QuickBooks works and we have also provided screenshots to support the concepts being taught.  Chapter 1 includes a section that covers bookkeeping basics which will help non-accountants gain a better understanding of the terminology used in the field of accounting as well as QuickBooks. This information will help aspiring accountants build on their existing bookkeeping knowledge.  In addition, we have included the behind the scenes debits and credits for certain transactions to help accounting students prepare for the CPA exam or other academic tests. Shelton’s views on QuickBooks Online and Desktop What are your thoughts on QuickBooks Online and Quickbooks Desktop? What are the benefits of cloud accounting over Desktop? Do factors such as the size of an organization, or its maturity matter in choosing between the online and the desktop version? There are several benefits of using cloud accounting software over the desktop. Cloud accounting software allows you to manage your business from any device with an internet connection; whereas desktop limits you to a desktop computer. With cloud accounting software like QuickBooks Online, you can give anyone access to your QuickBooks data without them having to travel to your office. Cloud accounting software includes automatic real-time updates of your data. Unlike desktop software, you don’t have to worry about backing up your data with Online; its automatically done for you. Finally, QuickBooks Online includes unlimited live technical support. This is an invaluable feature for small business owners who are managing their own books and need the ability to get help when they need it. The size of an organization, structure, and length of time in business can definitely impact whether a business should choose QuickBooks Online or desktop. As a QuickBooks ProAdvisor, one of the first things I do is conduct an assessment to determine what the needs of my clients are. This involves documenting the details of their current processes (i.e. invoicing customers, paying bills, managing inventory, etc.)  Once I have this information, I am able to determine whether QuickBooks desktop is right or if QuickBooks Online is the best fit. If both products are ideal, I provide my clients with the downsides (if any) of going with one product over the other. This gives my clients all of the information they need to make an informed decision. On how Quickbook secures online data How does Quickbooks help in securing payments? How does QuickBooks keep online data safe? To secure payments, intuit transmits, support, protect, and access all cardholder information in compliance with the Payment Card Industry’s (PCI) data security standards. Additional security precautions Intuit has implemented are as follows: All data between Intuit servers and their customers is encrypted with at least 128-bit TLS, and all copies of daily backup data are encrypted with 256-bit AES encryption. Data is kept secure with multiple servers housed in Tier-3 data centers that have strict access controls and real-time video monitoring of the data center. All servers are hardened Linux installations, which are monitored in real-time and kept up-to-date with security patches. Can you suggest some best practices (at least five) that will help Quickbook aspirants in saving time and becoming a Quickbook pro? There are several ways you can save time and become proficient in QuickBooks Online. First, I recommend that you use QuickBooks on a daily basis. The more hands-on experience with QuickBooks, the more proficient you will become. Second, take the time to properly set up your QuickBooks account before you start entering transactions.  In Chapter 2, we provide you with a detailed checklist which includes what information you need to setup QuickBooks. By taking the time to set up customers, vendors, the chart of accounts, and your products and services upfront, the less time you will spend having to do it later on when you are trying to enter data. Third, all aspiring bookkeepers and accountants should get certified in QuickBooks Online. Certification is offered through Intuit and it is free.  As a Certified QuickBooks ProAdvisor, you get access to product discounts, marketing materials to promote bookkeeping services to prospective clients, a certification badge and designation you can put on business cards, websites and email signature lines.  Fourth, utilize keyboard shortcuts. They will save you time as you navigate the program. We have included a list of QBO keyboard shortcuts in the appendix of this book. Finally, connect as many bank and credit card accounts as you can to QBO. By doing so, you will reduce the amount of manual data entry required which will help you to keep your books up-to-date. If you want to learn how to build the perfect budget, simplify tax return preparation, manage inventory, track job costs, generate income statements and financial reports, check out Crystalynn’s book ‘Mastering QuickBooks 2020’. This book will work for a small business owner, bookkeeper, or accounting student who wants to learn how to make the most of QuickBooks Online. About the author Crystalynn Shelton is a licensed Certified Public Accountant, a certified QuickBooks ProAdvisor and has been certified in QuickBooks for more than 10 years. Crystalynn is currently a staff writer for Fit Small Business and an Adjunct Instructor at UCLA Extension where she teaches accounting, bookkeeping and QuickBooks to hundreds of small business owners and accounting students each year. Her previous experience includes working at Intuit (QuickBooks) as a Sr. Learning Specialist.  MongoDB’s CTO Eliot Horowitz on what’s new in MongoDB 4.2, Ops Manager, Atlas, and more New QGIS 3D capabilities and future plans presented by Martin Dobias, a core QGIS developer Greg Walters on PyTorch and real-world implementations and future potential of GANs Elastic marks its entry in security analytics market with Elastic SIEM and Endgame acquisition “The challenge in Deep Learning is to sustain the current pace of innovation”, explains Ivan Vasilev, machine learning engineer

Vue occupies an interesting position in the triumvirate of frontend JavaScript frameworks. Not hyped to the extent that React is, and not as established as Angular, it’s spent the last couple of years quietly minding its business and building an engaged and enthusiastic community of developers. One of these developers is Marina Mosti: her book Building Forms with Vue.js is just the latest step in her career journey from a backend developer getting frustrated with Java to Vue.js evangelist and educator. She’s a great person to explain the attraction of Vue.js, and to provide an insight into how she first entered the community - luckily, I was able to chat with her. Buy Building Forms with Vue.js on the Packt store. Her background is interesting: “I actually started out as a PHP developer and found myself in a position where I forced myself into learning front end. It’s not until very recently that I started doing front end in terms of it being my main focus” she says. Rather than moving deeper down into the stack, she has gone the other way, gravitating towards the front end. That might not be completely conventional, but it’s also indicative of the evolution of both JavaScript and frontend development in general. Read the first chapter of the book for free on the Packt platform. Rethinking JavaScript Things today, Marina suggests, are quite different. She’s quick to tell me, for example, that “the current state of JavaScript is just so much better than what it used to be,” and recalls a general antipathy towards JavaScript in the early years of her career that she “dragged… around for many years.” “I went to this very small school where they taught us the basics of front end development” she explains. “And for good or for bad the teachers were always very adamant about saying ‘oh don’t bother learning vanilla JavaScript because we have jQuery and vanilla JavaScript is so bad that you’re not gonna use it.’” However, the framework boom changed this. “I got to a point where all these cool frameworks were coming out and you just realise ‘hey I don’t know javascript; I know jquery so how do I make this jump?’” This ‘jump’ wasn’t without challenges. “Just having to go back and learn the basics - that has been the most ‘challenging’ part of going onto front end - because the front end that I knew [at the time] was php side-generated HTML code with maybe a little bit of javascript, maybe some CSS.” Read next: Vue maintainers proposed, listened, and revised the RFC for hooks in Vue API Laravel and Vue.js Marina explains that she first encountered Vue while using Laravel. “We... wanted to take advantage of this built-in connection that Vue had with Laravel. Obviously this didn’t involve any fancy set up, there was no vue CLI, nothing of this good pre-compiling or anything, it was just injecting vue into the html and creating the components there on the fly” However, discovering Vue in this way proved to be revelatory: it actually underlined what makes it, in Marina’s view, a great front end framework. “You don’t really have to commit to the whole framework,” she says; “it just got the job done for what I needed at that moment.” What problems can Vue.js solve? Marina has a pretty clear perspective on the challenges in front end development. “The problems we’re currently facing in front end development... is that people don’t want the old browsing experience where you’re clicking around and having to wait for reloads” she says. “We want to make applications… that are very performant, they have a great user experience, you are not waiting for page refreshes - it flows. What we are looking for is having your applications flow in a way that makes sense, like if you were using a desktop application.” Vue is particularly good for this, Marina explains, because of its “reusable component structure.” To a certain extent, this way of working is just another example of the wider trend across engineering towards breaking things apart. “You are trying to make these small units of code, that do this specific thing… a very common way to describe it is like this lego system where you’re just putting pieces on top of each other and it just starts making sense.” I’ve heard that analogy before in relation to containers - there’s clearly a recurring theme that’s evolving out of core principles of design. If Vue is well-suited to building lightweight but highly performant front ends, another important element is that it makes development relatively easy. Again, Marina contrasts working with Vue today with what JavaScript development looked like in the past. “You used to have... these massive massive amounts of code, and code separation of concerns was just very complicated to manage. You had to [do] a lot of overhead and work... trying to figure out how are we going to structure these files so that it makes sense.” The Vue CLI Tooling was also more complicated; something that the Vue CLI has helped to solve.“You had to deal with - at a very intricate level - how tools like Grunt worked, for example. And now you have like these pre-build tools like the Vue CLI which allow you to not have to really think about things," she says. “You don’t have to think like ‘hey, how is this going to get compiled? How is webpack going to figure things out?’ At least not at an entry level because you have it all neatly packed in this box for you with the Vue CLI.” Comparing Vue.js to React and Angular Although it’s clear that Marina is incredibly passionate and enthusiastic about Vue, she’s also circumspect about ranking JavaScript frameworks against each other. “All 3 frameworks are fantastic. All 3 get the job done. This is like asking someone why is this your favorite flavor of ice cream?!” Vue.js v. React She notes that React and Vue have a lot in common. “They share a lot of similarities - both of them use a virtual DOM… they both have this reactive component structure.” However, the key difference, from Marina’s perspective, is JSX. “If you’re talking about React, you’re talking about JSX, this approach where everything is javascript. You’re writing HTML inside the JavaScript, you’re writing CSS inside the JavaScript.” It’s JSX that puts Marina off React, saying that the way it requires you to work “doesn’t really click. I know how to do it, but just in the way I like to code things I prefer just having the separation where HTML is HTML, and where CSS is CSS.” Want to learn React.js? Search the latest React eBooks and videos. Read next: Ionic React released; Ionic Framework pivots from Angular to a native React version Vue.js v. Angular Angular, meanwhile, is “great for enterprise projects where you need this huge huge framework” says Marina. “But that also comes at a cost of having to know all the framework. All the libraries, everything that Angular brings to the table, you have to know TypeScript - it's just very opinionated at what it does and sometimes the shoe is going to be very big for the project.” So, for Marina, Vue has a degree of flexibility. It’s not as opinionated as Angular, and it doesn’t require you to write using JSX. “It can grow up until the point you need it to… From the smallest component in your application to powering full enterprise solutions.” And related to this, it means Vue is accessible - the learning curve isn’t that steep. “Vue is just very gentle in that you can start using it; you can start building things right away.” “There's a very good pay off in making yourself an expert in it... once you start getting into the core of Vue, and understanding all the little tools that are at your disposal… you can start building upon this knowledge... the framework can grow and adjust to what you’re needing.” Search the latest Angular eBooks and videos. The Vue.js community There are undoubtedly many technical reasons to consider using Vue. However, another aspect that Marina emphasises throughout our conversation is how welcoming and supportive the Vue community is. “The Vue community is one of the biggest selling points of why you should pick Vue and why Vue is so amazing.” The Vue community was, Marina says, integral in getting her to where she is now. “I was, at one point, not very into vue… and I just found a very welcoming community and a very inclusive community... People in the community care about other developers that are getting into Vue. We try our best to make this feel like a very safe, inclusive community to just get people in here and get them developing vue and help them out with the problems they’re having.” Marina deserves credit for playing a part in fostering a welcoming and supportive culture. Not only has she created a wealth of learning materials (such as a great free introductory tutorial series), she also works closely with Vue Vixens, and provides mentoring and support for other women finding their way in the industry. “This focus on education just basically became my goal… Hey, let’s do things to teach people, to get more people involved with vue,” she says. In an industry that’s sometimes defined by hyper-competitiveness, and marred by toxicity, it’s certainly a worthwhile and important goal. It’s something we can all work at. Follow Marina Mosti on Twitter: @MarinaMosti Follow Vue Vixens on Twitter: @VueVixens