Tech Guides

In Dr. W.Edwards Deming’s words “In God we trust, all others must bring data”. Organizations worldwide, revolve around data like planets revolve around the sun. Since data is so central to organizations, there are certain data visualization tools that help them understand data to make better business decisions. A lot more data is getting churned out and collected by organizations than ever before. So, how to make sense of all this data? Humans are visual creatures and our human brain processes visual information far better than textual information. In fact, presentations that use visual aids such as colors, shapes, images, etc, are found to be far more persuasive according to a research done by University of Minnesota back in 1986. Data visualization is one such process that easily translates the collected information into engaging visuals. It’s easy, cheap and doesn’t require any designing expertise to create data visuals. However, some professionals feel that data visualization is just limited to slapping on charts and graphs when that’s not actually the case. Data visualization is about conveying the right information, in a way that enhances the audience’s experience. So, if you want your graphs and charts to be more succinct and understandable, here are eight ways to improve your data visualization process: 1. Get rid of unneeded information Less is more in some cases and the same goes for data visualization. Using excessive color, jargons, pie charts and metrics take away focus from the important information. For instance, when using colors, don’t make your charts and graphs a rainbow instead use a specific set of colors with a clear purpose and meaning. Do you see the difference color and chart make to visualization in the below images? Source: Podio Similarly, when it comes to expressing your data, note how people interact at your workplace. Keep the tone of your visuals as natural as possible to make it easy for the audience to interpret your data. For metrics, only show the ones that truly bring value to your storytelling. Filter out the ones that are not so important to create less fuss. Tread cautiously while using pie charts as they can be difficult to understand sometimes and also, get rid of the elements on a chart that cause unnecessary confusion.   Source: Dashboard Zone 2. Use conditional formatting for tabular data Data visualization doesn’t need to use fancy tools or designs. Take your standard excel table for example. Do you want to point out patterns or outliers in your data? Conditional formatting is a great tool for people working with data. It involves making simple rules on a given data and once that’s done, it’ll highlight only the data that matters the most to you. This helps quickly track the main information. Conditional formatting can be used for different things. It can help spot duplicate data in your table. You need to set bounds for the data using the built-in conditional formatting. It’ll then format the cells based on those bounds, highlighting the data you want. For instance, if sales quota of over 65% is good, between 65% and 55% is average, and below 55% is poor, then with conditional formatting, you can quickly find out who is meeting the expected sales quota, and who is not. 3. Add trendlines to unearth patterns for prediction Another feature that can amp up your data visualization is trendlines. They observe the relationship between two variables from your existing data. They are also are useful for predicting future values. Trendlines are simple to add and help discover trends in the given data set. Source: Interworks It also show data trends or moving averages in your charts. Depending on the kind of data you’re working with, there are a number of trendlines out there that you can use on your visualizations. Questions like whether a new strategy seems to be working in favor of the organization can be answered with the help of trendlines. This insight, in turn, helps predict new outcomes for the future. Statistical models are used in trendlines to make predictions. Once you add trend lines to a view, it’s up to you to decide how you want them to look and behave. 4. Implement filter by rule to get more specific Filter helps display just the information that you need. Using filter by rule, you can add filter option to your dataset. Organizations produce huge amounts of data on a regular basis. Suppose you want to know which employees within your organization are consistent performers. So, instead of creating a visualization that includes all the employees and their performances, you can filter it down, so that it shows only the employees who are always doing well. Similarly, if you want to find out which day the sales went up or down, you can filter it to show results for only the past week or month depending upon your preference. 5. For complex or dense data representation, add hierarchy Hierarchies eliminate the need to create extra visualizations. You can view data from a high level and dig deeper into the specifics of the data as you come up with questions based on the data. Adding a hierarchy to the data helps club multiple information in one visualization. Source: dzone For instance, if you create a hierarchy that shows the total sales achieved by different sales representative within an organization in the past month. Now, you can further break this down by selecting a particular sales rep, and then you can go even further by selecting a specific product assigned to that sales rep. This cuts down on a lot of extra work. 6. Make visuals more appealing by formatting data Data formatting takes only a few seconds but it can make a huge difference when it comes to the audience interpreting your data. Source: dzone It makes the numbers appear more visually appealing and easier to read for the audience. It can be used for charts such as bar charts and column charts. Formatting data to show a certain number of decimals, comma separators, number font, currency or percentage can make your visualization process more engaging. 7. Include comparison for more insight Comparisons provide readers a better perspective on data. It can both improve and add insights to your visualizations by including comparisons to your charts. For instance, in case you want to inform your audience about organization’s growth in current as well as the past year then you can include comparison within the visualization. You can also use a comparison chart to compare between two data points such as budget vs actually spent. 8. Sort data to improve readability Again, sorting through data is a great way to make things easy for the audience when dealing with huge quantities of data. For instance, if you want to include information about the highest and lowest performing products, you can sort your data. Sorting can be done in the following ways: Ascending - This helps sort the data from lowest to highest. Descending -  This sorts data from highest to lowest. Data source order - Sorts the data in the order it is sorted in the data source. Alphabetic - Data is alphabetically sorted. Manual -  Data can be sorted manually in the order you prefer. Effective data visualization helps people interpret the information in data that could not be seen before, to change their minds and prompt action. These were some of the tricks and features to take your data visualization game to the next level. There are different data visualization tools available in the market to choose from. Tableau and Microsoft Power BI are among the top ones that offer great features for data visualization. So, now that we’ve got you covered with some of the best practices for data visualization, it’s your turn to put these tips to practice and create some strong visual data stories. Do you have any DataViz tips to share with our readers? Please add them in the comments below. Getting started with Data Visualization in Tableau What is Seaborn and why should you use it for data visualization? “Tableau is the most powerful and secure end-to-end analytics platform”: An interview with Joshua Milligan  

The benefits of data science are evident for all to see. Not only does it equip you with the tools and techniques to make better business decisions, the predictive power of analytics also allows you to determine future outcomes - something that can prove to be crucial to businesses. Despite all these advantages, data science is a touchy topic for many businesses. It’s worth looking at some glaring stats that show why businesses are reluctant to adopt data science: Poor data across businesses and organizations - in both private and government costs the U.S economy close to $3 Trillion per year. Only 29% enterprises are able to properly leverage the power of Big Data and derive useful business value from it. These stats show a general lack of awareness or knowledge when it comes to data science. It could be due to some preconceived notions, or simply lack of knowledge and its application that seems to be a huge hurdle to these companies. In this article, we attempt to take down some of these notions and give a much clearer picture of what data science really is. Here are 5 of the most common myths or misconceptions in data science, and why are absolutely wrong: Data Science is just a fad, it won’t last long This is probably the most common misconception. Many tend to forget that although ‘data science’ is a recently coined term, this field of study is a cumulation of decades of research and innovation in statistical methodologies and tools. It has been in use since the 1960s or even before - just that the scale at which it was being used then was small. Back in the day, there were no ‘data scientists’, but just statisticians and economists who used the now unknown terms such as ‘data fishing’ or ‘data dredging’. Even the terms ‘data analysis’ and ‘data mining’ only went mainstream in the 1990s, but they were in use way before that period. Data Science’s rise to fame has coincided with the exponential rise in the amount of data being generated every minute. The need to understand this information and make positive use of it led to an increase in the demand for data science. Now with Big Data and Internet of Things going wild, the rate of data generation and the subsequent need for its analysis will only increase. So if you think data science is a fad that will go away soon, think again. Data Science and Business Intelligence are the same Those who are unfamiliar with what data science and Business Intelligence actually entail often get confused, and think they’re one and the same. No, they’re not. Business Intelligence is an umbrella term for the tools and techniques that give answers to the operational and contextual aspects of your business or organization. Data science, on the other hand has more to do with collecting information in order to build patterns and insights. Learning about your customers or your audience is Business Intelligence. Understanding why something happened, or whether it will happen again, is data science. If you want to gauge how changing a certain process will affect your business, data science - not Business Intelligence - is what will help you. Data Science is only meant for large organizations with large resources Many businesses and entrepreneurs are wrongly of the opinion that data science is - or works best - only for large organizations. It is a wrongly perceived notion that you need sophisticated infrastructure to process and get the most value out of your data. In reality, all you need is a bunch of smart people who know how to get the best value of the available data. When it comes to taking a data-driven approach, there’s no need to invest a fortune in setting up an analytics infrastructure for an organization of any scale. There are many open source tools out there which can be easily leveraged to process large-scale data with efficiency and accuracy. All you need is a good understanding of the tools. It is difficult to integrate data science systems with the organizational workflow With the advancement of tech, one critical challenge that has now become very easy to overcome is to collaborate with different software systems at once. With the rise of general-purpose programming languages, it is now possible to build a variety of software systems using a single programming language. Take Python for example. You can use it to analyze your data, perform machine learning or develop neural networks to work on more complex data models. All this while, you can link your web API designed in Python to communicate with these data science systems. There are provisions being made now to also integrate codes written in different programming languages while ensuring smooth interoperability and no loss of latency. So if you’re wondering how to incorporate your analytics workflow in your organizational workflow, don’t worry too much. Data Scientists will be replaced by Artificial Intelligence soon Although there has been an increased adoption of automation in data science, the notion that the work of a data scientist will be taken over by an AI algorithm soon is rather interesting. Currently, there is an acute shortage of data scientists, as this McKinsey Global Report suggests. Could this change in the future? Will automation completely replace human efforts when it comes to data science? Surely machines are a lot better than humans at finding patterns; AI best the best go player, remember. This is what the common perception seems to be, but it is not true. However sophisticated the algorithms become in automating data science tasks, we will always need a capable data scientist to oversee them and fine-tune their performance. Not just that, businesses will always need professionals with strong analytical and problem solving skills with relevant domain knowledge. They will always need someone to communicate the insights coming out of the analysis to non-technical stakeholders. Machines don’t ask questions of data. Machines don’t convince people. Machines don’t understand the ‘why’. Machines don’t have intuition. At least, not yet. Data scientists are here to stay, and their demand is not expected to go down anytime soon. You need a Ph.D. in statistics to be a data scientist No, you don’t. Data science involves crunching numbers to get interesting insights, and it often involves the use of statistics to better understand the results. When it comes to performing some advanced tasks such as machine learning and deep learning, sure, an advanced knowledge of statistics helps. But that does not imply that people who do not have a degree in maths or statistics cannot become expert data scientists. Today, organizations are facing a severe shortage of data professionals capable of leveraging the data to get useful business insights. This has led to the rise of citizen data scientists - meaning professionals who are not experts in data science, but can use the data science tools and techniques to create efficient data models. These data scientists are no experts in statistics and maths, they just know the tool inside out, ask the right questions, and have the necessary knowledge of turning data into insights. Having an expertise of the data science tools is enough Many people wrongly think that learning a statistical tool such as SAS, or mastering Python and its associated data science libraries is enough to get the data scientist tag. While learning a tool or skill is always helpful (and also essential), by no means is it the only requisite to do effective data science. One needs to go beyond the tools and also master skills such as non-intuitive thinking, problem-solving, and knowing the correct practical applications of a tool to tackle any given business problem. Not just that, it requires you to have excellent communication skills to present your insights and findings related to the most complex of analysis to other stakeholders, in a way they can easily understand and interpret. So if you think that a SAS certification is enough to get you a high-paying data science job and keep it, think again. You need to have access to a lot of data to get useful insights Many small to medium-sized businesses don’t adopt a data science framework because they think it takes lots and lots of data to be able to use the analytics tools and techniques. Data when present in bulk, always helps, true, but don’t need hundreds of thousands of records to identify some pattern, or to extract relevant insights. Per IBM, data science is defined by the 4 Vs of data, meaning Volume, Velocity, Veracity and Variety. If you are able to model your existing data into one of these formats, it automatically becomes useful and valuable. Volume is important to an extent, but it’s the other three parameters that add the required quality. More data = more accuracy Many businesses collect large hordes of information and use the modern tools and frameworks available at their disposal for analyzing this data. Unfortunately, this does not always guarantee accurate results. Neither does it guarantee useful actionable insights or more value. Once the data is collected, the preliminary analysis on what needs to be done with the data is required. Then, we use the tools and frameworks at our disposal to extract the relevant insights and built an appropriate data model. These models need to be fine-tuned as per the processes for which they will be used. Then, eventually, we get the desired degree of accuracy from the model. Data in itself is quite useless. It’s how we work on it - more precisely, how effectively we work on it - that makes all the difference. So there you have it! Data science is one of the most popular skills to have in your resume today, but it is important to first clear all the confusions and misconceptions that you may have about it. Lack of information or misinformation can do more harm than good, when it comes to leveraging the power of data science within a business - especially considering it could prove to be a differentiating factor for its success and failure. Do you agree with our list? Do you think there are any other commonly observed myths around data science that we may have missed? Let us know. Read more 30 common data science terms explained Why is data science important? 15 Useful Python Libraries to make your Data Science tasks Easier

Off late, organizations are moving towards getting their systems automated. The benefits are many. Firstly, it saves off a huge chunk of time and secondly saves investments in human resources for simple tasks such as updates and so on. Few years back, Chef and Puppet were the two popular names when asked about tools for software automation. Over the years, these have got a strong rival which has surpassed them and now sits as one of the famous tools for software automation. Ansible is the one! Ansible is an open source tool for IT configuration management, deployment, and orchestration. It is perhaps the definitive configuration management tool. Chef and Puppet may have got there first, but its rise over the last couple of years is largely down to its impressive automation capabilities. And with the demands on operations engineers and sysadmins facing constant time pressures, the need to automate isn’t “nice to have”, but a necessity. Its tagline is “allowing smart people to do smart things.” It’s hard to argue that any software should aim to do much more than that. Ansible’s rise in popularity Ansible, originated in the year 2013, is a leader in IT automation and DevOps. It was bought by Red Hat in the year 2015 to achieve their goal of creating frictionless IT. The reason Red Hat acquired Ansible was its simplicity and versatility. It got the second mover advantage of entering the DevOps world after Puppet. It meant that it can orchestrate multi-tier applications in the cloud. This results in server uptime by implementing an ‘Immutable server architecture’ for deploying, creating, delete, or migrate servers across different clouds. For those starting afresh, it is easy to write, maintain automation workflows and gives them a plethora of modules which make it easy for newbies to get started. Benefits Red Hat and its community Ansible complements Red Hat’s popular cloud products, OpenStack and OpenShift. Red Hat proved to be a complex yet safe open source software for enterprises. However, it was not easy-to-use. Due to this many developers started migrating to other cloud services for easy and simple deployment options. By adopting Ansible, Red Hat finally provided an easy option to automate and modernize theri IT solutions. Customers can now focus on automating various baseline tasks. It also aids Red Hat to refresh its traditional playbooks; it allows enterprises to use IT services and infrastructure together with the help of Ansible’s YAML. The most prominent benefit of using Ansible for both enterprises and individuals is that it is agentless. It achieves this by leveraging SSH and Windows remote Management. Both these approaches reuse connections and use minimal network traffic. The approach also has added security benefits and improves both client and central management server resource utilization. Thus, the user does not have to worry about the network or server management, and can focus on other priority tasks. What can you use it for? Easy Configurations: Ansible provides developers with easy to understand configurations; understood by both humans and machines. It also includes many modules and user-built roles. Thus, one need not start building from scratch. Application lifecycle management: One can be rest assured about their application development lifecycle with Ansible. Here, it is used for defining the application and Red Hat Ansible Tower is used for managing the entire deployment process. Continuous Delivery: Manage your business with the help of Ansible push-based architecture, which allows a more sturdy control over all the required operations. Orchestration of server configuration in batches makes it easy to roll out changes across the environment. Security and Compliance: While security policies are defined in Ansible, one can choose to integrate the process of scanning and solving issues across the site into other automated processes. Scanning of jobs and system tracking ensures that systems do not deviate from the parameters assigned. Additionally, Ansible Tower provides a secure storage for machine credentials and RBAC (role-based access control). Orchestration: It brings in a high amount of discipline and order within the environment. This ensures all application pieces work in unison and are easily manageable; despite the complexity of the said applications. Though it is popular as the IT automation tool, many organizations use it in combination with Chef and Puppet. This is because it may have scaling issues and lacks in performance for larger deployments. Don’t let that stop you from trying Ansible; it is most loved by DevOps as it is written in Python and thus it is easy to learn. Moreover, it offers a credible support and an agentless architecture, which makes it easy to control servers and much more within an application development environment. An In-depth Look at Ansible Plugins Mastering Ansible – Protecting Your Secrets with Ansible Zefflin Systems unveils ServiceNow Plugin for Red Hat Ansible 2.0

The government of Tennessee now officially recognizes Smart Contracts. That’s great news if we speak in terms of the publicity blockchain will receive. By virtue of such events, the Blockchain technology and all that’s related to it are drawing closer to becoming a standard way of how things work. However, the practice shows that the deeper you delve into the nuances of Blockchain, the more you understand that we are at the very beginning of quite a long and so far uncertain path. Before we investigate Smart Contracts on the back of a Tennessee law, let’s look at the concept in lay terms. Traditional Contract vs Smart Contract A traditional contract is simply a notarized piece of paper that details actions that are to be performed under certain conditions. It doesn’t control the actions fulfillment, but only assures it. Smart Contract is just like a paper contract; it specifies the conditions. Along with that, since a smart contract is basically a program code, it can carry out actions (which is impossible when we deal with the paper one). Most typically, smart contracts are executed in a decentralized environment, where: Anyone can become a validator and verify the authenticity of correct smart contract execution and the state of the database. Distributed and independent validators supremely minimize the third-party reliance and give confidence concerning unchangeability of what is to be done. That’s why, before putting a smart contract into action you should accurately check it for bugs. Because you won’t be able to make changes once it’s launched. All assets should be digitized. And all the data that may serve as a trigger for smart contract execution must be located within one database (system). What are oracles? There’s a popular myth that smart contracts in Ethereum can take external data from the web and use it in their environment (for example, smart contract transfers money to someone who won the bet on a football match results). You can not do that, because a smart contract only relies on the data that’s on the Ethereum blockchain. Still, there is a workaround. The database (Ethereum’s, in our case) can contain so-called oracles — ‘trusted’ parties that collect data from ‘exterior world’ and deliver it to smart contracts. For more precision, it is necessary to choose a wide range of independent oracles that provide smart contract with information. This way, you minimize the risk of their collusion. Smart Contract itself is only a piece of code For a better understanding, take a look at what Pavel Kravchenko — Founder of Distributed Lab has written about Smart Contracts on his Medium post: “A smart contract itself is a piece of code. The result of this code should be the agreement of all participants of the system regarding account balances (mutual settlements). From here indirectly it follows that a smart contract cannot manage money that hasn’t been digitized. Without a payment system that provides such opportunity (for example, Bitcoin, Ethereum or central bank currency), smart contracts are absolutely helpless!” Smart Contracts under the Tennessee law Storing data on the blockchain is now a legit thing to do in Tennessee. Here are some of the primary conditions stipulated by the law: Records or contracts secured through the blockchain are acknowledged as electronic records. Ownership rights of certain information stored on blockchain must be protected. Smart Contract is considered as an event-driven computer program, that’s executed on an electronic, distributed, decentralized, shared, and replicated ledger that is used to automate transactions. Electronic signatures and contracts secured through the blockchain technologies now have equal legal standing with traditional types of contracts and signatures. It is worth noting that the definition of a smart contract is pretty clear and comprehensive here. But, unfortunately, it doesn’t let the matter rest and there are some questions that were not covered: How can smart contracts and the traditional ones have equal legal standings if the functionality of a smart contract is much broader? Namely, it performs actions, while traditional contract only assures them. How will asset digitization be carried out? Do they provide any requirements for the Smart Contract source code or some normative audit that is to be performed in order to minimize bugs risk? The problem is not with smart contracts, but with creating the ecosystem around them. Unfortunately, it is impossible to build uniform smart-contract-based relationships in our society simply because the regulator has officially recognized the technology. For example, you won’t be able to sell your apartment via Smart Contract functionality if there won’t be a regulatory base that considers: The specified blockchain platform on which smart contract functionality is good enough to sustain a broad use. The way assets are digitized. And it’s not only for digital money transactions that you will be using smart contracts. You can use smart contracts to store any valuable information, for example, proprietary rights on your apartment. Who can be the authorized party/oracle that collects the exterior data and delivers it to the Smart Contract (Speaking of apartments, it is basically the notary, who should verify such parameters as ownership of the apartment, its state, even your existence, etc) So, it’s true. A smart contract itself is a piece of code and objectively is not a problem at all. What is a problem, however, is preparing a sound basis for the successful implementation of Smart Contracts in our everyday life. Create and launch a mechanism that would allow the connection of two entirely different gear wheels: smart contracts in its digital, decentralized and trustless environment the real world, where we mostly deal with the top-down approach and have regulators, lawyers, courts, etc. FAE (Fast Adaptation Engine): iOlite’s tool to write Smart Contracts using machine translation Blockchain can solve tech’s trust issues – Imran Bashir A brief history of Blockchain About the Expert, Dr. Pavel Kravchenko Dr. Pavel Kravchenko is the Founder of Distributed Lab, blogger, cryptographer and Ph.D. in Information Security. Pavel is working in blockchain industry since early 2014 (Stellar). Pavel's expertise is mostly focused on cryptography, security & technological risks, tokenization. About Distributed Lab Distributed Lab is a blockchain expertise center, with a core mission to develop cutting-edge enterprise tokenization solutions, laying the groundwork for the coming “Financial Internet”. Distributed Lab organizes dozens of events every year for the Crypto community – ranging from intensive small-format meetups and hackathons to large-scale international conferences which draw 1000+ attendees.  

It was back in 2016 when we first heard about the Google Fuchsia platform which was supposed to be an alternative to the Android operating system. Google had revealed a WIP version in 2016 and since then a lot of dust had gathered on this news until the latest developments and news resurfaced in January 2018. The question on everyone’s mind is is do you really need to be concerned about Fuchsia OS and does it have what it takes to even challenge the market positioning of Android? Before we come to these questions, let’s look at what Fuchsia has to offer. The Fuchsia UI - Inspired by Material Design Fuchsia brings a complete material design approach to UI design. The first look shared by Google seemed a lot different than the Android UI. Source: The Droid guy Basic Android UI Source: Tech Radar Google Fuchsia on a smartphone device There is more depth; the text, images and wallpapers all look sleeker and feel like a peek through a window rather than being underlays to text and icons. Fuchsia currently offers two layouts - a mobile-centric design codenamed Armadillo, and a more traditional desktop experience codenamed Capybara. While the mobile centric version is in more focus, the desktop version is far from being ready. Google is trying to push Material Design heavily with Fuchsia. How far hey will succeed depends on their roadmap and future investment plan. The Concept of One OS across all devices It has been a long standing dream of Google to make all the different devices work under one OS platform. Google seems to be betting on Fuchsia to be that OS on Desktops, Tablets & Mobiles too. The Google ledger facility allows you to get a cloud account to seamlessly access and manage different Google services. The primary feature of seamless transition of data from one device to another, is sure to help the users play around with it effortlessly. Using the Custom Kernel Feature What makes Android version updates a pain to implement is that different devices run different kernel versions of Linux,the spine of Android. As such, the update rollouts are never in unison. This can create security flaws, and can be a real worrisome factor for Android users. This is where Fuchsia trumps Android. Fuchsia has its own Kernel - Zircon, which is designed to be consistently upgradeable. This helps the apps to be isolated from the Kernel and hence adds an extra security layer to the apps and also doesn’t render these apps useless after an OS update. Language Interoperability The most important aspect from the developer’s perspective is the feature of multi language support. Fuchsia is written in Dart using the latest Google cross platform framework, Flutter. It also provides support for development in Go and Rust. It is also extending support for Swift developers. This along with the added FIDL protocol, will help the developers to easily develop different parts in different languages - such as using a Go based backend with a Dart based front end. This gives developers immense power and flexibility. Although these features seem to be useful and interesting, Fuchsia will need a steady development pipeline and regular updates to reach a stable version so that devices can use it as their default UI. Keeping the current development trends in mind, we can safely conclude that till the next stable release, you can continue to browse your Android phones and not worry about being replaced by Fuchsia or any other competitor. Google updates biometric authentication for Android P, introduces BiometricPrompt API Google’s Android Things, developer preview 8: First look Google Flutter moves out of beta with release preview 1  

I remember when I was in college a few years ago, this was a question everyone was asking. People who were studying Computer Science were always scared of this happening. Although it’s ironic because knowing the above, they were still brave enough to get into Computer Science in the first place! Okay, on a serious note, this is a highly debated topic and the IT industry is labeled to be notorious for employee burnout. The harsh reality Honestly speaking, I have developer friends who earn pretty good salary packages, even those working at a junior level. However, just two in five of them are actually satisfied with their jobs. They seem to be heading towards burnout quite quickly, too quickly in fact. I would understand if you told me that a middle aged person, having certain health conditions et al, working in a tech company, was nearing burnout. Here I see people in their early 20’s struggling to keep up, wishing for the weekend to come! Facts and figures Last month, a workspace app called Blind surveyed over 11K (11,487 to be precise) employees in the tech industry and the responses weren’t surprising! At least for me. The question posed to them was pretty simple: Are you currently suffering from job burnout? Source: TeamBlind Oh yeah, that’s a whopping 6,566 employees! Here’s some more shocking stats: When narrowed down to 30 companies, 25 of them had an employee burnout rate of 50% or higher. Only 5 companies had an employee burnout rate below 50%. Moreover, 16 out of the 30 companies had an employee burnout rate that was higher than the survey average of 57.16%. While Netflix had the least number of employees facing burnout, companies like Credit Karma, Twitch and Nvidia recorded the highest. I thought I’d analyse a bit and understand what some of the most common reasons causing burnout in the tech industry, could be. So here they are: #1 Unreasonable workload Now I know this is true for a fact! I’ve been working closely with developers and architects for close to 5 years now and I’m aware of how unreasonable projects can get. Especially their timelines. Customer expectation is something really hard to meet in the IT sector, mainly because the customer usually doesn’t know much about tech. Still, deadlines are set extremely tight, like a noose around developers’ necks, not giving them any space to maneuver whatsoever. Naturally, this will come down hard on them and they will surely experience burnout at some time, if not already. #2 Unreasonable managers In our recent Skill-Up survey, more than 60% of the respondents felt they knew more about tech, than what their managers did. More than 40% claimed that the biggest organisational barriers to their organisation’s (theirs as well) goals was their manager’s lack of tech knowledge. As with almost everyone, developers expect managers to be like a mentor, able to guide them into taking the right decisions and making the right choices. Rather, with the lack of knowledge, managers are unable to relate to their team members, ultimately coming across as unreasonable to them. On the other side of town, IT Management has been rated as one of the top 20 most stressful jobs in the world, by careeraddict! #3 Rapidly changing tech The tech landscape is one that changes ever so fast, and developers tend to get caught up in the hustle to stay relevant. I honestly feel the quote, “Time and tide wait for none” needs to be appended to “Time, tide and tech wait for none”! The competition is so high that if they don’t keep up, they’re probably history in a couple of years or so. I remember in the beginning of 2016, there was a huge hype about Data Science and AI - there was a predicted shortage of a million data scientists by 2018. Thousands of engineers all around the world started diving into their pockets to fund their Data Science Masters Degrees. All this can put a serious strain on their health and they ultimately meet burnout. #4 Disproportionate compensation Tonnes of software developers feel they’re underpaid, obviously leading them to lose interest in their work. Ever wonder why developers jump companies so many times in their careers? Now this stagnation is happening while on the other hand, work responsibilities are rising. There’s a huge imbalance that’s throwing employees off track. Chris Bolte, CEO of Paysa, says that companies recruit employees at competitive rates. But once they're on board, the companies don't tend to pay much more than the standard yearly increase. This is obviously a bummer and a huge demotivation for the employees. #5 Organisation culture The culture prevailing in tech organisations has a lot to do with how fast employees reach burnout. No employee wants to feel they’re tools or perhaps cogs in a wheel. They want to feel a sense of empowerment, that they’re making an impact and they have a say in the decisions that drive results. Without a culture of continuous learning and opportunities for professional and personal growth, employees are likely to be driven to burnout pretty quickly, either causing them to leave the organisation or worse still, lose confidence in themselves. #6 Work life imbalance This is a very tricky thing, especially if you’re out working long hours and you’re mostly unhappy at work. Moreover, developers usually tend to take their work home so that they can complete projects on time, and that messes up everything. When there’s no proper work life balance, you’re most probably going to run into a health problem, which will lead you to burnout, eventually. #7 Peer pressure This happens a lot, not just in the IT industry, but it’s more common here owing to the immense competition and the fast pace of the industry itself. Developers will obviously want to put in more efforts than they can, simply because their team members are doing it already. This can go two ways: One where their efforts still go unnoticed, and secondly, although they’re noticed, they’ve lost on their health and other important aspects of life. By the time they think of actually doing something innovative and productive, they’ve crashed and burned. [dropcap]I[/dropcap]f you ask me, burnout is a part and parcel of every industry and it majorly depends on mindset. The mindset of employees as well as the employer. Developers should try avoiding long work hours as far as possible, while trying to take their minds off work by picking up a nice hobby and exploring more ways to enrich their lives. On the other side of the equation, employers and managers should do better at understanding their team’s limitations or problems, while also maintaining an unbiased approach towards the whole team. They should realize that a motivated and balanced team is great for their balance sheet in the long run. They must be serious enough to include employee morale and nurturing a great working environment as one of management’s key performance indicators. If the IT industry must rise as a phoenix from the ashes, it will take more than a handful of people or organizations changing their ways. Change begins from within every individual and at the top for every organization. Should software be more boring? The “Boring Software” manifesto thinks so These 2 software skills subscription services will save you time – and cash Don’t call us ninjas or rockstars, say developers  

Lately, IoT is beginning to play an integral part in various industries, be it at the consumer-level, or at the enterprise side of it. With a lot of big players like Apple, Microsoft, Amazon, and Google entering this market, IoT adoption has scaled tremendously. It is said to have jumped from a hobbyist level to an industry infrastructure where everything functions on smart devices, that can talk. The bulk release of popular IoT products prove that this market is getting bigger and a lot of individuals have been amazed with home automation products such as Amazon Alexa, Apple Homepod, Google Home and others. These devices are one of the most sought-after things for hobbyist and enthusiasts who are interested to do simple automation with sensors. Following are 5 IoT projects ideas that you can build without a hole in the pocket. To learn how to actually build similar kind of projects, check out our books; Internet of Things with Raspberry Pi 3 Smart Internet of Things Projects Raspberry Pi 3 Home Automation Projects Weather control station This project will not only help you measure the room temperature but will also help you measure the altitude and the pressure in the room. For this project you will need the Adafruit Starter Pack for Windows 10 IoT Core on the latest Raspberry Pi kit. Along with the Raspberry Pi Kit you will also be using other sensors that read temperature, pressure, and altitude. To make your weather station advanced, you can connect the device to your cloud account to store the weather data. Hardware Raspberry Pi 2 or 3 Breadboard Adafruit BMP280 Barometric Pressure & Altitude Sensor Software Windows 10 IoT Core Approximate total cost Less than $60 Facial Recognition Door Self-built home security projects are some of the most popular DIY projects because they can be cheaper and simple compared to bulky professional installations. Here's a project that controls entry access using facial recognition, thanks to Microsoft Project Oxford. This project from Mazudo, based on Raspberry Pi and Windows IoT, is posted on Hackster.io. This is a handy project for DIY enthusiasts who want to build a quick security lock for their homes. Hardware Raspberry Pi 3 Breadboard USB camera Relay switch Speaker Software Windows 10 IoT Core Approximate total cost Less than $50 Your very own Alexa Echo Alexa Echo has always been a handy device, which can take notes, schedule reminders for your appointments, and play podcasts for you. Brilliant, isn’t it?  You can build a fully functional customized Alexa Echo with all the features of Alexa, apart from accessing official music servers like Amazon prime. It will also have an integration with recently included third party apps like todoist and Any.do. This DIY Echo can also be connected to your cell phone devices to manage notifications when the timer goes off, and so on. Only one thing that your DIY will be missing is the ability to function as a bluetooth speaker. Hardware Raspberry Pi 3 Breadboard USB speaker and mic Software Raspbian Approximate total cost Less than $50 Pet Feeder You surely don’t want your pet to starve when you’re away, do you? This customized pet feeder is controlled via the internet; set timings and feed your pet automatically later. These pet feeders are directly connected to WiFi using ESP8266 chip. We can easily add features like controlling the device using cell phone and making dashboards using Freeboard. This project can be later upgraded or rightly reprogrammed to fill your snack bowl at regular intervals as well. Hardware Arduino PIR motion sensor ESP8266 ESP-01 Software Arduino IDE ESP8266Flasher.exe Approximate total cost Less than $40 Video Surveillance Robot Video surveillance is a process of monitoring a scenario, person or an environment as a whole. A video surveillance robot can capture the activities happening in the surrounding where it is deployed and can be controlled using a GUI Interface. For further enhancements, you can even connect your device to the cloud and save the recorded data there. Hardware Raspberry Pi ARM Cortex- A7 CPU L293 motor driver Software Raspbian Approximate total cost Less than $50 These are few economical yet highly useful Internet of Things projects, which can be leveraged to improve your daily activities. Still not convinced?. Think of it this way. Buying the microchip board is a one time investment as it can be reused in separate projects. The sensors and other peripherals aren’t that expensive. You might say, it’s just way easier to buy an IoT device. I would argue that, buying an IoT device is not as satisfying as building one for the same purpose. In the end, there are multiple advantages of building one as you can brag about it to your friends and most importantly include it in your resume to give you that edge over others in an interview. Cognitive IoT: How Artificial Intelligence is remoulding Industrial and Consumer IoT Windows 10 IoT Core: What you need to know 5 reasons to choose AWS IoT Core for your next IoT project  

Google Cloud Platform (GCP) is considered to be one of the Big 3 cloud platforms among Microsoft Azure and AW. GCP is widely used cloud solutions supporting AI capabilities to design and develop smart models to turn your data into insights at a cheap, affordable cost. The following excerpt is taken from the book 'Cloud Analytics with Google Cloud Platform' authored by Sanket Thodge. GCP offers many machine learning APIs, among which we take a look at the 3 most popular APIs: Cloud Speech API A powerful API from GCP! This enables the user to convert speech to text by using a neural network model. This API is used to recognize over 100 languages throughout the world. It can also support filter of unwanted noise/ content from a text, under various types of environments. It supports context-awareness recognition, works on any device, any platform, anywhere, including IoT. It has features like Automatic Speech Recognition (ASR), Global Vocabulary, Streaming Recognition, Word Hints, Real-Time Audio support, Noise Robustness, Inappropriate Content Filtering and supports for integration with other APIs of GCP.  The architecture of the Cloud Speech API is as follows: In other words, this model enables speech to text conversion by ML. The components used by the Speech API are: REST API or Google Remote Procedure Call (gRPC) API Google Cloud Client Library JSON API Python Cloud DataLab Cloud Data Storage Cloud Endpoints The applications of the model include: Voice user interfaces Domotic appliance control Preparation of structured documents Aircraft / direct voice outputs Speech to text processing Telecommunication It is free of charge for 15 seconds per usage, up to 60 minutes per month. More than that will be charged at $0.006 per usage. Now, as we have learned about the concepts and the applications of the model, let's learn some use cases where we can implement the model: Solving crimes with voice recognition: AGNITIO, A voice biometrics specialist partnered with Morpho (Safran) to bring Voice ID technology into its multimodal suite of criminal identification products. Buying products and services with the sound of your voice: Another most popular and mainstream application of biometrics, in general, is mobile payments. Voice recognition has also made its way into this highly competitive arena. A hands-free AI assistant that knows who you are: Any mobile phone nowadays has voice recognition software in the form of AI machine learning algorithms. Cloud Translation API Natural language processing (NLP) is a part of artificial intelligence that focuses on Machine Translation (MT). MT has become the main focus of NLP group for many years. MT deals with translating text from the source language to text in the target language. Cloud Translation API provides a graphical user interface to translate an inputted string of a language to targeted language, it’s highly responsive, scalable and dynamic in nature. This API enables translation among 100+ languages. It also supports language detection automatically with accuracy. It provides a feature to read a web page contents and translate to another language, and need not be text extracted from a document. The Translation API supports various features such as programmatic access, text translation, language detection, continuous updates and adjustable quota, and affordable pricing. The following image shows the architecture of the translation model:  In other words, the cloud translation API is an adaptive Machine Translation Algorithm. The components used by this model are: REST API Cloud DataLab Cloud data storage Python, Ruby Clients Library Cloud Endpoints The most important application of the model is the conversion of a regional language to a foreign language. The cost of text translation and language detection is $20 per 1 million characters. Use cases Now, as we have learned about the concepts and applications of the API, let's learn two use cases where it has been successfully implemented: Rule-based Machine Translation Local Tissue Response to Injury and Trauma We will discuss each of these use cases in the following sections. Rule-based Machine Translation The steps to implement rule-based Machine Translation successfully are as follows: Input text Parsing Tokenization Compare the rules to extract the meaning of prepositional phrase Find word of inputted language to word of the targeted language Frame the sentence of the targeted language Local tissue response to injury and trauma We can learn about the Machine Translation process from the responses of a local tissue to injuries and trauma. The human body follows a process similar to Machine Translation when dealing with injuries. We can roughly describe the process as follows: Hemorrhaging from lesioned vessels and blood clotting Blood-borne physiological components, leaking from the usually closed sanguineous compartment, are recognized as foreign material by the surrounding tissue since they are not tissue-specific Inflammatory response mediated by macrophages (and more rarely by foreign-body giant cells) Resorption of blood clot Ingrowth of blood vessels and fibroblasts, and the formation of granulation tissue Deposition of an unspecific but biocompatible type of repair (scar) tissue by fibroblasts Cloud Vision API Cloud Vision API is powerful image analytic tool. It enables the users to understand the content of an image. It helps in finding various attributes or categories of an image, such as labels, web, text, document, properties, safe search, and code of that image in JSON. In labels field, there are many sub-categories like text, line, font, area, graphics, screenshots, and points. How much area of graphics involved, text percentage, what percentage of empty area and area covered by text, is there any image partially or fully mapped in web are included web contents. The document consists of blocks of the image with detailed description, properties show that the colors used in image is visualized. If any unwanted or inappropriate content is removed from the image through safe search. The main features of this API are label detection, explicit content detection, logo and landmark detection, face detection, web detection, and to extract the text the API used Optical Character Reader (OCR) and is supported for many languages. It does not support face recognition system. The architecture for the Cloud Vision API is as follows: We can summarize the functionalities of the API as extracting quantitative information from images, taking the input as an image and the output as numerics and text. The components used in the API are: Client Library REST API RPC API OCR Language Support Cloud Storage Cloud Endpoints Applications of the API include: Industrial Robotics Cartography Geology Forensics and Military Medical and Healthcare Cost: Free of charge for the first 1,000 units per month; after that, pay as you go. Use cases This technique can be successfully implemented in: Image detection using an Android or iOS mobile device Retinal Image Analysis (Ophthalmology) We will discuss each of these use cases in the following topics. Image detection using Android or iOS mobile device Cloud Vision API can be successfully implemented to detect images using your smartphone. The steps to do this are simple: Input the image Run the Cloud Vision API Executes methods for detection of Face, Label, Text, Web and Document properties Generate the response in the form of phrase or string Populate the image details as a text view Retinal Image Analysis – ophthalmology Similarly, the API can also be used to analyze retinal images. The steps to implement this are as follows: Input the images of an eye Estimate the retinal biomarkers Do the process to remove the effected portion without losing necessary information Identify the location of specific structures Identify the boundaries of the object Find similar regions in two or more images Quantify the image with retinal portion damage You can learn a lot more about the machine learning capabilities of GCP on their official documentation page. If you found the above excerpt useful, make sure you check out our book 'Cloud Analytics with Google Cloud Platform' for more information on why GCP is a top cloud solution for machine learning and AI. Read more Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) How machine learning as a service is transforming cloud Google announce the largest overhaul of their Cloud Speech-to-Text  

The Cold War between the United States and Russia ended in 1991. However, considering the ‘behind the scenes’ behavior of the world’s two current Super Powers – China and the USA, another might just be beginning. This time around, many believe that the real battle doesn’t relate to the trade deficit between the two countries, despite new stories detailing the escalation of trade tariffs. In the next decade and a half, the real battle will take place between China and the USA in the technology arena, specifically, in the area of Artificial Intelligence or AI. China’s not shy about it’s AI ambitions China has made clear its goals when it comes to AI. It has publicly announced its plan to be the world leader in Artificial Intelligence by 2030. The country has learned a hard lesson, missing out on previous tech booms, notably, in the race for internet supremacy early this century. Now, they are taking a far more proactive stance. The AI market is estimated to be worth $150 billion per year by 2030, slightly over a decade from now, and China has made very clear public statements that the country wants it all. The US, in contrast has a number of private companies striving to carve out a leadership position in AI but no holistic policy. Quite the contrary, in fact. Trumps government say, “There is no need for an AI moonshot, and that minimizing government interference is the best way to make sure the technology flourishes.” What makes China so dangerous as an AI Threat ? China’s background and current circumstance gives them a set of valuable strategic advantages when it comes to AI. AI solutions are based, primarily, on two things. First, of critical importance is the amount of data available to ‘train’ an AI algorithm and the relative ease or difficulty of obtaining access to it. Secondly, the algorithm which sorts the data, looking for patterns and insights, derived from research, which are used to optimize the AI tools which interpret it. China leads the world on both fronts. China has more data: China’s population is 4 times larger than the US’s giving them a massive data advantage. China has a total of 730 million daily internet users and 704 million smartphone mobile internet users. Each of the connected individuals uses their phone, laptop or tablet online each day. Those digital interactions leave logs of location, time, action performed and many other variables. In sum then, China’s huge population is constantly generating valuable data which can be mined for value. Chinese regulations give public and private agencies easier access to this data: Few countries have exemplary records when it comes to human rights. Both Australia, and the US, for example, have been rebuked by the UN for their treatment of immigration in recent years. Questions have been asked of China too. Some suggest that China’s centralized government, and alleged somewhat shady history when it comes to human rights means they can provide internet companies with more data, more easily, than their private equivalents in the US could dream of. Chinese cybersecurity laws require companies doing business in the country to store their data locally. The government has placed one state representative on the board of each of their major tech companies, giving them direct, unfettered central government influence in the strategic direction and intent of those companies, especially when it comes to coordinating the distribution of the data they obtain. In the US, data leakage is one of the most prominent news stories of 2018. Given Facebook’s presentation to congress around the Facebook/Cambridge Analytica data sharing scandal, it would be hard to claim that US companies have access to data outside each company competing to evolve AI solutions fastest. It’s more secretive: China protects its advantage by limiting other countries’ access to its findings / information related to AI. At the same time, China takes advantage of the open publication of cutting edge ideas generated by scientists in other areas of the world. How China is doubling down on their natural advantage in AI solution development A number of metrics show China’s growing advantage in the area. China is investing more money in the area and leading the world in the number of university led research papers on AI that they’re publishing. China is investing more money in AI than the USA. They overtook the US in AI funds allocation in 2015 and have been increasing investment in the area since. Source: Wall Street Journal China now performs more research in to AI than the US – as measured by the number of published scientific peer reviewed journals. Source: HBR Why ‘Network Effects’ will decide the ultimate winner in the AI Arms Race You won’t see evidence of a Cold War in the behaviors of World Leaders. The handshakes are firm and the visits are cordial. Everybody smiles when they meet at the G8. However, a look behind the curtain clearly shows a 21st Century arms race underway, being led by investments  related to AI in both countries. Network effects ensure that there is often only one winner in a fight for technological supremacy. Whoever has the ‘best product’ for a given application, wins the most users. The data obtained from those users’ interactions with the tool is used to hone its performance. Thus creating a virtuous circle. The result is evident in almost every sphere of tech: Network effects explain why most people use only Google, why there’s only one Facebook and how Netflix has overtaken cable TV in the US as the primary source of video entertainment. Ultimately, there is likely to be only one winner in the war surrounding AI, too. From a military perspective, the advantage China has in its starting point for AI solution development could be the deciding factor. As we’ve seen, China has more people, with more devices, generating more data. That is likely to help the country develop workable AI solutions faster. They ingest the hard won advantages that US data scientists develop and share – but do not share their own. Finally, they simply outspend and out-research the US, investing more in AI than any other country. China’s coordinated approach outpaces the US’s market based solution with every step. The country with the best AI solutions for each application will gain a ‘Winner Takes All’ advantage and the winning hand in the $300 billion game of AI market ownership. We must change how we think about AI, urge AI founding fathers Does AI deserve to be so Overhyped? Alarming ways governments are using surveillance tech to watch you    

Geography Visualization, also called as Geovisualization plays a pivotal role in areas like cartography, geographic information systems, remote sensing and global positioning systems. Uber, a peer-to-peer transportation network company headquartered at California believes in data-driven decision making and hence keeps developing smart frameworks like deck.gl for exploring and visualizing advanced geospatial data at scale. Uber strives to make the data web-based and shareable in real-time across their teams and customers. Early this month, Uber surprised the geospatial market with its newly open-source toolbox, kepler.gl, a geoanalytics tool to gain quick insights from geospatial data with amazing and intuitive visualizations. What’s exactly Kepler.gl is? kepler.gl is a visualization-rich web platform, developed on top of deck.gl, a WebGL-powered data visualization library providing real-time visual analytics of millions of geolocation points. The platform provides visual exploration of geographical data sets along with spatial aggregation of all data points collected. The platform is said to be data-agnostic with a single interface to convert your data into insightful visualizations. https://www.youtube.com/watch?v=i2fRN4e2s0A The platform is very user-friendly where one can just drag the CSV or the GeoJSON files and drop them into the browser to visualize the dataset more intuitively. The platform is supported with different map layers, filtering option, aggregation feature through which you can get the final visualization in an animated format or like a video. The usability of features is so high that you can apply all the metrics available to your data points without much of a hassle. The web platform exhibits high performance where you can get insights from your spatial data in less than 10 minutes and that too in a single window. Another advantage of this framework is it does not involve any sort of coding and hence non-technical users can also reap the benefits by churn valuable insights from the data points. The platform is also equipped with some advanced, complex features such as 2D cartographic plane,a separate dimension for altitude, visibility of height of hexagon and grids. The users seem happy with the new height feature which helps them detect abnormalities and illicit traits in an aggregated map. With the filtering menu, the analysts and engineers can compare their data and have a granular look at their data points. This option also helps in reading the histogram well and one can easily detect outliers and make their dataset more reliable. It  has a feature to add playback to time series data points which makes getting useful information of real time location systems easy. The team at Uber looks at this toolbox with a long-term vision where they are planning to keep adding new features and enhancements to make it highly functional and a single-click visualization dashboard. The team has already announced that they would be powering it up with two major enhancements to the current functionality in next couple of months. They would add support on, More robust exploration: There will be interlinkage between charts and maps, and support for custom charts, maps and widgets like the renowned BI tool Tableau through which it will facilitate analytics teams to unveil deeper insights. Addition of newer geo-analytical capabilities: To support massive datasets, there will be added features on data operations such as polygon aggregation, union of data points, operations like joining and buffering. Companies across different verticals such as Airbnb, Atkins Global, Cityswifter, Mapbox have found great value in kepler.gl offerings and are looking towards engineering their products to leverage this framework. The visualization specialists at these companies have already praised Uber for building such a simple yet fast platform with remarkable capabilities. To get started with kepler.gl, read the documentation available at Github and start creating visualizations and enhance your geospatial data analysis. Top 7 libraries for geospatial analysis Using R to implement Kriging – A Spatial Interpolation technique for Geostatistics data Data Visualization with ggplot2

Mobile has become an increasingly important part of many modern businesses tech strategy. In everything from eCommerce to financial services, mobile applications aren’t simply a ‘nice to have’, they’re essential. Customers expect them. The most difficult question today isn’t ‘do we need a mobile app’ Instead, it’s ‘which type of mobile app should we build: native vs cross platform?’ There are arguments to be made for cross platform mobile development and native app development. Developers who have worked on either project will probably have an opinion on the right way to go. Like many things in tech, however, the cross platform v native debate is really a question of which one is right for you. From both a business and capability perspective, you need to understand what you want to achieve and when. Let’s take a look at the difference between cross-platform framework or a native development platforms. You should then feel comfortable enough to make the right decision about which mobile platform is right for you. Cross platform development? A cross platform application runs across all mobile operating systems without any extra coding. By all mobile operating systems, I mean iOS and Android (windows phones are probably on their way out). A cross platform framework provides all the tools to help you create cross-platform apps easily. Some of the most popular cross- platform frameworks include: Xamarin Corona SDK appcelerator titanium PhoneGap Hybrid mobile apps One specific form of cross-platform mobile  application is Hybrid. With hybrid mobile apps, the graphical user interface (GUI) is developed using HTML5. These are then wrapped in native webpack containers and deployed on iOS and Android devices. A native app is specifically designed for one particular operating system. This means it will work better in that specific environment than one created for multiple platforms. One of the latest native android development framework is Google Flutter. For iOS, it’s Xcode.. Native mobile development vs Cross platform development If you’re a mobile developer, which is better? Let’s compare cross platform development with mobile development: Cross-platform development is more cost effective. This is simply because you can reuse 80% of your code becase you’re essentially building one application. The cost of native development is roughly double to that of Cross-platform development, although cost of android development is roughly 30% more than iOS development. Cross-platform development takes less time. Although some coding has to be done natively, the time taken to develop one app is, obviously, less than to develop two. Native apps can use all system resources. No other app can have any additional features . They are able to use the maximum computing power provided by the GPU and CPU; this means that load times are often pretty fast.. Cross platform apps have restricted access to system resources. Their access is dependent on framework plugins and permissions. Hybrid apps usually take more time to loadbecause smartphone GPUs are generallyless powerful than other machines. Consequently, unpacking a HTML5 UI takes more time on a mobile device. The same reason forced Facebook to shift their mobile apps from Hybrid to Native which according to facebook, improved their app load time and loading of newsfeed and images in the app. The most common challenge with about cross-platform mobile development is been balancing the requirements of iOS and Android UX design. iOS is quite strict about their UX and UI design formats. That increases the chances of rejection from the app store and causes more recurring cost. A critical aspect of Native mobile apps is that if they are designed properly and properly synchronized with the OS, they get regular software updates. That can be quite a difficult task for cross-platform apps. Finally, the most important consideration that should determine your choice are your (or the customer’s) requirements. If you want to build a brand around your app, like a business or an institution, or your app is going to need a lot of GPU support like a game, then native is the way to go. But if your requirement is simply to create awareness and spread information about an existing brand or business on a limited budget then cross-platform is probably the best route to go down. How to integrate Firebase with NativeScript for cross-platform app development Xamarin Forms 3, the popular cross-platform UI Toolkit, is here! A cross-platform solution with Xamarin.Forms and MVVM architecture  

With the advent of automation, artificial intelligence(AI), and machine learning, we hear about their applications regularly in news across industries. This has been especially true for healthcare where various hospitals, health insurance companies, healthcare units, etc. have been impacted in more substantial and concrete ways by AI when compared to other industries. In the recent years, healthcare startups and life science organizations have ventured into Artificial Intelligence technology and are one of the most heavily invested areas by VCs. Various organizations with ties to healthcare are leveraging the advances in artificial intelligence algorithms for remote patient monitoring, medical imaging and diagnostics, and implementing newly developed sophisticated methods, and applications into the system. Let’s explore some of the most popular AI applications which have revamped the healthcare industry. Proper maintenance and management of medical records Assembling, analyzing, and maintaining medical information and records is one of the most commonly used applications of AI. With the coming of digital automation, robots are being used for collecting and tracing data for proper data management and analysis. This has brought down manual labor to a considerable extent. Computerized medical consultation and treatment path The existence of medical consultation apps like DocsApp allows a user to talk to experienced and specialist doctors on chat or call directly from their phone in a private and secure manner. Users can report their symptoms into the app and this ensures the users are connected to the right specialist physicians as per the user’s medical history. This has been made possible due to the existence of AI systems. AI also aids in treatment design like analyzing data, making notes and reports from a patient’s file, thereby helping in choosing the right customized treatment as per the patient’s medical history. Eliminates monotonous manual labor Various medical tasks like analyzing X-Ray reports, test reports, CT scans and other common tasks can be executed by robots and other mechanical devices more accurately. Radiology is one such discipline wherein human supervision and control have dropped to a substantial level due to the extensive use of AI. Aids in drug manufacture and creation Generally, billions of dollars are spent on developing pharmaceuticals through clinical trials and they take almost a decade or two to manufacture a life-saving drug. But now, with the arrival of AI, the entire drug creation procedure has been simplified and has become pretty reasonable as well. Even in the recent outbreak of the Ebola virus, AI was used for drug discovery, to redesign solutions and to scan the current existing medicines to eradicate the plague. Regular health monitoring In the current era of digitization, there are certain wearable health trackers – like Garmin, Fitbit, etc. which can monitor your heart rate and activity levels. These devices help the user to keep a close check on their health by setting up their exercise plan, or reminding them to stay hydrated. All this information can also be shared with your physician to track your current health status through AI systems. Helps in the early and accurate detection of medical disorders AI helps in spotting carcinogenic and cardiovascular disorders at an early stage and also aids in predicting health issues that people are likely to contract due to hereditary or genetic reasons. Enhances medical diagnosis and medication management Medical diagnosis and medication management are the ultimate data-based problems in the healthcare industry. IBM’s Watson, a deep learning system has simplified medical investigation and is being applied to oncology, specifically for cancer diagnosis. Previously, human doctors used to collect patient data, research on it and conduct clinical trials. But with AI, the manual efforts have reduced considerably. For medication management, certain apps have been developed to monitor the medicines taken by a patient. The cellphone camera in conjunction with AI technology to check whether the patients are taking the medication as prescribed. Further, this also helps in detecting serious medical problems and tracking patients medicine adaptability and participants behavior in certain scientific trials. To conclude, we can connote that we are gradually embarking on the new era of cognitive technology with the power of AI-based systems. In the coming years, we can expect AI to transform every area of the healthcare industry that it brushes up with. Experts are constantly looking for ways and means to organize the existing structure and power up healthcare on the basis of new AI technology. The ultimate goals being to improve patient experience, build a better public health management and reduce costs by automating manual labor. Author Bio Maria Thomas is the Content Marketing Manager and Product Specialist at GreyCampus with eight years rich experience on professional certification courses like PMI- Project Management Professional, PMI-ACP, Prince2, ITIL (Information Technology Infrastructure Library), Big Data, Cloud, Digital Marketing and Six Sigma. Healthcare Analytics: Logistic Regression to Reduce Patient Readmissions How IBM Watson is paving the road for Healthcare 3.0

The year is 2018 and it’s all over the television, the internet, the newspapers; people are talking about it in coffee shops, at office desks across from where we sit, and what not. There’s a scramble for people to learn how to program. It’s a confusing and scary situation for someone who has never written a line of code, to think about all these discussions that are doing the rounds. In this article, I’m going to give you 5 reasons why I think you should learn to code, even if you are not a programmer by profession. Okay, first thing’s first: What is Programming? Programming is the process of writing/creating a set of instructions that tell a computer how to perform a certain task. Just like you would tell someone to do something and you would tell them in a language like English, computers also understand particular languages. This is called a programming language. There are several like Java, Python, C# (pronounced Csharp), etc. Just like many would find English easier to learn that French or maybe Cantonese, every person finds each language different, although almost all languages can do pretty much the same thing. So now, let’s see what our top 5 reasons are to learn a programming language, and ultimately, how to program a computer. #1 Automate stuff: How many times do we find ourselves doing the same old monotonous work ourselves. For example, a salesperson who has a list of 100 odd leads, will normally mail each person manually. How cool would it be if you could automate that and let your computer send each person a mail separately addressing them appropriately? Or maybe, you’re a manager who has a load of data you can’t really make sense of. You can use a language like Python to sort it and visualise your findings. Yes, that’s possible with programming! There’s a lot of other stuff that can be automated too, like HR scanning resumes manually. You can program your computer to do it for you, while you spend that time doing something more productive! Now while there might be softwares readily available that could do this for you, they’re pretty much standard and non-customisable. With programming, you can build something that’s tailor-made to your exact requirement. #2 Start thinking more logically: When you learn to program, you start thinking about outcomes more logically. Programming languages are all about logic and problem-solving. You will soon learn how to break down problems into small parts and tackle them individually. You can apply this learning in your own personal and work life. #3 Earn great moolah Programming pays really well and even freelance jobs pay close to $100 an hour. You could have your day job, while taking advantage of your programming skills to build websites, games, create applications for clients, after work or over the weekend, while making some good bucks. Here’s a list of average salaries earned by programmers, based on the language they used: Source: TOP 10 ChallengeRocket.com ranking of projected earnings in 2017 #4 Another great idea! Well, in case you’re an entrepreneur or are planning to become one, learning a programming language is sure to benefit you a great deal. The most successful startups these days are AI and software based and even though you might not be the one doing the programming, you will be interacting with those who will. It makes things much easier when you’re discussing with such a person, and more importantly, it saves you from being taken for a ride in many ways. #5 Having fun Unlike several other things that are boring to learn and will get you frustrated in a matter of hours, programming isn’t like that. That’s not to say that programming doesn’t have a learning curve, but with the right sources, you can learn it quickly and effectively. There are few things that can compare to the satisfaction of creating something. You can use programming to build your own game or maybe prank somebody! I tried that once - every time a friend clicked on the browser icon on my PC, it would make a loud farting noise! Don’t believe me yet? Over 80% of respondents to our most recent Skill-Up survey said that they programmed for fun, outside of work. #bonusreason! What’s to lose? I mean, seriously what can you lose? You’re going to be learning something completely new and will be probably much better at solving problems at home or your workplace. If you’re thinking you won’t find time to learn, think again. I’m sure all of us can make time, at least an hour a day to do something productive, if we commit to it. And you can always consider this your “me time”. Okay, so now you have your 5+1 reasons to learn to program. You’ve had some quality time to think about it and you’re ready to start learning. But you have some questions like where to start? Do you need to take a course or a college degree? Will it cost much? How long will it take to learn programming? The list is never ending. I’m going to put up some FAQs that most people ask me before they intend to start learning how to code. So here it goes… FAQs Where to start? Honestly speaking, you can start in the confines of your home! You just need a computer, an internet connection and the will to learn, if you want to get started with programming. You can begin by understanding what programming is a bit more, selecting a programming language, and then diving right into coding with the help of some material like the book, Introduction to Programming. What language do I pick? Every language can pretty much do what others can, but there are certain languages that have been built to solve a particular problem. Like for example, JavaScript, HTML and CSS are mainly used for building websites. Python is quite simple to learn and can be used to do a variety of things, most notably working with data. On the other hand, C# can be used to develop some cool games, while also being a great language to build websites and other applications. Think about what you want to do and then choose a language accordingly. I would suggest you choose between Python and JavaScript to start off. Do you need to take a course or a college degree? Not really, unless you plan on making it your full time career or becoming a software engineer or something like that. I’ve known some of the top professionals who haven’t earned a degree and still are at the position where they are. Mark Zuckerberg for example, dropped out of Harvard to start Facebook (he recently received an honorary degree in 2017, though). Programming is about learning to solve problems and in most cases, you don’t need a degree to prove that you’re great at solving problems. You can take an online course or buy a book to start learning. Sometimes, just looking at code often can teach you a lot too. Take HTML and CSS for example. If you like how a website looks, you could just checkout its source code to understand why it is the way it. Do this for a few sites and you you grasp the basics of what the HTML/CSS code do and how to write or alter simple code snippets. Will it cost much? You can learn a lot freely if you have a lot of time and patience at hand; sorting out the good from the bad. There are plenty of resources out there from Q&A sites like stackoverflow to youtube with its vast collection of videos. If you are like most people with a day job, you are better off spending a little to learn. There are several reasonably priced videos and courses from Packt, that will help you get started with computer programming. Alternatively, you can purchase a book or two for under $100. Trust me, once you become good at programming, you’ll be earning way more than you invested! How long will it take to learn programming? I can’t really answer that for certain. I took about 4 months to learn Python, while a friend of mine could code small programs within a couple of weeks. It all depends on the language you choose to learn, the amount of time you invest and how committed you are to learning something new. What jobs can I get? You may be quite happy in your current job as a non-programmer who now knows to code. But in case, you’re wondering about job prospects in programming, here is the rundown. As a programmer, you have a variety of jobs to choose from, depending on your area of interest. You could be a web developer, or a game developer, or you could also be building desktop applications like a notepad or word processor. There are a huge number of jobs available for those who can work with a lot of data as well, while there are a growing number of jobs for professionals who can manage thousands of computers working together - their maintenance, security, etc. Okay, so you have enough information to start your adventures into learning programming! You might hear people talk a lot about professionals losing jobs due to automation. Don’t let something like that be the reason behind why you want to learn how to program. Computer Science and programming has become more ingrained in school education, and our little ones are being coached to be industry ready. Always remember, programming is not everyone’s cup of tea and you shouldn’t do it just because everyone else is. Do it if you’re really passionate about solving problems in a better way. You will never know if programming is really meant for you until you try it. So go forth and get your hands dirty with some code! What is the difference between functional and object oriented programming? The Top 7 Python programming books you need to read Top 5 programming languages for crunching Big Data effectively

Python needs no introduction. It’s one of the top rated and growing programming languages, mainly because of its simplicity and wide applicability to solve a range of problems. Developers like yourself, beginners and experts alike, are looking to skill themselves up with Python. So I thought I would put together a list of Python programming books that I think are the best for learning Python - whether you're a beginner or experienced Python developer. Books for beginning to learn Python Learning Python, by Fabrizio Romano What the book is about This book explores the essentials of programming, covering data structures while showing you how to manipulate them. It talks about control flows in a program and teaches you how to write clean and reusable code. It reveals different programming paradigms and shows you how to optimize performance as well as debug your code effectively. Close to 450 pages long, the content spans twelve well thought out chapters. You’ll find interesting content on Functions, Memory Management and GUI app development with PyQt. Why Learn from Fabrizio Fabrizio has been creating software for over a decade. He has a master's degree in computer science engineering from the University of Padova and is also a certified Scrum master. He has delivered talks at the last two editions of EuroPython and at Skillsmatter in London. The Approach Taken The book is very easy to follow, and takes an example driven approach. As you end the book, you will be able to build a website in Python. Whether you’re new to Python or programming on the whole, you’ll have no trouble at all in following the examples. Download Learning Python FOR FREE. Learning Python, by Mark Lutz What the book is about This is one of the top most books on Python. A true bestseller, the book is perfectly fit for both beginners to programming, as well as developers who already have experience working with another language. Over 1,500 pages long, and covering content over 41 chapters, the book is a true shelf-breaker! Although this might be a concern to some, the content is clear and easy to read, providing great examples wherever necessary. You’ll find in-depth content ranging from Python syntax, to Functions, Modules, OOP and more. Why Learn from Mark Mark is the author of several Python books and has been using Python since 1992. He is a world renowned Python trainer and has taught close to 260 virtual and on-site Python classes to roughly 4,000 students. The Approach Taken The book is a great read, complete with helpful illustrations, quizzes and exercises. It’s filled with examples and also covers some advanced language features that recently have become more common in modern Python. You can find the book here, on Amazon. Intermediate Python books Modern Python Cookbook, by Steven Lott What the book is about Modern Python Cookbook is a great book for those already well versed with Python programming. The book aims to help developers solve the most common problems that they’re faced with, during app development. Spanning 824 pages, the book is divided into 13 chapters that cover solutions to problems related to data structures, OOP, functional programming, as well as statistical programming. Why Learn from Steven Steven has over 4 decades of programming experience, over a decade of which has been with Python. He has written several books on Python and has created some tutorial videos as well. Steven’s writing style is one to envy, as he manages to grab the attention of the readers while also imparting vast knowledge through his books. He’s also a very enthusiastic speaker, especially when it comes to sharing his knowledge. The Approach Taken The book takes a recipe based approach; presenting some of the most common, as well as uncommon problems Python developers face, and following them up with a quick and helpful solution. The book describes not just the how and the what, but the why of things. It will leave you able to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation. Find Modern Python Cookbook on the Packt store. Python Crash Course, by Eric Matthes What the book is about This one is a quick paced introduction to Python and assumes that you have knowledge of some other programming language. This is actually somewhere in between Beginner and Intermediate, but I've placed it under Intermediate because of its fast-paced, no-fluff-just-stuff approach. It will be difficult to follow if you’re completely new to programming. The book is 560 pages long and is covered over 20 chapters. It covers topics ranging from the Python libraries like NumPy and matplotlib, to building 2D games and even working with data and visualisations. All in all, it’s a complete package! Why Learn from Eric Eric is a high school math and science teacher. He has over a decade’s worth of programming experience and is a teaching enthusiast, always willing to share his knowledge. He also teaches an ‘Introduction to Programming’ class every fall. The Approach Taken The book has a great selection of projects that caters to a wide range of audience who’re planning to use Python to solve their programming problems. It thoughtfully covers both Python 2 and 3. You can find the book here on Amazon. Fluent Python, by Luciano Ramalho What the book is about The book is an intermediate guide that assumes you have already dipped your feet into the snake pit. It takes you through Python’s core language features and libraries, showing you how to make your code shorter, faster, and more readable at the same time. The book flows over almost 800 pages, with 21 chapters. You’ll indulge yourself in topics on the likes of Functions as well as objects, metaprogramming, etc. Why Learn from Luciano Luciano Ramalho is a member of the Python Software Foundation and co-founder of Garoa Hacker Clube, the first hackerspace in Brazil. He has been working with Python since 1998. He has taught Python web development in the Brazilian media, banking and government sectors and also speaks at PyCon US, OSCON, PythonBrazil and FISL. The Approach Taken The book is mainly based on the language features that are either unique to Python or not found in many other popular languages. It covers the core language and some of its libraries. It has a very comprehensive approach and touches on nearly every point of the language that is pythonic, describing not just the how and the what, but the why. You can find the book here, on Amazon. Advanced Python books The Hitchhiker's Guide to Python, by Kenneth Reitz & Tanya Schlusser What the book is about This isn’t a book that teaches Python. Rather, it’s a book that shows experienced developers where, when and how to use Python to solve problems. The book contains a list of best practices and how to apply these practices in real-world python projects. It focuses on giving great advice about writing good python code. It is spread over 11 chapters and 338 pages. You’ll find interesting topics like choosing an IDE, how to manage code, etc. Why Learn from Kenneth and Tanya Kenneth Reitz is a member of the Python Software Foundation. Until recently, he was the product owner of Python at Heroku. He is a known speaker at several conferences. Tanya is an independent consultant who has over two decades of experience in half a dozen languages. She is an active member of the Chicago Python User’s Group, Chicago’s PyLadies, and has also delivered data science training to students and industry analysts. The Approach Taken The book is highly opinionated and talks about what the best tools and techniques are to build Python apps. It is a book about best practices and covers how to write and ship high quality code, and is very insightful. The book also covers python libraries/frameworks that are focused on capabilities such as data persistence, data manipulation, web, CLI, and performance. You can get the book here on Amazon. Secret Recipes of the Python Ninja, by Cody Jackson What the book is about Now this is a one-of-a-kind book. Again, this one is not going to teach you about Python Programming, rather it will show you tips and tricks that you might not have known you could do with Python. In close to 400 pages, the book unearth secrets related to the implementation of the standard library, by looking at how modules actually work. You’ll find interesting topics on the likes of the CPython interpreter, which is a treasure trove of secret hacks that not many programmers are aware of, the PyPy project, as well as explore the PEPs of the latest versions to discover some interesting hacks. Why Learn from Cody Cody Jackson is a military veteran and the founder of Socius Consulting, an IT and business management consulting company. He has been involved in the tech industry since 1994. He is a self-taught Python programmer and also the author of the book series Learning to Program Using Python. He’s always bubbling with ideas and ways about improving the way he codes and has brilliantly delivered content through this book. The Approach Taken Now this one is highly opinionated too - the idea is to learn the skills from a Python Ninja. The book takes a recipe based approach, putting a problem before you and then showing you how you can wield Python to solve it. Whether you’re new to Python or are an expert, you’re sure to find something interesting in the book. The recipes are easy to follow and waste no time on lengthy explanations. You can find the book here on Amazon and here on the Packt website. So there you have it. Those were my top 7 books on Python Programming. There are loads of books available on Amazon, and quite a few from Packt that you can check out, but the above are a list of those that are a must-have for anyone who’s developing in Python. Read Next What are data professionals planning to learn this year? Python, deep learning, yes. But also… Python web development: Django vs Flask in 2018 Why functional programming in Python matters: Interview with best selling author, Steven Lott What the Python Software Foundation & Jetbrains 2017 Python Developer Survey had to reveal

Developers are always on the verge of learning something new, which can add on to their skill and their experience. Organizations such as Red Hat, Microsoft, Oracle, and many more roll out certain courses and certifications for developers and other individuals. 2018 has brought in some exciting areas for security and system experts to explore. Our annual Skill Up survey highlighted few of the technologies that security and system specialists are planning to learn in this year. Docker emerged to be at the top with professionals wanting to learn more about it and its implementations in building up a software with the ‘everything at one place’ concept. The survey also highlighted specialists being interested in learning RedHat’s OpenStack, Microsoft Azure, and AWS technologies. OpenStack being a cloud OS keeps a check on large pools of compute, storage, and networking resources within any datacenter, all through a web interface. It provides users with a much modular architecture to build their own cloud platforms without restrictions faced in the traditional cloud infrastructure. OpenStack also offers a Red Hat® Certified System Administrator course using which one can secure private clouds on OpenStack. You can check out our book on OpenStack Essentials to get started. The survey also highlights that system specialists are interested in learning Microsoft Azure. The primary reason for their choice is it offers a varied range of options to protect one’s applications and the data. It offers a seamless experience for developers who want to build, deploy, and maintain applications on the cloud. It also supports compliance efforts and provides a cost-effective security for individuals and organizations. AWS also offers out-of-the-box features with its products such as Amazon EC2, Amazon S3, AWS Lambda, and many more. Read about why AWS is a preferred cloud provider in our article, Why AWS is the preferred cloud platform for developers working with big data? In response to another question in the same survey, developers expressed their interest in learning security. With a lot of information being hosted over the web, organizations fear that their valuable data might be attacked by hackers and can be used illegally. Read also: The 10 most common types of DoS attacks you need to know Top 5 penetration testing tools for ethical hackers Developers are also keen on learning about security automation that can aid them in performing vulnerability scans without any human errors and also decreases their time to resolution. Security automation further optimizes ROI of their security investments. Learn security automation using one of the popular tools Ansible with our book, Security Automation with Ansible 2. So here are some of the technologies that security and system specialists are planning to learn. This analysis was taken from Packt Skill Up Survey 2018. Do let us know your thoughts in the comments below. The entire survey report can be found on the Packt store. IoT Forensics: Security in an always connected world where things talk Top 5 cybersecurity assessment tools for networking professionals Pentest tool in focus: Metasploit