How-To Tutorials - Uncategorized

For app and web developers, the world at the end of the decade is very different to the one that began it. Sure, change is inevitable, but the way the discipline(s) have evolved in just a matter of years (arguably the most significant changes came in the latter half of the decade) is a mark of how technologies, business needs, customer expectations, and harsh economic realities have conspired to shape and remold our notion of what software development actually looks like. Full-stack, cloud-native, DevOps (and maybe even ‘NoOps’): all these things have been shaping the way app and web developers work over the last ten years. And in 2019 it feels like that new world is beginning to settle into a specific pattern. Many of the trends and technologies that really defined 2019 are, in truth, trends that have been nascent and emerging for a number of years. Cloud and microservices When cloud first emerged - at some point much earlier this decade - it was largely just about resource efficiency. The idea was to ditch your on premises servers and move instead to a model whereby you rent server space from big vendors. Okay, perhaps that’s a somewhat crude summation; but it’s nevertheless the case that cloud was primarily a field dealt with by administrators and IT professionals, rather than developers. Today, of course, cloud is having a very real impact on the way developers work, giving a degree of agility and flexibility in how software is deployed and managed. With cloud partnering nicely with microservices - which allow developers to break down an application into constituent parts - it’s easy to see how these two trends are getting many app and web developers excited. They shorten the development lifecycle and allow developers to get closer to their code as it runs in production. Learn cloud development - explore Packt's range of cloud bundles. Pick up 5 for $25 throughout our $5 campaign. An essential resource for microservices development: Microservices Development Cookbook. $5 for the rest of December and into January. Go and Rust The growth of Go and Rust throughout 2019 (okay, and a bit before that too) is directly related to the increasing importance of cloud and microservices in software development. Although JavaScript has been taken beyond the browser, it isn’t the best programming language for building high performance applications; that’s where the likes of Go and Rust have been taking over a not insignificant slice of the collective developer imagination. Both languages share a similar history (as this article nicely details); at a fundamental level, moreover, both also aim to build on C++, but with accessibility and safety in mind (C++ has long had a reputation for being both complicated and sometimes vulnerable to bugs and security issues). Go is likely to continue to grow at a faster rate than Rust: it’s a lot easier to use, so for web and app developers with experience in Java or JavaScript, it’s a much gentler learning curve. But this isn’t to say that Rust won’t remain a fixture for developers. Consistently ranked the ‘most loved’ language in Stack Overflow surveys, as developers seek relentless improvements to performance alongside watertight reliability and security, Rust will remain an important language in a fast-changing development world. Search Packt's extensive selection of Go eBooks and videos - $5 throughout December and into the new year. Visit the Packt store. Learn Rust with Rust Programming Cookbook. WebAssembly It’s impossible to talk about web and application development without mentioning WebAssembly. Arguably the full implications of WebAssembly are yet to be realised (indeed, at ReactConf 2019, Richard Feldman suggested that it was unlikely to initiate a wholesale transformation of the web - that, he believes, will take a few more years), but 2019 has been a year when it has properly started to make many developers sit up and take notice. But why is WebAssembly so exciting? Essentially, it allows you to run code on the web using multiple languages at a speed that’s almost akin to native applications. Indeed, WebAssembly is making languages like Rust more attractive to web developers. If WebAssembly is a bridge between Rust and JavaScript, Rust immediately becomes more attractive to developers who previously would have paid very little attention to it. If 2019 was the year more developers decided to take note of WebAssembly, 2020 will be the year when we start to see increased adoption. Learn WebAssembly is $5 throughout this year's $5 campaign. Get it here. State management: Redux, Flux, Vuex… For many years, MVC (Model-View-Controller) was the dominant model for managing application state. However, as applications have grown in complexity, it has become more and more difficult for us to establish a ‘single source of truth’ inside our apps.That can impact performance and can also make them harder to maintain on the development side. To tackle this, we’ve started to see a number of different patterns and frameworks emerging to help us manage application state. The growth of React has been instrumental here - as a very lightweight library it gives developers the freedom to manage application state however they choose - and it’s worth noting that Flux architecture was developed by Facebook to complement the library. Watch: Why do React developers love Redux for state management? https://www.youtube.com/watch?v=7YzgZA_hA48&feature=emb_title Following Flux we’ve also had Redux and Vuex - all of them, each with subtly different approaches, have become an essential aspect of modern web and app development. And while they might not have first emerged in 2019, it feels as though the state management discourse has hit the heights that it previously has not. If you haven’t yet had time to dive into this topic, it's well worth making sure you commit to it in 2020. Learning React with Redux and Flux [Video] is $5 - purchase it here on the Packt store. Learn Vuex with Vuex Quick Start Guide. Functional programming Functional programming is on the rise. This doesn’t however mean that purely functional languages like Haskell and Lisp are dominating the programming language landscape - in fact, it’s been said that JavaScript is now the language used for functional programming (even though it isn’t a functional language). Functional programming is popular because it can help minimize complexity and make it easier to test and reuse code. When you’re dealing with a dense codebase that grows and grows as your application scales, this is immensely valuable. It’s also worth placing functional programming in the context of managing application state. Insofar as functional programming allows you to be specific in determining how different parts of a component should interact with one another - the function is a theoretical abstraction that makes it easier to get to grips with managing the state of a complex and dynamic application. Get to grips with functional programming and discover how to leverage its power. Read Mastering Functional Programming. The new JavaScript framework boom I’m not sure whether JavaScript fatigue is over. On the one hand the space has coalesced around a handful of core tools and frameworks - React, GraphQL, Node.js, among a couple of others - but on the other hand, the last year (and a bit) have been characterized by many other small projects developed to support these core tools. So, while it’s maybe a little bit easier to parse the JavaScript ecosystem at pretty high level of abstraction than it was in the past, at a deeper level you have a range of tools that are designed for very specific purposes or to be used alongside some of those frameworks and tools just mentioned. Tools ranging from Koa.js (for Node), to Polymer, Nuxt, Next, Gatsby, Hugo, Vuelidate (to name just a random assortment) are all vying for developer mindshare. You could say that many of these tools are ‘second-order’ frameworks and libraries - they don’t fundamentally change the way you think about development but instead make it easier to do specific things. It’s for this reason that I’m reluctant to suggest that JavaScript fatigue will return to its former glory - this new JavaScript framework boom is very much geared towards productivity and immediate gains rather than overhauling the way you build applications because of some principled belief in the ‘right’ or ‘best’ way to do things. Learn Nuxt: pick up Build a News Feed with Nuxt 2 and Firestore [Video] for $5 before the end of the year. Get to grips with Next.js with Next.js Quick Start Guide. Learn Koa with Hands-on Server-Side Development with Koa.js [Video] Learn Gatsby with GatsbyJS: Build a PWA Blog with GraphQL, React, and WordPress [Video] GraphQL Much of this decade has been dominated by REST when it comes to APIs. But just as the so called ‘API economy’ has gone into overdrive, GraphQL has come on the scene. Adoption has been rapid, with many developers turning to it because it allows them to handle more complex and sophisticated requests at scale without writing long and confusing lines of code. This isn’t to say, of course, that GraphQL has all but killed REST. Instead, it’s more the case that GraphQL has been found to be a better tool for managing APIs in specific domains than REST. If you’re dealing with APIs that are complex in terms of the number of entities and their relationships between one another, then GraphQL can prove immensely useful. Find out how to put GraphQL to use. Pick up GraphQL Projects for $5 for the rest of December and into January. React Hooks (and Vue Hooks) Launched with React 16.8, React Hooks “let you use state and other React features without writing a class” (that’s from the project’s site). That’s a good thing because building components with a class can sometimes be somewhat inelegant. For a better explanation of the ‘point’ of React Hooks you could do a lot worse than this article. Vue Hooks is part of Vue 3.0 - this won’t be officially released until early next year. But the fact that both leading front end frameworks are taking similar approaches to improve the developer experience demonstrates that they’re responding to a need for more flexibility and control over large projects. That means 2019 has been the year that both tools have hit maturity in the web development space. Learn how React Hooks work with Packt's new React Hooks video. Conclusion The web and app development world is becoming difficult to parse. A few years ago discussion and debate really centered on frameworks; today it feels like there are many other elements to consider. Part of this is symptomatic of a slow DevOps revolution - the gap between build and production is smaller than it has ever been, and developers now have a significant degree of accountability and responsibility for things that were the preserve of different breeds of engineers and IT professionals. Perhaps that story is a bit of a simplification - however, it’s hard to dispute that the web and app developer skill set is incredibly diverse. That means there are an array of options and opportunities out there for those developers looking to push their careers forward, but it also means that they’ll need to do some serious decision making about what they want to do and how they want to do it.

At the second ChaosConf in San Francisco, Gremlin CEO Kolton Andrus revealed the company's latest step in its war against downtime: 'Scenarios.' Scenarios makes it easy for engineering teams to simulate a common issues that lead to downtime. It's a natural and necessary progression for Gremlin that is seeing even the most forward thinking teams struggling to figure out how to implement chaos engineering in a way that's meaningful to their specific use case. "Since we released Gremlin Free back in February thousands of customers have signed up to get started with chaos engineering," said Andrus. "But many organisations are still struggling to decide which experiments to run in order to avoid downtime and outages." Scenarios, then, is a useful way into chaos engineering for teams that are reticent about taking their first steps. As Andrus notes, it makes it possible to inject failure "with a couple of clicks." What failure scenarios does Scenarios let engineering teams simulate? Scenarios lets Gremlin users simulate common issues that can cause outages. These include: Traffic spikes (think Black Friday site failures) Network failures Region evacuation This provides a great starting point for anyone that wants to stress test their software. Indeed, it's inevitable that these issues will arise at some point so taking advance steps to understand what the consequences could be will minimise their impact - and their likelihood. Why chaos engineering? Over the last couple of years plenty of people have been attempting to answer why chaos engineering? But in truth the reasons are clear: software - indeed, the internet as we know it - is becoming increasingly complex, a mesh of interdependent services and platforms. At the same time, the software being developed today is more critical than ever. For eCommerce sites downtime means money, but for those in IoT and embedded systems world (like self-driving cars, for example), it's sometimes a matter of life and death. This makes Gremlin's Scenarios an incredibly exciting an important prospect - it should end the speculation and debate about whether we should be doing chaos engineering, and instead help the world to simply start doing it. At ChaosConf Andrus said that Gremlin's mission is to build a more reliable internet. We should all hope they can deliver.

So, you want to be a data scientist. It’s a smart move: it’s a job that’s in high demand, can command a healthy salary, and can also be richly rewarding and engaging. But to get the job, you’re going to have to pass a data science interview - something that’s notoriously tough. One of the reasons for this is that data science is a field that is incredibly diverse. I mean that in two different ways: on the one hand it’s a role that demands a variety of different skills (being a good data scientist is about much more than just being good at math). But it's also diverse in the sense that data science will be done differently at every company. That means that every data science interview is going to be different. If you specialize too much in one area, you might well be severely limiting your opportunities. There are plenty of articles out there that pretend to have all the answers to your next data science interview. And while these can be useful, they also treat job interviews like they’re just exams you need to pass. They’re not - you need to have a wide range of knowledge, but you also need to present yourself as a curious and critical thinker, and someone who is very good at communicating. You won’t get a data science by knowing all the answers. But you might get it by asking the right questions and talking in the right way. So, with all that in mind, here are what you need to do to ace your data science interview. Know the basics of data science This is obvious but it’s impossible to overstate. If you don’t know the basics, there’s no way you’ll get the job - indeed, it’s probably better for your sake that you don’t get it! But what are these basics? Basic data science interview questions "What is data science?" This seems straightforward, but proving you’ve done some thinking about what the role actually involves demonstrates that you’re thoughtful and self-aware - a sign of any good employee. "What’s the difference between supervised and unsupervised learning?" Again, this is straightforward, but it will give the interviewer confidence that you understand the basics of machine learning algorithms. "What is the bias and variance tradeoff? What is overfitting and underfitting?" Being able to explain these concepts in a clear and concise manner demonstrates your clarity of thought. It also shows that you have a strong awareness of the challenges of using machine learning and statistical systems. If you’re applying for a job as a data scientist you’ll probably already know the answers to all of these. Just make sure you have a clear answer and that you can explain each in a concise manner. Know your algorithms Knowing your algorithms is a really important part of any data science interview. However, it’s important to not get hung up on the details. Trying to learn everything you know about every algorithm you know isn’t only impossible, it’s also not going to get you the job. What’s important instead is demonstrating that you understand the differences between algorithms, and when to use one over another. Data science interview questions about algorithms you might be asked "When would you use a supervised machine learning algorithm?" "Can you name some supervised machine learning algorithms and the differences between them?" (supervised machine learning algorithms include Support Vector Machines, Naive Bayes, K-nearest Neighbor Algorithm, Regression, Decision Trees) "When would you use an unsupervised machine learning algorithm?" (unsupervised machine learning algorithms include K-Means, autoencoders, Generative Adversarial Networks, and Deep Belief Nets.) Name some unsupervised machine learning algorithms and how they’re different from one another. "What are classification algorithms?" There are others, but try to focus on these as core areas. Remember, it’s also important to always talk about your experience - that’s just as useful, if not even more useful than listing off the differences between different machine learning algorithms. Some of the questions you face in a data science interview might even be about how you use algorithms: "Tell me about the time you used an algorithm. Why did you decide to use it? Were there any other options?" "Tell me about a time you used an algorithm and it didn’t work how you expected it to. What did you do?" When talking about algorithms in a data science interview it’s useful to present them as tools for solving business problems. It can be tempting to talk about them as mathematical concepts, and although it’s good to show off your understanding, showing how algorithms help solve real-world business problems will be a big plus for your interviewer. Be confident talking about data sources and infrastructure challenges One of the biggest challenges for data scientists is dealing with incomplete or poor quality data. If that’s something you’ve faced - or even if it’s something you think you might face in the future - then make sure you talk about that. Data scientists aren’t always responsible for managing a data infrastructure (that will vary from company to company), but even if that isn’t in the job description, it’s likely that you’ll have to work with a data architect to make sure data is available and accurate to be able to carry our data science projects. This means that understanding topics like data streaming, data lakes and data warehouses is very important in a data science interview. Again, remember that it’s important that you don’t get stuck on the details. You don’t need to recite everything you know, but instead talk about your experience or how you might approach problems in different ways. Data science interview questions you might get asked about using different data sources "How do you work with data from different sources?" "How have you tackled dirty or unreliable data in the past?" Data science interview questions you might get asked about infrastructure "Talk me through a data infrastructure challenge you’ve faced in the past" "What’s the difference between a data lake and data warehouse? How would you approach each one differently?" Show that you have a robust understanding of data science tools You can’t get through a data science interview without demonstrating that you have knowledge and experience of data science tools. It’s likely that the job you’re applying for will mention a number of different skill requirements in the job description, so make sure you have a good knowledge of them all. Obviously, the best case scenario is that you know all the tools mentioned in the job description inside out - but this is unlikely. If you don’t know one - or more - make sure you understand what they’re for and how they work. The hiring manager probably won’t expect candidates to know everything, but they will expect them to be ready and willing to learn. If you can talk about a time you learned a new tool that will give the interviewer a lot of confidence that you’re someone that can pick up knowledge and skills quickly. Show you can evaluate different tools and programming languages Another element here is to be able to talk about the advantages and disadvantages of different tools. Why might you use R over Python? Which Python libraries should you use to solve a specific problem? And when should you just use Excel? Sometimes the interviewer might ask for your own personal preferences. Don’t be scared about giving your opinion - as long as you’ve got a considered explanation for why you hold the opinion that you do, you’re fine! Read next: Why is Python so good for AI and Machine Learning? 5 Python Experts Explain Data science interview questions about tools that you might be asked "What tools have you - or could you - use for data processing and cleaning? What are their benefits and disadvantages?" (These include tools such as Hadoop, Pentaho, Flink, Storm, Kafka.) "What tools do you think are best for data visualization and why?" (This includes tools like Tableau, PowerBI, D3.js, Infogram, Chartblocks - there are so many different products in this space that it’s important that you are able to talk about what you value most about data visualization tools.) "Do you prefer using Python or R? Are there times when you’d use one over another?" "Talk me through machine learning libraries. How do they compare to one another?" (This includes tools like TensorFlow, Keras, and PyTorch. If you don’t have any experience with them, make sure you’re aware of the differences, and talk about which you are most curious about learning.) Always focus on business goals and results This sounds obvious, but it’s so easy to forget. This is especially true if you’re a data geek that loves to talk about statistical models and machine learning. To combat this, make sure you’re very clear on how your experience was tied to business goals. Take some time to think about why you were doing what you were doing. What were you trying to find out? What metrics were you trying to drive? Interpersonal and communication skills Another element to this is talking about your interpersonal skills and your ability to work with a range of different stakeholders. Think carefully about how you worked alongside other teams, how you went about capturing requirements and building solutions for them. Think also about how you managed - or would manage - expectations. It’s well known that business leaders can expect data to be a silver bullet when it comes to results, so how do you make sure that people are realistic. Show off your data science portfolio A good way of showing your business acumen as a data scientist is to build a portfolio of work. Portfolios are typically viewed as something for creative professionals, but they’re becoming increasingly popular in the tech industry as competition for roles gets tougher. This post explains everything you need to build a great data science portfolio. Broadly, the most important thing is that it demonstrates how you have added value to an organization. This could be: Insights you’ve shared in reports with management Building customer-facing applications that rely on data Building internal dashboards and applications Bringing a portfolio to an interview can give you a solid foundation on which you can answer questions. But remember - you might be asked questions about your work, so make sure you have an answer prepared! Data science interview questions about business performance "Talk about a time you have worked across different teams." "How do you manage stakeholder expectations?" "What do you think are the most important elements in communicating data insights to management?" If you can talk fluently about how your work impacts business performance and how you worked alongside others in non-technical positions, you will give yourself a good chance of landing the job! Show that you understand ethical and privacy issues in data science This might seem like a superfluous point but given the events of recent years - like the Cambridge Analytica scandal - ethics has become a big topic of conversation. Employers will expect prospective data scientists to have an awareness of some of these problems and how you can go about mitigating them. To some extent, this is an extension of the previous point. Showing you are aware of ethical issues, such as privacy and discrimination, proves that you are fully engaged with the needs and risks a business might face. It also underlines that you are aware of the consequences and potential impact of data science activities on customers - what your work does in the real-world. Read next: Introducing Deon, a tool for data scientists to add an ethics checklist Data science interview questions about ethics and privacy "What are some of the ethical issues around machine learning and artificial intelligence?" "How can you mitigate any of these issues? What steps would you take?" "Has GDPR impacted the way you do data science?"  "What are some other privacy implications for data scientists?" "How do you understand explainability and interpretability in machine learning?" Ethics is a topic that’s easy to overlook but it’s essential for every data scientist. To get a good grasp of the issues it’s worth investigating more technical content on things like machine learning interpretability, as well as following news and commentary around emergent issues in artificial intelligence. Conclusion: Don’t treat a data science interview like an exam Data science is a complex and multi-faceted field. That can make data science interviews feel like a serious test of your knowledge - and it can be tempting to revise like you would for an exam. But, as we’ve seen, that’s foolish. To ace a data science interview you can’t just recite information and facts. You need to talk clearly and confidently about your experience and demonstrate your drive and curiosity. That doesn’t mean you shouldn’t make sure you know the basics. But rather than getting too hung up on definitions and statistical details, it’s a better use of your time to consider how you have performed your roles in the past, and what you might do in the future. A thoughtful, curious data scientist is immensely valuable. Show your interviewer that you are one.