This is an updated version of the original article. I want to thank @joseanpg for the corrections and the idea regarding TextEncoder. Usually, my articles are mostly too long, filled with too many screenshots and philosophy. But this time I’ll try to remain concise and, hopefully, more precise than usual. I’ll refer to Jake Archibald’s article on Web Streams and his prediction that they’ll become the dominant web technology of the Year 2016 (in combination with Service Workers, of course). The article is filled with lots and lots of nice code examples you can basically copy & paste into your browser (some of […]
Coding
47 posts
It’s been a while since I wrote my last article and. Actually, I planned to write the second part of my Scala Crash Course but in the mean time I adopted an orphaned (abandoned?) project called purescript-ractive that’s related to my favorite UI-Library, RactiveJS, and is written in a little, amazing language called PureScript. As many of you already know there are so many different languages that compile to JavaScript. There’s even a COBOL transpiler for JS! Therefore I’ll surely not waste your time discussing the (dis)advantages of Transpiling-to-JS vs. Writing-in-JS. Instead, I’ll try to describe the advantages of PureScript by explaining a small […]
In this article we’ll explore Microsoft’s Azure Machine Learning environment and how to combine Cloud technologies with Python and Jupyter. As you may know I’ve been extensively using them throughout this article series so I have a strong opinion on how a Data Science-friendly environment should look like. Of course, there’s nothing against other coding environments or languages, for example R, so your opinion may greatly differ from mine and this is fine. Also AzureML offers a very good R-support! So, feel free to adapt everything from this article to your needs. And before we begin, a few words about how I came […]
Julia is what I’d like Python to be: dynamic but fast like C, supporting strong typing without being dogmatic (in both directions: static vs. dynamic), with a powerful REPL and many modules written in the same language (so I don’t have to switch to C). Julia is still a new language and I suppose not many of us use it currently in production. And yes, Julia is an ‘academic’ language, with a strong emphasis on technical/scientific computing, but honestly, would you rather like to run your business on an ‘anti-scientific’ / ‘anti-technical’ language? Yes, I know, it sounds very polemic because there […]
Sometimes, the hardest part in writing is completing the very first sentence. I began to write the “Loser’s articles” because I wanted to learn a few bits on Data Science, Machine Learning, Spark, Flink etc., but as the time passed by the whole degenerated into a really chaotic mess. This may be a “creative” chaos but still it’s a way too messy to make any sense to me. I’ve got a few positive comments and also a lot of nice tweets, but quality is not a question of comments or individual twitter-frequency. Do these texts properly describe “Data Science”, or at […]
It’s been a while since I wrote my last article. A Big-Data Sorry to my “massive” audience. Actually, I was planning to write a follow-up to the last article on Machine Learning but could not find enough time to complete it. Also, I’ll soon give a presentation in a Meetup (in Germany). A classical example on what happens when you have to complete several tasks at the same time. In the end all of them will fail. But I’ll try to compensate it with yet another task: by writing an article about the brand-new Apache Flink v0.10.0 and its DataStream API. 😀 As always, […]
Douglas Crockford once said that people who finally understand Monads immediately lose the capability to explain them to others. Well, the few readers of this chaotic blog are lucky: neither I understand them nor am able to explain them anyway. However, I can say in advance that a Monad in Scala is something that implements two methods: map and flatMap. Haskell coders (luckily, they’re certainly not reading this blog) now would say: No, there’s no flatMap but only bind written as >>=. Yes, I know but anyway, we’ll stick with flatMap. And to make this article somewhat cooler we’ll use a […]
It’s been a while since I’ve written an article on Data Science for Losers. A big Sorry to my readers. But I don’t think that many people are reading this blog. Now let’s continue our journey with the next step: Machine Learning. As always the examples will be written in Python and the Jupyter Notebook can be found here. The ML library I’m using is the well-known scikit-learn. What’s Machine Learning From my non-scientist perspective I’d define ML as a subset of the Artificial Intelligence research which develops self-learning (or self-improving?) algorithms that try to gain knowledge from data and make predictions […]
In the last article from the Data Science for Losers series I’ve used a few mini examples in Scala to show how Apache Spark works. Granted, I’m still not sure if this was a “good” idea at all but regarding the fact that the whole series degenerated into something really chaotic a few harmless lines in Scala wouldn’t make it worse anyway. However, the much lower retweet-rate of the last article made it clear that the jump from my bad Python code to my even worse Scala code wasn’t very well accepted. Well, I think it’s time for a crash course in Scala […]
I’ve already mentioned Apache Spark and my irrational plan to integrate it somehow with this series but unfortunately the previous articles were a complete mess so it has had to be postponed. And now, finally, this blog entry is completely dedicated to Apache Spark with examples in Scala and Python. The notebook for this article can be found here. Apache Spark Definition By its own definition Spark is a fast, general engine for large-scale data processing. Well, someone would say: but we already have Hadoop, so why should we use Spark? Such a question I’d answer with a remark that Hadoop is EJB reinvented and […]