I wanted to do some analysis on source code, and I needed a dataset where code snippets are labeled with the programming language they are in. I scraped this data from codeforces.com, which is a website for holding programming contests. In this post, I share this data.
tl;dr Scroll down to get the links.
The book “Test-Driven Machine Learning” by Justin Bozonier, published by Packt Publishing, is in print now. I was a technical reviewer of this book, and in this post you will learn some details about it. The book is available on the publisher’s website as well as on Safari Books Library.
Our IT4BI Master studies finished, and the next logical step after graduation is finding a job. I was interested in Data Science jobs and this post is a summary of my interview experience and preparation.
The term “Data Science” is not yet well establish, so interviews for Data Science jobs might include a very broad range of questions, depending on the interpretation of the term by a particular company. In this post I attempt to organize Data Science interview questions in some usable form, but it might also be biased by how I see Data Science myself. I hope you also can find it useful.
In the past, I was a Software Developer, and my primary programming language was Java. I also quite often interviewed people and also sometimes was an interviewee. In this post, I would like to share typical questions that you might expect at a job interview for a Java Developer position.
This is a first blog post in the series “Fundamental Theorem of Linear Algebra”, where we are working through Gilbert Strang’s paper “The fundamental theorem of linear algebra” published by American Mathematical Monthly in 1993.
In this post, we will go through the first two parts of the Fundamental Theorem: the dimensionality and the orthogonality of the Fundamental Subspaces.
This is a series of articles devoted to Gilbert Strang’s Paper “The fundamental theorem of linear algebra” published by American Mathematical Monthly in 1993.
Last week the newest Apache Mahout 0.10 was released. One of the new features it has is a new math environment called “Samsara”, or Mahout Scala/Spark Bindings.
Samsara is a Linear Algebra library for Mahout. It’s written in Scala, which makes it possible to use operator overloading and it features nice R-like or Matlab-like syntax for basic Linear Algebra operations. For example, matrix multiplication is just X %*% Y. What is more, these operations can be distributed and run by an executing environment - currently by Apache Spark.
In this article we will see how to quickly set up a basic skeleton project and then we’ll try to do some very simple analysis on a 200 MB dataset.
In this blog post we are going to implement a Naive Bayes classifier in Apache Flink. We are going to use it for text classification by applying it to the 20 Newsgroup dataset. To understand what is going on, you should be familiar with Java and know what MapReduce is. If you have seen and understood a word count example in any system, you're good to go. If you haven't heard of MapReduce or haven't seen the word count, you may first have a look at our introductory post "Hadoop and MapReduce".
In this article we will briefly discuss the computation paradigm MapReduce, and Apache Hadoop as one of its implementations. We won't get into much details, and we even won't implement the Word Count on Hadoop, but it should give some foundation for the future articles about tools for scalable data processing.
