We've compiled the latest set of "most read" articles from the Data Science Weekly Newsletter. This is what is most popular thus far in 2015 - a mix of interesting applications of data science, advice on how best to get into the field, and unique explanations of some of the core concepts / techniques…

- Three Things About Data Science You Won't Find In the Books

In case you haven’t heard yet, Data Science is all the craze. Courses, posts, and schools are springing up everywhere. However, every time I take a look at one of those offerings, I see that a lot of emphasis is put on specific learning algorithms. Of course, understanding how logistic regression or deep learning works is cool, but once you start working with data, you find out that there are other things equally important, or maybe even more... - What PhDs do wrong (and right!) when applying for Data Science jobs

I've been doing lots of interviewing folks transitioning from academia into industry data science jobs and collected some reflections on what we've learned and how we advise candidates to approach the problem... - Mathematicians have finally figured out how to tell correlation from causation

Untangling cause and effect can be devilishly difficult... - How to Choose Between Learning Python or R First

If you’re interested in a career in data, and you’re familiar with the set of skills you’ll need to master, you know that Python and R are two of the most popular languages for data analysis. If you’re not exactly sure which to start learning first, you’re reading the right article... - Automating the Data Scientists

Software that can discover patterns in data and write a report on its findings could make it easier for companies to analyze it... - Where It All Started: How I Became a Data Scientist

I thought I’d ease into this more technical subject by answering a question that I get asked many times: “how did you end up as a social media data scientist from your biophysics PhD background?”... - Advice to graduate students interviewing for industry positions

A couple of weeks ago I saw a post in a LinkedIn group which went something like this: "I've just received a Ph.D. in physics and I know python and R. I've been applying for data scientist roles. However, I'm not getting much traction. Do you think that I need to learn a BI tool such as Tableau?"...I want to take the opportunity to give a few pieces of advice from a hiring manager's perspective... - Markov Chain Monte Carlo Without all the Bullshit

I have a little secret: I don’t like the terminology, notation, and style of writing in statistics. I find it unnecessarily complicated. So to counter, here’s my own explanation of Markov Chain Monte Carlo... - Becoming a Full-Stack Statistician in 6 Easy Steps

It's been a fun challenge to go from being an academic statistician to a practicing data scientist deep in the trenches of the software industry. Here are a few essential skills that I have had to pick up along the way. Remember, to become a full-stack statistician, try to be as fast as possible in each of the following categories... - The Three Kinds Of Data Science Project Exams That Show Up In A Data Science Interview

You've got an interview and you've found out an exam will be given to you. All that you've been told is that, at some point of your choosing in the next few weeks, you'll be given 4 hours to ingest and operate on a sizable data set using your programming language of choice. This is scary... - The Simple, Elegant Algorithm That Makes Google Maps Possible

Algorithms are a science of cleverness. A natural manifestation of logical reasoning—mathematical induction, in particular—a good algorithm is like a fleeting, damning snapshot into the very soul of a problem. A jungle of properties and relationships becomes a simple recurrence relation, a single-line recursive step producing boundless chaos and complexity. And to see through deep complexity, it takes cleverness... - Software development skills for data scientists

Data scientists often come from diverse backgrounds and frequently don't have much, if any, in the way of formal training in computer science or software development. That being said, most data scientists at some point will find themselves in discussions with software engineers because of some code that already is or will be touching production code... - Data Mining Reveals the Surprising Factors Behind Successful Movie

The secret to making profitable movies will amaze you. (Spoiler: it’s not hiring top box office stars.)… - A Neural Network in 11 lines of Python

A bare bones neural network implementation to describe the inner workings of backpropagation... - The State of Artificial Intelligence in Six Visuals

We cover many emerging markets in the startup ecosystem. Previously, we published posts that summarized Financial Technology, Internet of Things, Bitcoin, and MarTech in six visuals. This week, we do the same with Artificial Intelligence (AI). At this time, we are tracking 855 AI companies across 13 categories, with a combined funding amount of $8.75billion...

If you're interested in reading the rest of our "most read" articles series (i.e., from other quarters), you can check them out here: