In some work categorization frameworks, jobs are placed into quadrants depending on whether they are routine or nonroutine, and cognitive or manual. Data science can be a rewarding and stimulating career path due to its nature as a nonroutine cognitive discipline. However, the way that demands and problems change throughout the course of projects can lead to some uncertainty.

As I approach the two year anniversary of my first non-internship position as a data scientist, I think it would be interesting to record and share my experience so far.

In particular, I’ve identified four typical “phases” of projects I’ve worked…

The purrr package is one of the mainstays of the Tidyverse, right up there with dplyr and the pipe operator in terms of usefulness and universality. One of its main purposes is to align the intent of a loop with its syntax. Instead of involving a bunch of tedious boilerplate when writing a for-loop — which tells the reader little about what the loop does — a purrr map can be read almost like plain English, and fits neatly into groups of piped-together operations.

But this is not all purrr has to offer. For the longest time, I only used…

Are neural networks and deep learning the keys that will finally unlock the path to artificial general intelligence, or are we on the verge of another AI Winter?

If you’re an entrepreneur, then the answer is: it doesn’t matter. Your funding doesn’t depend on grant providers being excited about your research agenda producing a talking robot. It depends on providing real value to real people.

The truth is, even though developments in AI so far have yet to produce machines that are intelligent in a way comparable to our own intelligence, the technologies that have been developed along the way…

Polar Bar Charts can be great ways to display categorical information if you want to draw more attention to the categories with higher values. They were used to great effect in the Wall Street Journal’s article on Amazon’s HQ2 Shortlist. But even with the powerful visualization tools at our fingertips, producing charts like that takes a bit of legwork.

I’m here today to provide you with the pieces of geometrical drudgery needed to turn Plotly’s polar bar chart into something visually acceptable. …

It’s a tragedy whenever someone gets that degree or lands that job, and puts their days of earnest learning behind them. The truth is, we come away from our formal educations with holes in our abilities. Often, these gaps can be filled in a way that enhances how we perceive the world around us, with only a small investment of time and attention.

I believe that one class of learning gaps comes from the mismatch between the mechanical difficulty and the conceptual difficulty of the things we learn in school. …

It seems that we’ve become so obsessed with building neural networks in 10 lines of code that we’ve forgotten to build neural networks in reusable lines of code.

Neural networks are useful machine learning models, and they’re maturing as a technology. As a consequence of this, many things that used to be done by hand, like determining the dimensions of the weight matrices or implementing backpropagation, are now taken care of by software frameworks. Of course, many of us are too busy demonstrating that we know how to transpose a matrix in NumPy to notice.

Since building neural networks is…

When it comes to updating beliefs and making decisions under uncertainty, Bayes’ theorem is just about the best tool available. And yet it is so often relegated to academic textbooks and machine learning applications when it should be bringing us value in our daily lives.

This article is for those of us who are familiar with Bayes’ theorem, but might have trouble remembering which terms go where and don’t immediately think of it when reading headlines like “red wine reduces dementia risk.”

First, I’ll review Bayes’ theorem, talk about why I think it is hard to remember when it really…

Math is beautiful, and the software we have at our fingertips represents an invaluable way to explore that beauty. Never before have we been able to visualize and experiment with mathematical objects in such an accessible way.

Today, I’d like to break down the steps required to plot a function of two variables using Python.

Along the way, we’ll learn about a few NumPy procedures that’ll have you manipulating matrices like a wizard. I’ve broken the process down into three conceptual steps that’ll also help refresh the underlying math.

Finally, I put everything together into a function you can use…