Class Central Data Science Career Guide

Class Central’s Data Science Career Guide is a six-piece series that recommends the best MOOCs for launching yourself into the data science industry. The first five pieces recommend the best courses for several data science core competencies (programming, statistics, data analysis, data visualization, and machine learning). The final piece is a summary of those courses and the best MOOCs for other key topics such as data wrangling, databases, and even software engineering.

For each piece, I scour the online course landscape for every single offering for the subject in question, extract key bits of information from their syllabi and reviews, and compile their ratings. For this task, I turn to none other than the open source Class Central community and its database of thousands of course ratings and reviews.

Here are the parts of the series that have been published so far:

1. The Best Intro to Programming Courses for Data Science
2. The Best Statistics & Probability Courses for Data Science
3. The Best Intro to Data Science Courses

The 6 most desirable coding jobs (and the types of people drawn to each)

More than 15,000 people responded to Free Code Camp’s 2016 New Coder Survey, granting researchers (like me!) an unprecedented glimpse into how people are learning to code. The entire dataset was released on Kaggle.

6,503 new coders answered the question: “Which one of these roles are you most interested in?”

Here are the 6 most popular coding jobs and the (very different) types of people drawn to each.

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Ranking the Rankers: Introduction

View this entry at Blue Steam Hockey

"Ranking the Rankers" is a 7-piece series that quantifies past NHL draft rankings from various prognosticators based on player performance to date. View code

Your favourite hockey team just drafted a teenager. He, presumably, is good at hockey. How can you be sure? Because this set of draft rankings from [insert writer / scout / website here] says so.

This series attempts to determine which of the above sets of draft rankings is best at predicting future NHL success (and which one you should hope your team follows on draft day).

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Udacity Over University: Why I Chose Online Education

First day as a computer science student at the University of Toronto, one of the best computer science programs in the world. Packed lecture hall. Rock hard yellow seats, almost identical to the ones above. Class starts and the professor begins to speak, virtually uninterrupted for the entire hour. Class ends. I think to myself: this doesn’t feel right.

Two weeks later, I dropped out of the program and started creating my own curriculum using online resources like Udacity, edX, and Coursera. The decision was not difficult.

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Balks: An Illustrative & Quantitative History

1. How have balks trended throughout baseball history? Does the trend align with rule changes, rule enforcements, etc.?
2. The balk rule is designed to limit pitcher deception towards the baserunner. Did the balk rule changes and enforcements in the mid-to-late 1900s spark an increase in stolen base attempts?
3. Are there significantly more balks called per inning pitched in the regular season compared to the postseason? If yes, why might this be occurring?
4. Who is the all-time balk king? Who is the modern-day (post-2000) balk king? Who is the balk iron man (most innings pitched without a balk)?

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My Data Science Master's

I dropped out of a top computer science program to teach myself data science using online resources like Udacity, edX, and Coursera. The decision was not difficult. I could learn the content I wanted to faster, more efficiently, and for a fraction of the cost. I already had a university degree and, perhaps more importantly, I already had the university experience. Paying $30K+ to go back to school seemed irresponsible.

Here are my curriculum choices and the rationale behind them.

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Blog banner photo provided by Ryan McGuire