Basic coding, stats, and probability experience required
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The final three guides in this series of articles will cover each aspect of the data science process in detail. These compilations of courses elude the purpose of this series: to find the best individual courses for each subject to comprise a data science education. This guide therefore won’t include full specializations or programs like Johns Hopkins University’s Data Science Specialization on Coursera or Udacity’s Data Analyst Nanodegree. We don’t want too in-depth coverage of specific aspects of the process, hence the “intro to” portion of the title.įor each aspect, the ideal course explains key concepts within the framework of the process, introduces common tools, and provides a few examples (preferably hands-on). Our goal with this introduction to data science course is to become familiar with the data science process. The following infographic from Harvard professors Joe Blitzstein and Hanspeter Pfister outlines a typical data science process, which will help us answer these questions. What is data science? What does a data scientist do? These are the types of fundamental questions that an intro to data science course should answer. Python and R are the two most popular programming languages used in data science. Is the course taught using popular programming languages like Python and/or R? These aren’t necessary, but helpful in most cases so slight preference is given to these courses.
Does the course brush over or skip certain subjects? Does it cover certain subjects in too much detail? See the next section for what this process entails.Ģ. We made subjective syllabus judgment calls based on two factors:ġ. We read text reviews and used this feedback to supplement the numerical ratings. We compiled average rating and number of reviews from Class Central and other review sites to calculate a weighted average rating for each course. So please let us know in the comments section if we left a good course out. There’s always a chance that we missed something, though. Since there are seemingly hundreds of courses on Udemy, we chose to consider the most-reviewed and highest-rated ones only. We believe we covered every notable course that fits the above criteria. Though these are viable ways to learn, this guide focuses on courses.
Dhawal personally helped me assemble this list of resources. Since 2011, Class Central founder Dhawal Shah has kept a closer eye on online courses than arguably anyone else in the world. For this task, I turned to none other than the open source Class Central community and its database of thousands of course ratings and reviews. I’ll explain shortly.)įor this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. (Don’t worry if you’re unsure of what an intro to data science course entails. Then it was statistics and probability classes. A few months ago, I started creating a review-driven guide that recommends the best courses for each subject within data science.įor the first guide in the series, I recommended a few coding classes for the beginner data scientist. I know the options out there, and what skills are needed for learners preparing for a data analyst or data scientist role.
I’ve taken many data science-related courses and audited portions of many more. And I could learn it faster, more efficiently, and for a fraction of the cost. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. I started creating my own data science master’s program using online resources. A year ago, I dropped out of one of the best computer science programs in Canada.