If You Want To Make 6 Figures as a Data Analyst, Start With This Free Online Python Course
I’ve updated my data analytics career guides after nearly a decade of industry experience. Here are my programming course rankings for 2023.
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In 2015, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through platforms like edX and Coursera instead. And I could learn it faster, more efficiently, and for a fraction of the cost.
I took dozens of data science-related courses and audited portions of many more. I knew the options out there and the skills needed for learners preparing for a data science role. So I created a review-driven guide that recommended the best courses for each subject.
Those posts went viral with millions of views, and multiple industry-leading online education companies hired me to create data courses for them, specifically focusing on the Data Analyst role.
I’m now updating my guides for the Data Analyst role in 2023.
For the first article in this new series on Medium, I will share my recommendations for introduction to programming courses for the beginner data analyst, my personal favorite role within data science.
I spent 8+ hours reviewing every Python programming course offered on the internet as of February 2023, extracting key bits of information from their syllabi and reviews, compiling their ratings, then evaluating them using an industry-proven methodology. My end goal was to identify the three best courses available and present them to you, below.
Want to learn *ALL* of the skills necessary to become a data analyst, including the top programming course in this guide, without spending 4 years and $41,762 to go to university? Follow my 28-day curriculum below.
How I picked courses to consider
Each course had to fit four criteria:
- It introduces programming (and, optionally, computer science) and the language of instruction is Python.
- It must be an online course with a duration of at least 10 hours.
- No prerequisite programming experience required.
- It must have a rating greater than 4.5/5.
Why Python programming?
In short, Python/R/SQL programming proficiency plus domain expertise equals a six-figure salary as a Data Analyst. This Reddit comment from a data pro backs up what I’ve noticed when researching Data Analyst job postings: less than $100k if you can’t code, more than $100k if you can.
Python, R, or SQL proficiency + domain expertise = six-figure salary as a Data Analyst
Yes, it is true that data analysts don’t need coding skills if they learn business intelligence tools like Excel or PowerBI. However, learning programming gives analysts more flexibility with their work, and this aforementioned upside on their earning power.
I chose Python as the programming language for this series because it is the most popular programming language for data analytics and it has better (as judged by my criteria in this article) introductory course options vs. R or SQL. It is also the language in which I have the most expertise.
A note on Programming vs. Computer Science
Programming is not computer science and vice versa. There is a difference. Borrowing this answer from Software Engineering Stack Exchange:
Computer science is the study of what computers [can] do; programming is the practice of making computers do things.
The course I am looking for introduces programming and optionally touches on relevant aspects of computer science that would benefit a new programmer in terms of awareness.
Many of the courses below do indeed have a computer science portion. However, none are strictly computer science courses, which is why Harvard CS50’s Introduction to Computer Science is excluded.
How I evaluated courses
After finding the courses that fit my criteria, I then looked at course structure and learning software. I want short videos with automatic grading that delivers targeted feedback when students make a mistake. The goal here was to gauge for and maximize completion rate and learning outcome achievement.
An extreme example: Harvard CS50’s Introduction to Programming with Python is multiple weeks long and uses lecture videos that are often 1–2 hours long. That is daunting for most students, and causes some to abandon the course.
On the other hand, DataCamp courses are four hours long and have 3–4 minute videos, then interactive exercises to test and grade video content. Beyond that, they also use a proprietary automatic grading system that points out learner mistakes and guides them to the correct solution. The result is DataCamp courses have completion rates significantly higher than the industry average (60% vs. 15%).
I therefore default to DataCamp courses when evaluating a subject area within data science. I will recommend courses on other platforms if:
- they are extremely well-reviewed (often because they also use bite-sized videos and automatic grading), or
- the content in the DataCamp course is less focused and/or detailed than a competitor’s course.
The second bullet point leads into another factor for evaluation: how well the course covers the fundamentals of programming (and nothing beyond the fundamentals) and presents them in a context useful to a data analyst. Programming courses geared towards web developers aren’t as desirable.
I believe I covered every notable course that exists and which fits the above criteria. There is a chance I missed something, however. Please let me know in the comments if you think that is the case.
Note: my data analyst career guides are learner-supported. Some of the links to the resources in this guide are affiliate links, meaning I receive a commission (at no extra cost to you) if you use that link to make a purchase.
My recommendation for the BEST intro to programming for data analysts is…
The first two courses in the University of Michigan’s Python for Everybody Specialization on Coursera:
- Programming for Everybody (Getting Started with Python) (4.8 rating based on 218,864 ratings)
- Python Data Structures (4.9 rating based on 91,809 ratings)
Pricing
Both courses are free to audit, which means you have access to the lecture materials. I recommend upgrading to the paid Specialization ($49 per month at time of writing in the United States) to get access to the autograded Assignment sections to get feedback on your coding practice.
Time commitment
The time commitment is 19 hours for the first course and 18 hours for the second course, for a total of 37 hours. Dedicating 4–5 hours per day to these courses, that is approximately 8 days.
My review
These courses are all taught by Dr. Charles Severance, also known as Dr. Chuck. The first course covers Chapters 1–5 of his textbook Python for Everybody, and the second course covers Chapters 6–10. The first course is one of the most popular online courses ever with nearly 2.8 million enrollments on Coursera at time of writing.
There is a third and a fourth course in the Specialization called Using Python to Access Web Data and Using Databases with Python, but I don’t recommend aspiring data analysts take them right away because they are more specialized skills.
Dr. Chuck’s approachable teaching style is loved by students. I like how he explains “Why we program” before diving into technique. The scope of the first two courses is also just right for an aspiring data analyst — not too in depth, not too long, and covers the main topics within programming that you need for analytics. The description of the Specialization alludes to this analytics-geared scope: “Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.”
One nitpick, but not a dealbreaker: I’d prefer if the videos in the course were closer to 5 minutes each in length. Currently they are often in the 10–15 minute range. The autograder works well as well, though it is not as seamless and fully featured as DataCamp’s system, which is my preference as I describe in the How I evaluated courses section above.
Recent reviews from students
On the Programming for Everybody course:
The instructor is there with you every step of the way, from beginning to play around with Python and writing your first “hello world”, to writing a complicated function.
The course is easy to get through, and you can tell how passionate Dr. Chuck is about Python. He’s very encouraging, which is great if you had little to no confidence like I did when first starting, and is great at explaining things in beginner-friendly terms and makes it easy for you to become truly interested in learning to program … It was also so encouraging watching the office hours and seeing all the other people who took the course around the world.
I genuinely cannot recommend this course enough to everybody, it really made such an impact on me…It was, and still is, the best beginner course I’ve ever come across. Full review
My #2 pick: If you prefer an intro that’s longer & more in depth…
…go with the first three courses in The Georgia Institute of Technology’s Introduction to Python Programming Professional Certificate on edX.
- Fundamentals and Procedural Programming (4.8 rating, based on 228 Class Central reviews)
- Control Structures (4.9 rating, based on 54 Class Central reviews)
- Data Structures (4.8 rating, based on 44 Class Central reviews)
Pricing
The courses are free to audit. Students must upgrade to the paid Verified Track to access the coding problems, which is $149 per course at time of writing in the United States. One reviewer writes: “The platform hosting the online problems works really well, with the ability to run your code and change the values right in the platform.” I recommend upgrading because getting feedback on your coding practice is vital for a beginner.
Time commitment
My analysis of the student reviews on Class Central suggests each of the above courses should take between 30–35 hours per course, which is a total of 90–105 hours. Again, this series is more in depth than my #1 pick.
My review
There is a fourth course in the Professional Certificate called Objects & Algorithms, but I don’t recommend aspiring data analysts to take it because data analysts don’t use the skills taught in that course very often.
These courses are all taught by Dr. David Joyner. The series is identical to Georgia Tech’s first class in undergraduate computer science:
Over 400 students on campus have completed this version of the course, and our analysis shows that they exit the course with the same learning outcomes as students taking the traditional on-campus version. This Professional Certificate uses the same instructional material and assessments as learning Python on campus, giving you a Georgia Tech-caliber introduction into the field of computing at your own pace.
I love how the course is broken up into bite-sized videos, often as short as one-minute long. The autograding system functions as well, though it is not as seamless (Georgia Tech uses a separate external tool) and fully featured as DataCamp’s system, as described in the How I evaluated courses section above. The length of the course is the main reason why it is not my #1 pick.
Recent reviews from students
On the Control Structures course:
The best part of the course, apart from the material, is the tone of the teaching. It’s inviting. Professor Joyner and his team are adept at understanding core problem areas of students when it comes to learning how to code, which is evident in how they emphasize certain learning challenges throughout the course (nested loops, understanding the “return” command of a function, etc.). Moreover, they treat you like a true beginner, someone who doesn’t know anything about the course that they’re taking, which is a huge plus. Sometimes introductory courses aren’t “introductory” enough. Full review
My #3 pick: an intro with the best learning software for data analytics
All four courses in DataCamp’s Python Fundamentals Track:
- Introduction to Python (4.7 rating, based on 814 reviews)
- Intermediate Python (4.6 rating, based on 254 reviews)
- Python Data Science Toolbox (Part 1)
- Python Data Science Toolbox (Part 2)
Pricing
The first course (Introduction to Python) is free. Completing the rest of the courses in the track requires a DataCamp subscription.
Time commitment
Fifteen hours in total for the entire track.
My review
All four of these courses are taught by Hugo Bowne-Anderson, a veteran data scientist, educator, writer and podcaster.
Everything I mentioned in the How I evaluated courses section above applies here regarding how I (highly) rate DataCamp courses and lean towards recommending them in most cases. However, I believe my #1 and #2 picks win out in the case of the subject of Intro to Python programming because they get better ratings and have a more in-depth coverage of the subject.
Recent reviews from students:
On the Introduction to Python course:
I have been trying to learn Python for a while… I finally received a recommendation from one of my instructors at school to try DataCamp. I am very happy I did it. I loved the structure of the course and the fact that it’s very hands on. The bite size exercises and code snippets are very well planned out. I also like the constructive feedback when an answer is not fully correct to help you get it right. I am glad to say Hugo has turned me into a Pythonistic Ninja 🙂.
The Competition
Let’s look at other notable courses on the market. Here are Python programming courses that fit the criteria mentioned above (descending by my preference):
- Dataquest’s Python Basics for Data Analysis Skill Path, which is made up of five courses. Text-based courses with interactive grading. Data-focused platform and curriculum. Estimated completion time of 45 hours. 4.8 rating based on 359 reviews.
- Crash Course on Python by Google on Coursera. Great course, incredibly popular. Geared towards IT professionals and not data analysts, so it didn’t make my top 3, but would still be a good option. 27 hours. Free with optional upgrade available. 4.8 rating based on 28,990 ratings.
- Python Basics and Python Functions, Files, and Dictionaries by University of Michigan on Coursera. Part of Python 3 Programming Specialization. Well-structured and well-liked, but relatively long vs. my #1 pick (also a University of Michigan course but taught by a different professor). 34 hours for course 1 and 32 hours for course 2. Free with optional upgrade available. 4.8 rating based on 16,108 ratings for course 1, 4.8 and 4,927 for course 2.
- Python for Data Science, AI & Development by IBM Skills Network on Coursera. Taught by a Ph.D. and Data Scientist at IBM. Geared towards data professionals specifically. Part of Data Science Fundamentals with Python and SQL Specialization. 22 hours. Free with optional upgrade available. 4.6 rating based on 30,713 ratings.
- Harvard CS50’s Introduction to Programming with Python on edX. New course. 1–2 hour long lecture-style videos. 60 hours. Free with optional upgrade available. 5.0 rating based on 4 Class Central reviews.
- Learn to Program: The Fundamentals and Learn to Program: Crafting Quality Code by University of Toronto on Coursera. I like the content of the courses but the rating data isn’t as strong as competitors. 25 hours for course 1 and 14 hours for course 2. Free with optional upgrade available. 4.7 rating based on 5,942 ratings for course 1, and 4.6 and 698 for course 2.
- An Introduction to Interactive Programming in Python (Part 1) and An Introduction to Interactive Programming in Python (Part 2) by Rice University on Coursera. Part of Fundamentals of Computing Specialization. Content more geared towards the web developer profession. 19 hours for Part 1 and 16 hours for Part 2. Free with optional upgrade available. 4.8 rating based on 3,194 ratings for Part 1, and 4.9 and 1,156 for Part 2.
- Python Foundations for Data Analysis & Business Intelligence by Maven Analytics on Udemy. Nice scope and length of course. No automated grading. 12 hours of video and 3 articles. Paid course, price depends on current discount. 4.7 rating based on 572 ratings.
- CS For All: Introduction to Computer Science and Python Programming by Harvey Mudd College on edX. Scope of course larger and length of course longer than my #1 pick. 84 hours. Free with optional upgrade available. 5.0 rating based on 4 Class Central reviews.
- Automate the Boring Stuff by Al Sweigart on Udemy. Not marketed towards data analysts but the scope of the course serves our purposes well. 10 hours of video plus 95 downloadable resources. Paid course, price depends on current discount. 4.7 based on 105,163 ratings.
- Team Treehouse’s Beginning Python Track. Videos, multiple choice questions, and coding challenges. 14 hours. “Courses” plan required for access to entire track.
- The Complete Python Bootcamp From Zero to Hero in Python by Jose Portilla on Udemy. Covers advanced topics that aren’t as useful in an intro to programming course for a data analyst. No automated grading. 22 hours of video plus 14 articles and 19 coding exercises. Paid course, price depends on current discount. 4.6 rating based on 451,290 ratings.
- Introduction to Computer Science and Programming Using Python by MIT on edX. Long, difficult course according to reviewers. 135 hours. Free with optional upgrade available. 4.5 rating based on 129 Class Central reviews.
- 365 Data Science’s Python Programmer Bootcamp. Videos with coding exercises graded by multiple choice. 11 hours. Requires a 365DataScience subscription. 4.9 rating based on 1,588 reviews.
Summary
My #1 recommendation for an introduction to programming course in Python for aspiring data analysts is the University of Michigan’s Python for Everybody Specialization on Coursera, and specifically the first two courses in that specialization:
- Programming for Everybody (Getting Started with Python) (4.8 rating based on 218,864 ratings)
- Python Data Structures (4.9 rating based on 91,809 ratings)
Python is the most popular language for data analytics and this course is the most popular course on the internet for learning Python, so it’s a natural choice. Take these two courses and you’ll maximize flexibility and earning power in your future data analyst work.
Next in this series…
This is the first of a seven-piece series that covers the best online courses for launching yourself into the data analytics field. It will cover several other data analytics core competencies: Excel, SQL, Exploratory Data Analysis, Data Visualization, and Statistics.
The final article will summarize my top course picks for those major subject areas. That article will also present a comprehensive 40-day curriculum with course picks for the smaller subjects within data analytics that don’t merit a full guide like this one.
Want to learn *ALL* of the skills necessary to become a data analyst, including the top programming course in this guide, without spending 4 years and $41,762 to go to university? Follow my 28-day curriculum below.
