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To become a data analyst, I spent about $30,000 on a 3-year computer science degree.
This was an exorbitant and time-consuming process.
After graduating, I realized that I could have learned all the necessary skills online instead. Top universities like Harvard, Stanford, and MIT have released dozens of courses that anyone can take.
And the best part?
They are completely free.
Thanks to the Internet, you can now get an Ivy League education for free from the comfort of your home.
If I could start over, here are 5 free college courses I would take to learn how to code for data science.
Note: Python and R are two of the most commonly used programming languages in data science, so most of the courses on this list focus on one or both of these languages.
1. Harvard University – CS50 Introduction to Computer Science
CS50 from Harvard the course is one of the most popular programming courses for beginners offered by the university.
It guides you through the fundamentals of computer science, covering both theoretical concepts and practical applications. You will be exposed to multiple programming languages such as Python, C and SQL.
Think of this course as a mini computer science degree wrapped in 24 hours of YouTube content. For comparison, CS50 covered science that took me three semesters to learn at my university.
Here’s what you’ll learn in CS50:
- Basics of programing
- Data structures and algorithms
- Website design with HTML and CSS
- Software engineering concepts
- Memory management
- Database management
If you want to become a data scientist, a solid foundation in programming and computer science is required. You’ll often be expected to extract data from databases, deploy machine learning models in production, and create model pipelines that can scale.
Programs like CS50 equip you with the technical foundations you need to move to the next stage of your educational journey.
Link to the course: : Harvard CS50
2. MIT – Introduction to computer science and programming
MITx’s introduction to computer science and programming is another introductory course designed to equip you with basic IT and programming skills.
However, unlike CS50, this course is taught primarily in Python and places a bulky emphasis on computational thinking and problem solving.
Moreover, MIT’s Introduction to Computer Science course focuses more on data science and practical applications of Python, making it a solid choice for students whose sole goal is to learn programming for data science.
After completing the Introduction to Computer Science course at MIT, you will be familiar with the following concepts:
- Programming in Python: syntax, data types, functions
- Computational thinking: problem solving, algorithm design
- Data structures: lists, tuples, dictionaries, sets
- Algorithmic Complexity: Huge O notation
- Object-oriented programming: classes, objects, inheritance, polymorphism
- Software engineering principles: debugging, software testing, exception handling
- Mathematics for Computer Science: Statistics and probability, linear regression, data modeling
- Computational models: principles and techniques of simulation
- Data science fundamentals: data visualization and analysis
You can check out this course for free on edX.
Link to the course: : MITx – Introduction to Computer Science
3. MIT – Introduction to Algorithms
You can take part after completing a basic computer science course such as CS50 MIT’s Introduction to Algorithms teaching path.
This program will teach you how to design, analyze and implement algorithms and data structures.
As a data scientist, you will often need to implement solutions that maintain performance even as the size of your data set increases. You also need to handle enormous data sets, which can be computationally exorbitant to process.
In this course, you will learn to optimize your data processing tasks and make informed decisions about which algorithms to employ, based on available computing resources.
Here’s what you’ll learn in Intro to Algorithms:
- Algorithm analysis
- Data structures
- Sorting algorithms
- Graph algorithms
- Algorithmic techniques
- Hashing
- Computational complexity
All Introduction to Algorithms lectures can be found on MIT OpenCourseWare.
Link to the course: : MIT – Introduction to Algorithms
4. University of Michigan – Python for everyone
Python for everyone is a basic programming specialization focusing on teaching Python.
This is a five-course learning path covering Python fundamentals, data structures, API usage, and database access with Python.
Unlike previous courses, Python for Everyone is largely practical. The specialization focuses on practical application rather than theoretical concepts.
This makes it ideal for those who want to dive straight into implementing real-world projects.
Here are some concepts you will know by the end of this 5-course specialization:
- Python variables
- Functions and loops
- Data structures
- APIs and web data access
- Using databases in Python
- Data visualization in Python
You can check out this course for free on Coursera.
Link to the course: : Python for everyone
5. Johns Hopkins University – R Programming
You may have noticed that every course so far focuses on Python programming.
That’s because I’m a bit of a Python lover.
I find the language to be versatile and user-friendly, and knowledge of Python can be transferred to many fields, not just data science.
However, there are some benefits to learning R for data science. R programming is designed specifically for statistical analysis, and there are a number of specialized R packages for tuning and optimizing parameters that are not available in Python.
You should consider learning R if you are interested in deep statistical analysis, academic research, and advanced data visualization. If you want to learn R, R Programming specialization at Johns Hopkins University is a great place to start.
Here’s what you’ll learn in this specialization:
- Data types and functions
- Flow control
- Reading, cleaning and processing data in R
- Analysis of reconnaissance data
- Data simulation and profiling
You can check out this course for free on Coursera.
Link to the course: : Specialization: R Programming
Learn to code for data science: next steps
After completing one or more of the courses described in this article, you will be equipped with a wealth of modern programming knowledge.
But the journey doesn’t end here.
If your ultimate goal is to build a career in data analytics, here are some potential next steps you should consider:
1. Practice your coding skills
I suggest visiting coding challenge sites such as Hacker rank AND Leetcode to practice your programming skills.
Since programming is a skill best developed through gradual challenge, I recommend starting with problems marked “Easy” on these platforms, such as adding or multiplying two numbers.
As you improve your programming skills, you can start increasing the difficulty level and solving more challenging problems.
When I started working in data science, I solved HackerRank problems every day for about 2 months and found that by the end of that period, my programming skills had improved significantly.
2. Create personal projects
Once you’ve spent a few months solving HackerRank challenges, you’ll be prepared for convoluted projects.
You can start by creating a elementary calculator application in Python and move on to more challenging projects, such as a data visualization dashboard.
If you still don’t know where to start, check out this list Python project ideas for inspiration.
3. Building a portfolio site
Once you learn how to code and create a few personal projects, you can display your work on a centralized portfolio site.
When potential employers want to hire a developer or data scientist, they can view all of your work (skills, certifications, and projects) in one place.
If you want to build your own portfolio website, I’ve created a complete video tutorial on how to do it build a data science portfolio website for free with ChatGPT.
You can check out the tutorial for a step-by-step guide on how to create a visually appealing portfolio website.
 
 
Natasha Selwaraj is a self-taught data analyst with a passion for writing. Natasha writes about everything related to data science, she is a true master of all data related topics. You can connect with her LinkedIn or check it out Youtube channel.