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Many courses could teach you the basics of data science, but Harvard University is undoubtedly at the top. Coming from an elite university, all their courses certainly provide you with the skills necessary to become a data scientist.
So, what are these free courses that you should know?
Let’s get into it.
HarvardX: CS50’s Introduction to Programming with Python
Python is the key to employment for any aspiring data scientist.
Data scientists are expected to understand programming languages in the modern era. Python is a de facto language that is used in many industries, so it’s beneficial to learn it.
HarvardX: CS50’s Introduction to Programming with Python would teach you the primary Python programming language necessary as a data scientist. In this course, we would learn the following:
- Functions, Variables
- Conditionals
- Loops
- Libraries
- Unit Testing
And many more things you would learn within ten weeks if you put in around 3–9 hours per week. You should start with this free Harvard course before entering any other courses, as many data science courses after this depend on your ability to use Python.
HarvardX: Fat Chance: Probability from the Ground Up
For data scientists, it is important to understand the basics of statistical probability as it relates to our work. To boost your quantitative reasoning skills, the HarvardX: Fat Chance: Probability from the Ground Up course is perfect for building up that knowledge.
In this course, you would go through material that increases your understanding of probability and statistics, such as:
- Basic and advanced counting
- Basic and Conditional Probability
- Expected Values
- Bernoulli Trials
- Normal Distribution
The course is designed for self-paced learning and considerably takes around seven weeks to finish if you put up 3–5 hours per week for learning.
HarvardX: Introduction to Data Science with Python
After you have the foundation for Python and Probability, it’s time to learn about Data Science. The HarvardX: Introduction to Data Science with Python would teach you the foundations necessary to enter the data science field.
It’s a self-paced course but requires a basic understanding of Python and Probability. That is why, it’s important to finish the two previous courses.
The course can be finished in 8 weeks if you spend around 3-4 hours per day and you would learn the following:
- Linear, Multiple, and Polynomial Regression
- Model Selection and Cross-Validation
- Bias, Variance, and Hyperparameters
- Classification and Logistic Regression
- Bootstrap, Confidence Intervals, and Hypothesis Testing
There are many things you would learn from this course. Learn it well; the foundation would be important for the next course.
HarvardX: Machine Learning and AI with Python
If you already have the data science basic, it’s time to learn a more advanced field. Machine Learning and AI are inseparable from data science as many business data science projects are based on machine learning output.
Machine learning and AI knowledge are valuable in industry as they could uncover patterns that we haven’t seen previously while able to provide automation. This could make businesses perform much more efficiently than standard rule-based or feeling-based decision-making.
The HarvardX: Machine Learning and AI with Python course would give the learner a basic understanding of machine learning and AI, including:
- Machine Learning models
- Model Training
- Model Evaluation
- Python for Machine Learning models
With six week estimation to finish if you spend around 4–5 hours per week, you will be ready to develop your first data science project.
Conclusion
The Harvard courses we have explored would help you become a data scientist.
By shaping up your foundation, all these courses would guide the aspiring data scientist into their dream careers.
There might be only four courses listed, but these four are the only ones you need to build up the basics.
Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.
