5 Free Stanford University Courses for Data Science

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Learning data science has never been more accessible. If you’re motivated, you can study data science for free at elite universities around the world.

We’ve prepared a list of free courses at Stanford University that will support you gain all the necessary data science skills:

• Basics of programing
• Databases and SQL
• Machine learning
• Working with vast data sets

So start learning today to achieve your educational goals and start your data science career. Now let’s move on to these courses.

To get started in data science, it’s vital to build a foundation in programming in a programming language like Python. The Programming methodology the class teaches Python programming from scratch and does not assume any prior programming experience.

In this course, you will learn how to solve problems in Python while becoming familiar with the features of the language. You’ll start with the basics like variables and control flow, then learn about built-in data structures like lists and dictionaries.

Along the way, you’ll also learn how to work with images, object-oriented programming in Python, and memory management.

To combine: : Programming methodology

Note: You can monitor the course for free and access all its content.

• Relational databases and SQL
• Query performance
• Transaction and concurrency control
• Database limitations, triggers, views
• OLAP cubes, star diagram
• Database modeling
• Work with semi-structured data such as JSON and XML

Links to courses: :

1. Databases: relational databases and SQL
2. Databases: Advanced topics in SQL
3. Databases: OLAP and recursion
4. Databases: modeling and theory
5. Databases: semi-structured data

As a data analyst, you should be able to analyze data using Python and SQL and answer business questions. But sometimes you may need to build predictive models. This is why machine learning is helpful.

Machine learning Or CS229: Machine Learning at Stanford University is one of the most popular and highly recommended ML courses. You’ll learn everything you would typically learn in a semester-long university course. The course covers the following topics:

• Supervised learning
• Unsupervised learning
• Deep learning
• Generalization and regularization
• Enhancing learning and control

To combine: : Machine learning

Introduction to Statistical Learning with Python Applications (or ISL with Python) is the Python edition of ISLR’s popular book on statistical learning.

The Statistical learning with Python The course covers all content ISL with Python book. Thanks to this, you will learn basic tools for data analysis and statistical modeling. Here is an overview of the vital topics covered in this course:

• Linear Regression
• Classification
• Resampling
• Choosing a linear model
• Tree-based methods
• Unsupervised learning
• Deep learning

To combine: : Statistical learning with Python

Mining huge data sets is a course focusing on data mining and machine learning algorithms for working with and analyzing huge data sets.

To get the most out of this course, you should have some programming knowledge, preferably in Java or Python. You should also know math: probability and linear algebra. If you are a beginner, consider taking the courses mentioned earlier before taking this one.

Here are some of the topics covered in this course:

• Nearest neighbor search in multidimensional space
• Location-aware hashing (LSH)
• Dimensionality reduction
• Supervised machine learning at scale
• Grouping
• Recommendation systems

You can utilize Mining huge data sets book as a supplement to this course. The book is also available free of charge on the Internet.

To combine: : Mining huge data sets

This compilation of free courses from Stanford University should support you learn almost everything you need if you ever want to delve deeper into data science.

If you’re looking for college courses where you can learn Python and data science for free, here are some articles you might find useful:

Have a nice studying!

Bala Priya C is a software developer and technical writer from India. He likes working at the intersection of mathematics, programming, data analytics and content creation. Her areas of interest and specialization include DevOps, data analytics and natural language processing. She enjoys reading, writing, coding and coffee! He is currently working on learning and sharing his knowledge with the developer community by writing tutorials, guides, reviews, and more. Bala also creates compelling resource overviews and coding tutorials.