Top 5 Free Data Science Courses from Stanford University

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Are you an aspiring data scientist? If so, these free data science courses from Stanford will help you move forward in your data science journey!

Learning data science has never been more accessible. If you’re motivated, you can teach yourself data science—for free—with courses from elite universities across the world.

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Top 5 Free Data Science Courses from Stanford University

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We’ve put together this list of Free Data Science Courses from Stanford University to help you learn all the essential data science skills:

  • Programming fundamentals 
  • Databases and SQL
  • Machine Learning
  • Working with large datasets 

5 Must Take Courses by Top 5 Free Data Science Courses

Here are the 5 courses by Stanford University:

1. Programming Methodology

To get started with data science, building programming foundations in a programming language like Python is important. The Programming Methodology class teaches Python programming from the ground up and does not assume any previous programming experience.

In this course, you’ll learn problem-solving with Python while becoming familiar with the features of the language. Free Data Science Courses You’ll start with the basics such as variables and control flow and then learn about built-in data structures like lists and dictionaries.

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

LinkProgramming Methodology

2. Databases

A strong understanding of databases and SQL is important to succeed in any data career. You can take the popular databases course by Prof. Jennifer Widom as a series of five self-paced courses on edX.

Note: You can audit the course and access all course contents for free. 

If you are new to databases, take the first Free Data Science Courses covering the basics of relational databases before you proceed to the courses on more advanced topics. By working through the series of courses, you’ll learn:

  • Relational databases and SQL
  • Query performance
  • Transaction and concurrency control
  • Database constraints, triggers, views
  • OLAP cubes, star schema
  • Database modeling
  • Working with semi-structured data like JSON and XML

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  1. Databases: Relational Databases and SQL
  2. Databases: Advanced Topics in SQL
  3. Databases: OLAP and Recursion 
  4. Databases: Modeling and Theory
  5. Databases: Semistructured Data

3. Machine Learning 

As a data scientist, you should be able to analyze data using Python and SQL and answer business questions. But sometimes you may also need to build predictive models. Which is why learning 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’d typically learn in a semester-long university course. This course covers the following topics: 

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

LinkMachine Learning

4. Statistical Learning with Python

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

The Statistical Learning with Python course covers all the contents of the ISL with Python book. So you’ll learn essential tools for data science and statistical modeling. Free Data Science Courses Here is an overview of important topics that this course covers:

  • Linear regression
  • Classification 
  • Resampling 
  • Linear model selection
  • Tree-based methods 
  • Unsupervised learning
  • Deep learning 

LinkStatistical Learning with Python

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5. Mining Massive Data Sets

Mining Massive Data Sets is a course focusing on data mining and machine learning algorithms for working with and analyzing massive datasets. 

To make the most out of this course you should be comfortable with programming, preferably with Java or Python. Free Data Science Courses You should also be familiar with math: probability and linear algebra. If you’re a beginner, consider working through the courses mentioned earlier before you take this one.

Here are some topics this course covers:

  • Nearest neighbor search in high-dimensional space 
  • Locality Sensitive Hashing (LSH)
  • Dimensionality reduction 
  • Large-scale supervised machine learning 
  • Clustering 
  • Recommendation systems 

You can use the Mining Massive Datasets book as a companion to this course. The book is also accessible for free online.

LinkMining Massive Data Sets

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FAQs

What are the specific titles of the 5 Free Data Science Courses offered by Stanford?

A: While an official list might not be available yet, Stanford offers various free courses relevant to data science. Here are some possibilities:

Are there any prerequisites for these courses?

Prerequisites may vary depending on the specific course. Some might require basic programming knowledge or familiarity with high school-level math.

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