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Learning from free courses can be highly beneficial for those seeking to enter the field of data science. Free courses offer numerous advantages such as cost-effectiveness, flexibility, access to the latest tools and concepts, opportunities to learn from industry experts, community support, and hands-on learning experience instead of spoon-feeding.

In this blog, my goal is to help you enhance your data science skills by providing a comprehensive list of free courses on various topics, including Python, SQL, data analytics, business intelligence, data engineering, machine learning, deep learning, generative AI, and MLOps.

Most of these courses are from top universities and platforms like Coursera, MIT, UC Davis, FreeCodeCamp, Google, Microsoft, IBM, Harvard, and Stanford Universities. So, start your journey of becoming a professional data scientist today!
 

Note: Coursera courses are available for free to audit, and if that option is not available, you can complete the courses during the trial period or ask for financial aid.

 

1. Python

 

Python is a necessary programming language for data science. You will learn it for data manipulation, analysis, visualization, and machine learning. It offers a vast array of libraries and frameworks that simplify complex tasks, making it a popular choice among data scientists.

• Python for Beginners by Programming with Mosh
• Python for Everybody  by freecodecamp
• Intermediate Python Programming by freecodecamp
• CS50’s Introduction to Programming with Python by Harvard University
• Principles of Computation with Python by Carnegie Mellon University

 

2. Databases and SQL

 

SQL (Structured Query Language) is a query language used to manage and manipulate relational databases, which are crucial for data storage, retrieval, and analysis.

• SQL Tutorial - Full Database Course for Beginners by freecodecamp
• Learn SQL Basics for Data Science Specialization by UC Davis
• NoSQL vs SQL – Which Type of Database Should You Use? by freecodecamp
• Intro to Database Systems by Carnegie Mellon University
• Advanced Database Systems by Carnegie Mellon University

 

3. Data Analytics

 

As you may know, data analytics is a crucial aspect of data science that helps businesses make informed decisions based on data-driven insights. This involves using a variety of tools and techniques to extract meaningful information from data.

• Google Data Analytics Professional Certificate by Google
• Data Analysis with Python for Excel Users by freecodecamp
• Data Analysis with Python Certification by freecodecamp
• Advanced Data Analytics Professional Certificate by Google
• Data Analyst Professional Certificate by IBM

 

4. General Data Science

 

General data science courses cover a wide range of topics, from data manipulation to time series analysis and data modeling.

• 9 Free Harvard Courses to Learn Data Science by Harvard University
• Data Science Undergraduate Program by OSSU
• Data Visualization by Kaggle
• Introduction to Data Science with Python by Harvard University
• Statistical Learning by Stanford University

 

5. Business Intelligence

 

You can use Business Intelligence tools like Power BI or Tableau to transform raw data into actionable insights, which helps with decision-making. You don't need to learn any other programming languages besides SQL.

• Power BI Full Course by edureka
• Tableau For Data Science by SimpleLearn
• Data Warehousing for Business Intelligence by University of Colorado 
• Business Intelligence Course with Certificate by SimpleLearn
• Business Analyst Roadmap 2024 by WsCube Tech

 

6. Data  Engineering

 

Data engineering is the subfield of data science that deals with designing, building, and maintaining data pipelines and infrastructure.

• Data Engineering by IBM
• Data Engineer Learning Path by Google
• Database Engineer Professional Certificate by Meta
• Big Data Specialization by UC San Diego
• The Data Engineering Zoomcamp by DataTalks.Club

 

7. Machine Learning

 

Machine learning is a branch of artificial intelligence that involves creating algorithms capable of learning from data and making predictions. It is an essential skill for data scientists.

• Intro to Machine Learning by Kaggle
• Machine Learning for Everybody by Kylie Ying
• Machine Learning in Python with Scikit-Learn by FUN MOOC
• Machine Learning Zoomcamp by DataTalksClub
• CS229: Machine Learning by Stanford University

 

8. Deep Learning

 

Deep learning is a subset of machine learning that focuses on neural networks with multiple layers. It is widely used in image and speech recognition, natural language processing, and other complex tasks.

• Artificial Intelligence: The Big Picture of AI by Pluralsight
• Basics of Deep Learning by Udemy
• The Key to Understanding Deep Learning by MIT
• Deep Learning Specialization by DeepLearning.AI
• Deep Learning Crash Course for Beginners by freecodecamp

 

9. Generative AI

 

Generative AI refers to the process of creating new content, such as text, images, and audio, by analyzing patterns and structures learned from existing data. In your learning process, you will mainly focus on Large Language Models, and how to train, fine-tune, and deploy them.

• Generative AI for Beginners by Microsoft
• LangChain & Vector Databases in Production by Activeloop
• Generative AI with Large Language Models by AWS
• Large Language Models: Application through Production by DataBricks
• Large Language Model Course by Maxime Labonne

 

10. MLOps

 

MLOps, short for Machine Learning Operations, is the process of automating and streamlining the deployment and management of machine learning models. Currently, it is one of the most in-demand career fields in the data science industry.

1. Python Essentials for MLOps by Duke University
2. MLOps for Beginners by Udemy
3. Machine Learning Engineering for Production (MLOps) Specialisation by DeepLearning.AI
4. MLOps bootcamp from DataTalks.Club
5. Made With ML by Goku Mohandas

 

Conclusion

 

You don't need to search Google to find high-quality courses on data. All you have to do is bookmark this page and start your journey with Python and SQL. In a few months, you will be able to ingest, process, analyze, and model data. After that, it will be a continuous learning journey. It is highly recommended to build your portfolio on GitHub or any other platform from the start if you want to get hired by top recruiters.

Check out the blog on "5 Free Platforms for Building a Strong Data Science Portfolio" to learn about other platforms and what they provide.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.