Top Free Data Science Online Courses for 2023
Learn Data Science in 2023 for FREE with these online courses.
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Sticking to your new year's resolutions can be difficult. It’s even harder when you’ve decided you want to shift careers and learn a new skill. It can be daunting to enter something you have no prior knowledge about.
Having the right resources available helps you overcome that fear and allows you to stick to your new year’s resolutions.
If you are looking to enter the world of data science, but are unsure of what courses, books, or bootcamps to go ahead with. Continue reading this article, and you will find a list of FREE online data science courses to help you build that new skill.
Free Courses to Excel with in 2023
All of these courses are free and have been highly trusted by hundreds of students and have high ratings.
Python for Data Science, from freeCodeCamp
For a lot of newbies, Python is the go-to language. If you’ve chosen Python as your programming language, this free Python for Data Science course by freeCodeCamp is an amazing start to your data science career.
It covers the following topics: the basics of programming, why python, how to install anaconda and python, how to launch a jupyter notebook, how to code in the iPython Shell, variables and operators in python, booleans and comparisons in python, and more.
Link: Python for Data Science
Introduction to Programming with Python, from Harvard University
Harvard University offers an introductory programming course using Python. Although it’s with a university, it is a self-paced course which will take you roughly 10 weeks to complete. This course has been designed for students that have no prior knowledge of programming, and wish to build their Data Science skills using Python.
It covers the following topics: functions, variables, conditionals, loops, exceptions, libraries, unit tests, file I/O, regular expressions, object-oriented programming, and more.
Data Science: R Basics, from Harvard University
If you have chosen R as your programming language, it is always good to start with the basics. Harvard University offers a Data Science: R Basics course which helps you to build a solid foundation by learning and using the R programming language to wrangle, analyze, and visualize data.
The course is free; however, you can pay for a verified certificate for $149.
Link: Data Science: R Basics
Statistical Learning, from edX
Statistics is an important element of data science, and you must have your fingers wrapped around it. This Statistical Learning course by edX will provide you with the main tools used in statistical modeling and data science.
It covers the following topics: an overview of statistical learning, linear regression, classification, resampling methods, linear model selection and regularization, moving beyond linearity, tree-based methods, support vector machines, deep learning, survival modeling, unsupervised learning, and multiple testing.
Link: Statistical Learning
Statistics Fundamentals, from Josh Starmer
If you prefer to watch videos, I would highly recommend Josh Starmer's YouTube page to help you better grasp the fundamentals of statistics and probability. You will cover various topics that are related to data science with clean explanations and examples.
Link: Statistics Fundamentals
Machine Learning Specialization, from Coursera
This course has been put together by Andrew Ng, Founder & CEO of Landing AI, Founder of deeplearning.ai, and Co-Chairman and Co-Founder of Coursera. He has constructed a machine learning specialization made up of 3 courses:
- Supervised Machine Learning: Regression and Classification
- Advanced Learning Algorithms
- Unsupervised Learning, Recommenders, Reinforcement Learning
These courses are free; however, there is a fee if you wish to get certified.
Applied Machine Learning, from Andreas Mueller
Once you understand the fundamentals of machine learning, your next step will be to learn how to apply it. Andreas Mueller has a YouTube channel that has 22 videos on applying machine learning.
You will cover topics such as visualizations and matplotlib, linear models for regression, gradient boosting, model inspection, feature selection, and more.
Link: Applied Machine Learning
Feature Engineering, from Kaggle
Once you know how to build a model, you will want to learn how to improve your model by feature engineering. This feature engineering course provided by Kaggle helps you to learn how to get the most out of your data by using the feature to improve your model.
It covers: What is feature engineering, Mutual information, Creating features, Clustering with k-means, Principal component analysis, and Target encoding.
Link: Feature Engineering
Deep Learning Crash Course - freeCodeCamp
Suppose you want to go that extra mile and dive into the deep end of deep learning. I recommend this deep learning crash course for beginners provided by freeCodeCamp. It will give you a good overview of deep learning and its fundamental elements.
You will cover topics such as introduction to neural networks, activation functions, loss functions, regularization, convolutional neural nets, and more.
Data Management with Data Science, from The University of Wisconsin-Madison
If you have a particular interest in Data Management, this course by The University of Wisconsin-Madison is the course for you.
It is broken down into 6 sections: Introduction to Data Science, Relational Databases and Relational Algebra, The MapReduce Model and No SQL Systems, Predictive Analytics, Information Extraction and Data Integration, and Communicating Insights.
There are a lot of readily available resources to help you learn data science. It is always good to begin with the free resources and gain a good foundation, before moving on to paid or certified courses to help you land a job. However, many people have landed a job without paying for courses.
If you need more guidance on your data science path, read this: The Complete Data Science Study Roadmap.
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.