Data Science Online Courses – Top Free Data Science Online Courses for 2023

It can be challenging to keep your New Year’s resolutions. It becomes even more challenging when you change careers and pick up new skills. Entering a field you have no prior knowledge of can be intimidating.

You can get over that fear and keep your New Year’s resolutions by having the appropriate resources available.

If you want to start a career in data science but aren’t sure which programs, publications, or boot camps to join. To help you develop that new skill, this article will provide you with a list of FREE online data science courses.

Free Courses To Excel Within 2023

The majority of students have given these courses high ratings and they are all free.

1. Python For Data Science, From FreeCodeCamp

Python is the language of choice for many beginners. This free Python for Data Science course offered by freeCodeCamp is a fantastic place to start if Python is your preferred programming language.

The fundamentals of programming, why Python, how to set up Anaconda and Python, how to open a Jupyter Notebook, how to program in the iPython Shell, variables and operators in Python, booleans and comparisons in Python, and more are all covered in this article.

2. Introduction To Programming With Python, From Harvard University

An introductory Python programming course is available at Harvard University. Even though a university offers the course, you can complete it on your schedule in about 10 weeks.

Students who wish to develop their Python-based Data Science skills have taken this course, intended for those without prior programming experience.

Functions, variables, conditions, loops, exceptions, libraries, unit tests, file I/O, regular expressions, object-oriented programming, and more are among the subjects it covers.

3. Data Science: R Basics, From Harvard University

It’s wise to start simple when learning a new programming language, and this is especially true if you’ve chosen R. A course called “Data Science: R Basics” is available through Harvard University, and it will teach you how to work with, analyze, and visualize data using the R programming language.

The course is free; however, you can pay for a verified certificate for $149.

4. Statistical Learning, From edX

Data science depends heavily on statistics, so you must be well-versed in this field. You will learn the key techniques for statistical modelling and data science in this edX course on statistical learning.

An overview of statistical learning, linear regression, classification, resampling techniques, linear model selection and regularization, moving beyond linearity, tree-based techniques, support vector machines, deep learning, survival modelling, unsupervised learning, and multiple testing are some of the subjects it covers.

5. Statistics Fundamentals, From Josh Starmer 

To help you better understand the foundations of statistics and probability, I highly recommend Josh Starmer’s YouTube channel if you prefer to watch videos. You will provide clear explanations and examples for various data science-related topics.

6. Machine Learning Specialization, from Coursera

Andrew Ng, Co-Chairman and Co-Founder of Coursera, Founder of deeplearning.ai, and Founder of Landing AI, put together this course. 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 for getting certified.

7. Applied Machine Learning, From Andreas Mueller

Your next step will be to learn how to apply machine learning once you fully grasp its fundamentals. There are 22 videos on using machine learning on Andreas Mueller’s YouTube channel.

You will study subjects like Matplotlib visualizations, linear regression models, gradient boosting, model inspection, feature selection, and more.

8. Feature Engineering, from Kaggle

Once you have a model built, you should learn feature engineering to enhance it. You can learn how to make the most of your data by using the feature to enhance your model by taking this Kaggle feature engineering course.

It addresses the following topics: Mutual information, feature creation, k-means clustering, principal component analysis, and target encoding are some of the techniques used.

9. Deep Learning Crash Course – freeCodeCamp

Let’s say you want to go above and beyond and plunge into the depths of deep learning. I suggest taking this freeCodeCamp beginner’s deep learning crash course. It will give you a thorough understanding of deep learning and its essential components.

You will study subjects like convolutional neural networks, regularization, activation, and loss functions.

10. 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. 

Predictive analytics, Relational Databases and Relational Algebra, The MapReduce Model and No SQL Systems, Information Extraction and Data Integration, and Communicating Insights comprise this guide.

In Conclusion

Many easily accessible resources can assist you in learning data science. Before enrolling in paid or certified courses to help you land a job, starting with free resources and building a solid foundation is always wise. However, a lot of people have found employment without having to pay for courses.

If you need more guidance on your data science path, read 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 about 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.