Top Online Courses for Data Science

This article will review the top online data science courses, including Coursera and Data Camp courses.

Shortlist for Top Online Courses for Data Science 2022

1. Applied Data Science with Python Specialization – University of Michigan
2. Data Science Specialization – John Hopkins University
3. Data Science A-Z: Real-Life Data Science Exercises Included – Kirill Eremenko & SuperDataScience Team
4. CS109 Data Science – Harvard
5. Dataquest
6. Datacamp

Complementary Books

Using online courses comes with the risk of covering a wide range of topics without enough depth. To make the most out of your online learning experience, pair your chosen course with one or more books covering the course material in more detail. Visit my blog post recommending the best books for the machine learning concepts that underpin all of the listed courses.

Applied Data Science With Python Specialization – University of Michigan

• Price: Free to audit or $49/month for certification and marked assignments

Courses:

• Introduction to Data Science in Python
• Applied Plotting, Charting, & Data Representation in Python
• Applied Machine Learning in Python
• Applied Text Mining in Python
• Applied Social Network Analysis

This course is easy to follow, with a significant focus on application. Each course is divided into four weeks, meaning the entire specialization is estimated to take 20 weeks. It is an intermediate-level specialization, where the ability to write programs in Python is assumed, as well as basic knowledge of statistics. It is strongly project-driven, with theory kept to a minimum. So you can immediately apply the knowledge gained from the lectures and recommended reading materials. Discussion forums and mentors are available to help guide you through the course.

This course would be particularly beneficial for those finishing a degree program and wanting to specialize in data science. It is recommended that you pair this course with a more theoretical course if you do not have experience with machine learning, such as Andrew Ng’s Machine Learning course, which I include on this page.

If you are looking for a course to build your knowledge of statistics, this course will not provide this. Therefore, I recommend having Statistical Learning as a book companion as you take the course.

Data Science Specialization – John Hopkins University

• Price: Free to audit or $49/month for certification and marked assignments.

Courses:

• The Data Scientist’s Toolbox
• R Programming
• Getting and Cleaning Data
• Exploratory Data Analysis
• Reproducible Research
• Statistical Inference
• Regression Models
• Regularization
• Practical Machine Learning
• Developing Data Products
• Data Science Capstone

This specialization has a broad scope on the possible curriculum for data science and is rigorous in terms of building and delivering a data science pipeline. However, given the breadth of topics covered, you should do supplementary reading alongside the course for both statistics and machine learning. The course can be completed on a scale of six months to a year. This course has open-ended course projects to take advantage of that are portfolio-worthy. Invest in the projects to get the most out of it. The programming language used is R. I would advise some familiarity with the basics of the language. You can find the best courses to learn R here and use Advanced R Programming by Hadley Wickham as a book companion.

Data Science A-Z: Real-Life Data Science Exercises Included – Kirill Eremenko & SuperDataScience Team

• Price: £44.99, but there are typically deals.

Courses

• Visualization
• Modelling
• Data Preparation
• Communication

This course contains over two hundred lectures and more than twenty hours of content. It is one of the best-structured series for data science and provides the flexibility to select the module you want. It is perfect as an introduction to data science and allows you to learn how to use Tableau for visualization, SQL for database management, SSIS, and Gretl for statistical inference. As this course is introductory and focused on applications, you should do additional reading on the topics you feel could have been explored further.

CS109 Data Science – Harvard

• Price: Free

Harvard CS109 is a well-known and highly-rated university course for data science. The class material describes the “data science process” and breaks it up into five key concepts:

• Data Wrangling
• Data Management
• Exploratory Data Analysis
• Statistical Inference
• Communication

Python is the language used for assignments and projects; you should have working-level knowledge of Python to make the most of the course material. To get the most out of the content, you should focus on solving the problem sets. These are all available in Jupyter Notebook format: see cs109/content. You can also access material from previous years. CS109 will be more intuitive with an increasing understanding of statistics and programming experience. I recommend Python for Data Analysis as a book companion for the problem sets.

Dataquest

Price: Free to try. Basic plan $29/month, Premium plan $49/month

Dataquest offers a different experience to the traditional online learning experience. Instead of extensive lecture videos alongside problem sets, Dataquest operates like an interactive textbook. It aims to teach the student to be autonomous by leveraging project-based learning. It ensures you work through concepts instead of skimming through videos to build your understanding. The material and problem sets can be done via the student’s browser on the Dataquest learning interface. The data scientist course assumes a basic level of mathematics and Python and gradually builds your confidence and skills to perform statistical inferences and use machine learning algorithms. There is the opportunity to dive into relevant mathematics topics such as probability, calculus, and linear algebra. You will go through the fundamental algorithms for machine learning, including:

• K-Means Clustering
• Decision Trees
• Neural Networks
• Linear and Logistic Regression
• K-Nearest Neighbours

In addition to the course, Dataquest has a great blog with plenty of great data science tips, which you can find here. Within every subscription level, you can participate in the Slack community, which is very active. Premium subscription students can also get access to career counselling and CV advice if you find that you are someone who switches off lectures and wants a fully interactive learning experience. Dataquest is one of the best options.

Datacamp

• Pricing is Free, Basic $25/mo, Premium $250/yr. Increasing the subscription price gives you access to more courses, coding challenges, projects and support.

Datacamp combines short video lessons with problem sets that require you to fill in the blanks and projects. Upon signing up, you can access a Course library, which contains over 340 courses, including:

Courses:

• Introduction to Python
• Introduction to R
• Introduction to SQL
• Introduction to Data Engineering
• Data Science for Everyone
• Introduction to Tableau (visualization)

You can design your course and target the skills you want to build. An instant feedback loop in the platform, called Practice mode, allows you to perform exercises repeatedly. Datacamp focuses on keeping exercises short and combining them with to-the-point video lessons. Within the paid plan, you can access the Slack community, and a community page is accessible to everyone. If you are a visual learner and are just starting your data science adventure, Datacamp will allow you to learn and advance quickly. However, I would advise you to enroll in a more in-depth course with a strong emphasis on programming once you have completed the course.

How To Use These Courses

Each course has its strengths and weaknesses. The common factor for all of them is not all of the material explored will go into equal depth. Some modules may leave you wanting, and some will be too extensive for your level. To make the most out of the available courses, combine several of them and use supplementary material where it is necessary to boost your knowledge. The key to online learning is autonomy. Think of book companions or lecture notes as friends you can refer to if you missed something during your online course. Do not expect everything to be fed into your brain passively; you will have to go outside of the curriculum for your chosen course or do preparation beforehand. The more effort you put into finding what you learn from best and what level you are at, the more you will extract from the listed courses.

Here is a video highlighting the best ways to make the most out of your online course.

Data science is a rapidly evolving and exciting field. You must invest a substantial part of your time and be dedicated to learning the full range of knowledge bases to be a good data scientist. If you are an academic building your knowledge of data science concepts, visit my post, which provides tips for academic readers who want to start a career in data science. You can also click here to access the best courses covering machine learning, Python, and R. Have fun while learning and enjoy your journey!