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An In-depth Guide To The Best 8 Online Data Science Courses For 2022

Writer : Mr. George Miguel

An in-depth guide to the best 8 online data science courses for 2022.

For over a decade, I've spent over 100 hours watching course videos, participating in quizzes and assignments, and reading reviews on various aggregators and forums to come up with the best data science courses on the market.

The TL;DR is that this is a lengthy article, so here's the summary:

 

The 8 Best Data Science Courses and Certifications for 2022 are as follows:

  • Applied Data Science with JHU at Coursera
  • The Metis Applied Data Science with Python Specialization at the University of Michigan at Coursera: An Introduction to Data Science
  • University of California, San Diego's Data Science MicroMasters program at edX DataQuest's Statistics and Data Science MicroMasters program and Harvard's CS109 Data Science course are also available at edX.
  •  

Criteria

I've narrowed down the options to those who are just getting started in data science, so I've only included courses that meet the following standards:

  • The course covers all aspects of data science.
  • The course makes use of well-known open-source software development resources.
  • They teach students about the most commonly used algorithms in machine learning.
  • Theory and practice are well integrated in the course.
  • Either the course should be available on-demand or on a monthly basis at the very least.
  • This course includes hands-on tasks and projects.
  • The teachers are enthusiastic and friendly.
  • Ratings are generally higher than or equal to 4.5/5 for the course.

If you're looking for the best data science courses, you'll need to do some serious filtering now that there are so many options. In order to become a data scientist, you'll need to put in a lot of time and effort over the course of several months.

My list of top general data science courses includes a separate section for more specific interests, such as Deep Learning and SQL. This is a list of courses that focus on a specific aspect of data science, but they are still among the best options. These extra picks can be served as a pre- or post-meal snack, as well as during the meal itself.

See my complementary article on the best machine learning courses of the year if you're more interested in learning about machine learning in general.

 

Tools for learning that you should make use of

Learners of data science online must not only understand what they're doing, but also practice using data science on a variety of different problems.

You should also read the following two books in addition to the courses on this page:

 

  • One of the most widely recommended books for newbies in data science, Introduction to Statistical Learning is available for free. provides an introduction to the fundamentals of machine learning and the underlying principles at work
  • An in-depth look at how to apply predictive modeling to real-world datasets, with a wealth of practical advice along the way.
  •  

It is much more beneficial to study with the help of these two textbooks than to simply take courses on their own. If you're able to grasp most of what's in the first book, you'll be a better data scientist than most nascent ones.

A great learning experience would be to work through both of these books in R and then convert them into Python, as both of the exercises and examples use R.

 

1. JHU @ Coursera's Data Science Specialization —

Courses in this series are some of the most popular and well-received on here. JHU did an outstanding job of balancing the curriculum's breadth and depth. This series includes a section on statistics that is missing from many data science courses, which is the foundation of data science.

In general, the Data Science specialization is a good mix of theory and practical application using the R programming language.. Among other things, you should have some programming experience and a solid grasp of Algebra as prerequisites for this course. Although prior knowledge of linear algebra and/or calculus is not required, it is beneficial.

Certificate and graded materials are available for free or for $49 a month.

Johns Hopkins University serves as the service provider.

 

Curriculum:

  • R Programming is included in the Data Scientist's Toolbox.
  • Experimentation with data collection and cleaning
  • Statistical Inference Regression Models That Can Be Reproduced
  • Machine Learning in the Real World
  • Creating Data-Based Solutions
  • Thesis in Data Science
  •  

The Statistics with R Specialization can help you brush up on your statistics skills and/or learn more about R.

 

2. Metis: An Introduction to Data Science

Highly rated on both SwichUp and CourseReport (4.9/5 on each site) and taught in real time by an experienced company data scientist, this course is sure to impress. Six-week course that covers the entire data science process and is the only live online one on this list. " In addition to a certificate of completion, you'll earn CEUs for completing this course because it's accredited.

Two nights a week, you'll learn data science in the same way you would in an online college course. Additionally, during office hours the instructor spends additional time helping students who may be having difficulties.

750 dollars is the cost.

The course of study:

 

  • a solid foundation in mathematics, statistics, and linear algebra is a must. In a nutshell:
  • Analysis and visualization of exploratory data sets
  • Structured vs. Unstructured Learning and evaluating models
  • Features, Engineering and Data Pipelines: Data Modelling
  • Supervised and Unsupervised Learning in Data Modeling
  • Data Modeling: Advanced Model Evaluation and Data Pipelines - Presentations.
  •  

This course requires knowledge of Python, linear algebra, and a basic understanding of statistics as pre-requisites. We also have Beginner Python and Math for Data Science, which is an online course for those who need to brush up on their Python and math skills for data science. If you're looking for the best Python course, check out my article on the subject.

It's the University of Michigan at Coursera's "Applied Data Science with Python Specialization."

It's no surprise that the University of Michigan, which recently launched an online Master's degree program in data science, is the source of this outstanding specialization in the field. This means that you'll get a solid grounding in the most commonly used Python libraries for data science, such as matplotlib, pandas, nltk, scikit-learn, and networkx, and learn how to apply them to real data.

There are no statistics or machine learning algorithm derivations included in this series, but it does provide a thorough explanation of how to use and evaluate the algorithms in Python. As a result, I believe this tutorial is better suited to those who are already familiar with R and/or are learning statistical concepts in another setting.

You may want to take the Statistics with Python Specialization first if you're rusty with statistics. Many of the statistical skills required for data science will be taught in this course.

Certificate and graded materials are available for free or for $49 a month.

University of Michigan Courses as a Service Provider:

 

  • Plotting, Charting, and Data Representation in Python Machine Learning in Python Text Mining in Python Social Network Analysis in Python: An Introduction to Python Data Science

There are a couple of excellent lectures in the first course that cover the more advanced Python features you'll need to process data effectively before enrolling in these courses.

Four. UC San Diego's Data Science MicroMasters program at edX.

Advanced, graduate-level courses from edX can be applied toward a real Master's degree at some institutions. The Rochester Institute of Technology's Master of Science in Data Science degree can be completed in 30 percent of the time with this MicroMaster's (RIT).

Prerequisites for these courses are more demanding than for many of the other options on this list because they are designed for students who plan to pursue a master's degree. Since this series' first course does not cover basic Python concepts, you should already be familiar with programming. For the first course, it would probably help if you spent some time on a platform like Codecademy.

A good balance of theory and practice was found in this MicroMaster's course. The lectures cover a lot of ground, but they also include plenty of examples from the real world.

Certificate and graded materials are available at a cost of $1,260.

UC San Diego Courses as a Service Provider:

 

  • For Data Science, Python
  • using Python Machine Learning Fundamentals for Probability and Statistics in Data Science
  • Spark: A Big Data Analytical Engine
  •  

As with many courses on edX, this MicroMaster's and others aren't offered as often as they could be. It's a good idea to sign up for the first course if your schedule allows it.

5. Dataquest

Even if you don't take any of the other courses on this list, you can use Dataquest to supplement your online education.

Rather than using videos, Dataquest uses an interactive textbook to teach. If you're interested in learning about data science, you'll have access to interactive coding tutorials for each topic.

There are many interactive platforms, but Dataquest stands out for three reasons: a comprehensive curriculum, the opportunity to work on real-world data science projects, and the availability of an active and supportive Slack community.

In order to learn data science with Python, the platform offers only one main curriculum:

 

The Python Pathway for a Data Scientist

At the time of writing, there are 31 courses in this track, ranging from the basics of Python to advanced topics such as statistics, mathematics for machine learning, and deep learning. For a better learning experience, the curriculum is constantly being revised and updated.

Premium content costs $49 a month; Basic content is free.

The following is a condensed outline of the course material:

 

  • Data science libraries for Python - Pandas, NumPy, Matplotlib, and more
  • Storytelling and Visualization
  • Cleaning and exploratory data analysis that is effective
  • Use of command-line and Git for data science projects
  • Web scraping and SQL APIs, from the basics to the advanced
  • Mathematical probability and statistical analysis Calculus and Linear Algebra for Machine Learning - Intermediate to Advanced
  • Using Python for Machine Learning - Regression, K-Means Clustering, Decision Trees, and Deep Learning
  • Processing of Natural Language
  • Map-Reduce and Spark

The curriculum is also littered with data science projects, as well. In order to gain experience working on an end-to-end data science strategy, you'll complete a series of projects that put what you've learned to use.

Finally, if you'd like to learn more about data science with R, take a look at the new Data Analyst in R path offered by Dataquest. You can learn R or Python (or both!) on Dataquest's platform if you purchase a subscription.

 

6. MIT @ edX MicroMasters in Statistics and Data Science

For those who want to understand data intuitively, the inclusion of probability and statistics courses in this series from MIT makes it a well-rounded curriculum This MIT MicroMaster's class spends more time on statistics than the one from UC San Diego that was just mentioned.

Because of the difficulty of this task, prior knowledge of single and multivariate calculus and Python programming is required. If you're unfamiliar with Python, they recommend taking Introduction to Computer Science and Programming Using Python to get a feel for the language before diving into the ML portion. Check out Codecademy's Python track if you prefer an interactive platform to learn Python.

For free, or $1,350 for a certificate and graded materials,

University of Michigan Courses as a Service Provider:

 

  • An Introduction to Probability and Data Analysis in Social Science: A Test of Your Understanding
  • The Capstone Exam in Statistics and Data Science: Machine Learning with Python from Linear Models to Deep Learning
  •  

One final exam will round out the ML course, which includes a variety of interesting projects to work on throughout the semester.

7. Harvard CS109 Data Science

This Harvard course is a great place to start if you're a beginner because it has a good balance of theory and practice. However, you won't receive any sort of certification and it is not on an interactive platform like Coursera or edX.

Curriculum:

 

  • Python, Pandas, Regular Expressions, Data Transformations
  • Experimentation with a Large Sample
  • Tables, Queries, and the Grammatical Grammar of Statistical Modeling
  • Using Storytelling to Improve Your Communication Skills
  • Bias and regression classification, kNN, cross-validation, dimensionality reduction, PCA, MDS SVM, evaluation, decision trees and random forests, ensemble methods, best practices recommendations MapReduce, Spark Bayes Theorem, Bayesian Methods, and The Grouping of Textual Data
  • Delivering an Impactful Presentation
  • This is a test run.
  • Networks at a Deep Level
  • Forging an Analytical Foundation
  •  

This course makes extensive use of Python and the various data science libraries in order to tackle challenging real-world problems. This is the only data science course you'll find that covers everything you need to know about the field.

You can also check out Udemy's Python for Data Science and Machine Learning Bootcamp.

This is an excellent value for the money. Python, visualization, and statistical learning concepts are well-explained by the instructor in this course. Assignments are a huge advantage of this course over other Udemy courses. If you're having trouble grasping a concept, the instructor will walk you through the solution in a video after you finish your Jupyter notebook workbooks.

Curriculum:

 

  • A Quick Guide to Python
  • Numpy, Pandas, and Python for Data Analysis
  • Using Matplotlib, Seaborn, Plotly and Cufflinks for data visualization in Python
  • The Data Capstone Regression, kNN, Trees and Forests, SVM, K-Means, and PCA are some of the techniques used in this project.
  • Systems that Make Suggestions
  • Processing of Natural Language
  • Spark is a tool for handling massive amounts of data.
  • Deep Learning and Neural Nets.
  •  

There is no statistics section in this course because it is more practical in nature. It's a good idea to take this course along with a separate one on statistics and probability if you're considering taking it.

Another Udemy course, Data Science A-Z, gets an honorable mention. In spite of the fact that Data Science A-Z has a comprehensive coverage, I don't believe it meets the criteria of Python for Data Science and Machine Learning Bootcamp because it uses tools outside the Python/R ecosystem.

 


 

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