Top 10 Best Data Science Courses on Udemy [2021 Reviews]

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data science courses

Udemy is a popular marketplace for affordable courses on a huge range of subjects. This includes high-quality options for anyone looking to learn data science.

However, with so much choice it can be difficult to know which course to go for. Well, we are here to help.

In this top 10 review round-up, we have carefully selected 10 of the best data science courses currently running on Udemy in 2021.

Whether you are an expert looking to develop your skills further, or a beginner wanting to learn the basics (and other needs in between) you will find something here to suit you.

For our top choice courses at a glance, just head to the table below. For an in-depth review of each of the selected courses, keep on reading.

Top 10 Data Science Courses on Udemy

COURSE

TITLE

DETAILS

OUR RATING


BEST SHORT BEGINNER COURSE

Intro to Data Science: Your Step-by-Step Guide To Starting

5hrs of video
1 Resources

6 Articles
Lifetime Access
Certificate

Careers in Data Science A-Z

3.5hrs of Video

3 Articles
9 Resources
Lifetime Access
Certificate

BEST COMPLETE BEGINNER COURSE

The Data Science Course 2021: Complete Data Science Bootcamp

28.5hrs of video

501 Resources

90 Articles

12 Exercises
Lifetime Access
Certificate

BEST BEGINNERS LEARNING R

R Programming A-Z™: R For Data Science With Real Exercises!

10.5hrs of video

6 Resources
Lifetime Access
Certificate

BEST FOR BUSINESS APPLICATION

Data Science for Business | 6 Real-world Case Studies

11.5 hrs of video

1 Articles
13 Resources
Lifetime Access
Certificate

Machine Learning Entrepreneurship - Applied Data Science

4hrs of video

2 Articles
13 Resources

Lifetime Access
Certificate

BEST FOR PYTHON

Scientific Python: Data Science Visualization

18hrs of video

5 Articles
4 Resources
Lifetime Access
Certificate

BEST FOR INTERMEDIATES

R Programming: Advanced Analytics In R For Data Science

6hrs of video
5 Articles
Lifetime Access
Certificate

BEST FOR SPSS USERS

Advanced Data Science Techniques in SPSS

6.5hrs of video

9 Articles

8 Resources
Lifetime Access
Certificate

Advanced Data Science: Master Deep Web Experiment Analysis!

5hrs of video
4 Resources

13 Coding Exercises
Lifetime Access
Certificate

The Reviews

The following reviews focus on 10 of the best data science courses that are currently being offered on Udemy.

Best Data Science Courses for Beginners

1. TOP BEGINNER COURSE: Intro to Data Science: Your Step-by-Step Guide To Starting

1

This is a beginner-friendly introductory course to data science. It covers the basics of data science, with introductory lessons to statistical learning, database, and machine learning.

It also covers the entire data science process, which is the process from data collection, data cleaning, and data analysis, as well as modeling and validation of data. It also introduces the learner to use Python programming language for data science tasks.

This short course has 7 sections that cover 42 lectures. Regarding the duration of the course, all the sections add up to about 5.5 hours. The course contents are video lectures lasting for 5 hours, 6 articles, and a downloadable resource.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in August 2021, which makes it the latest introductory data science course for beginners. At the time of writing this review, 11,502 students have taken the course. 2,697 students have given the course a rating of 4.4.

Key aspects of this course are explained below.
  • Target Audience: Anyone interested in data science, as well as beginners in Data Science.
  • Course Type: Tutorials.
  • Rating: 4.4 out of 5 following 2,697 ratings.
  • Language: English, with videos featuring English subtitles.

Requirements

  • Internet-enabled Personal Computer (PC).

The Tutors

This course is created by Hadelin de Ponteves, Kirill Eremenko, and the Ligency Team.

Hadelin de Ponteves is an expert in Artificial Intelligence (AI) and co-founder of BlueLife AI, and Kirill Eremenko is a mathematics graduate who works as a consultant for data science management.

The Ligency Team is a marketing and public relations team that works with clients who need data science services.

Course Content

The key topics covered in the course include the following:
  • Introduction to data science.
  • Data science with Tableau.
  • Data science with Python.
  • Data science with the cloud.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

2. Careers in Data Science A – Z

2

This is a short course about careers that are available to data scientists and introduces data sciences to absolute beginners. It serves as an introductory course for data science for real-world tasks.

It also contains lessons about how a data scientist should market him/herself to prospective clients. It also explains the importance of getting accreditation through certified courses.

This mini-course has 8 sections that cover 52 lectures. Regarding the duration of the course, all the sections add up to about 4 hours. The course contents are video lectures lasting for 3.5 hours, 3 articles, and 9 downloadable resources.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in August 2021, which makes it the latest introductory data science course for beginners. At the time of writing this review, 10,864 students have taken the course. 2,191 students have given the course a rating of 4.4.

Key aspects of this course are explained below.
  • Target Audience: Anyone interested in data science, as well as beginners in Data Science.
  • Course Type: Tutorials.
  • Rating: 4.4 out of 5 following 2,191 ratings.
  • Language: English, with videos featuring English subtitles.

The Tutors

This course is created by Hadelin de Ponteves, Kirill Eremenko, and the Ligency Team. The biographies of these 3 creators have been highlighted in the previous course.

Requirements

Internet-enabled PC.

Course Content

The key topics covered in the course include the following:
  • Introduction to data science.
  • Requirements to become a data scientist.
  • How to become an outstanding data scientist.
  • Job options.
  • How to promote oneself.
  • How to handle job interviews.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

3. BEST COMPLETE COURSE: The Data Science Course 2021: Complete Data Science Bootcamp

3

This is a comprehensive data science Bootcamp for absolute beginners. Unlike the 2 afore-reviewed courses, this is a long course. It also covers the 2 domains for the 2 afore-reviewed courses. This means that this course provides an introduction to data science, as well as covers careers that are available to data scientists.

This course covers the mathematics needed for statistics and machine learning, and the data science process. Also, it introduces the learner to using Python programs to perform statistical regressions.

Additionally, it provides introductory lessons on machine learning algorithms, deep neural networks, and deep learning frameworks like TensorFlow.
This comprehensive course has 63 sections that cover 476 lectures.

Regarding the duration of the course, all the sections add up to about 29 hours. The course contents are video lectures lasting for 28.5 hours, 90 articles, and 501 downloadable resources.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in July 2021, which makes it a recently updated data science course for beginners.

At the time of writing this review, 420,242 students have taken this popular course. 95,874 students have given the course a rating of 4.6; and this makes it a best-seller course.

Key aspects of this course are explained below.
  • Target Audience: Anyone interested in data science, as well as beginners in Data Science.
  • Course Type: Tutorials with exercises.
  • Rating: 4.6 out of 5 following 95,874 ratings.
  • Language: English, with videos featuring autogenerated English, German, Portuguese, French, Spanish, Polish, Italian, and Indonesian subtitles.

The Tutors

This course is created by the 365 Careers Team. This team specializes in creating and teaching data science courses.

Requirements

Internet-enabled PC.

Course Content

The key topics covered in the course include the following:
  • Introduction to data science.
  • Requirements to become a data scientist.
  • How to become an outstanding data scientist.
  • Probability.
  • Statistics.
  • Data science with Python.
  • Machine learning for data science.
  • Deep learning for data science.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

4. BEST FOR BEGINNERS LEARNING R: R Programming A-Z, R For Data Science With Real Exercises!

4

This is an introductory course on how to use R programming for data science. As expected, it is more focused on how to use R Studio and how to write code in R.

It also covers the basics of data science including the data science process, and statistical analysis, as well as how to use selected software packages and GGPlot2. It also has lessons on Laws of Large Numbers, along with exercises.

This specialist introductory course has 8 sections that cover 82 lectures. Regarding the duration of the course, all the sections add up to about 11 hours. The course contents are video lectures lasting for 10.5 hours and 6 articles.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in August 2021, which makes it a recently updated specialist data science course. At the time of writing this review, 213,796 students have taken this popular course. 42,069 students have given the course a rating of 4.6, and this makes it a best-seller course.

Key aspects of this course are explained below.
  • Target Audience: Beginners in Data Science.
  • Course Type: Tutorials with exercises.
  • Rating: 4.6 out of 5 following 42,069 ratings.
  • Language: English, with videos featuring autogenerated English, German, Portuguese, Japanese, French, Romanian, Spanish, Turkish, Polish, Italian, and Indonesian subtitles.

The Tutors

This course is created by Kirill Eremenko and the Ligency Team. Summaries of each of their biographies have been provided in the first course reviewed here.

Requirements

  • Internet-enabled PC.

Course Content

The key topics covered in the course include the following:
  • Introduction to data science.
  • Installation of R Studio and R packages.
  • Core R programming principles.
  • Functions and packages of R.
  • Data frames.
  • Matrices.
  • GGPlot2 for data visualization.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

Best Intermediate-Level Data Science Courses

5. BEST FOR BUSINESS APPLICATION: Data Science for Business | 6 Real-world Case Studies

5

This is an intermediate-level course for learners who have completed introductory courses in data science, and want to try to build models for managing data in business settings.

This course comes with 6 case studies based on real-world application of data science concepts.

The learner is expected to build AI, machine learning (ML), and natural language processing (NLP) models that can be used to process data from different business operations such as sales, marketing, and public relations.

This 12-hour long course has 7 sections that cover 73 lectures. The course contents are video lectures lasting for 11.5 hours, an article, and 13 downloadable resources.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in February 2021. At the time of writing this review, 6,427 students have taken this popular course. 682 students have given the course a rating of 4.6, and this makes it a best-seller course.

Key aspects of this course are explained below.
  • Target Audience: Students who have a basic understanding of data science.
  • Course Type: Tutorials with exercises.
  • Rating: 4.6 out of 5 following 682 ratings.
  • Language: English, with videos featuring autogenerated English subtitles.

The Tutors

This course is created by Ryan Ahmed, Mitchell Bouchard, and the Ligency Team. Ryan Ahmed is a professor of mechanical engineering who specializes in mechatronics and has a deep understanding of AI.

Mitchell Bouchard of Red Cape Learning specializes in creating online courses. Stemplicity is also involved in this course, where it provides support for managing question and answer sessions.

Requirements

  • Internet-enabled PC.
  • Basic understanding of data science process and concepts.

Course Content

The key topics covered in the course include the following:
  • Review of data science concepts.
  • Building a model for the production department
  • Building a model for the marketing department
  • Building a model for the human resources department.
  • Building a model for the sales department.
  • Building a model for the public relations department.
  • Building a model for the operations department.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

6. Machine Learning Entrepreneurship – Applied Data Science

6

This is a short intermediate-level course for learners who want to build web applications for handling data, and have these applications embedded in websites behind paywalls.

The focus of this course is how to use data science and machine learning to build applications that can be used in real-life situations.

As expected, the learner is required to have an understanding of some programming languages, with the main language required for this course being Python.

This 4-hour long course has 6 sections that cover 20 lectures. The course contents are video lectures lasting for 4 hours, 2 articles, and 13 downloadable resources. Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in March 2020. At the time of writing this review, 253 students have taken this course. 36 students have given the course a rating of 4.8.

Key aspects of this course are explained below.
  • Target Audience: Students who have a basic understanding of data science and Python programming.
  • Course Type: Tutorials with exercises.
  • Rating: 4.8 out of 5 following 36 ratings.
  • Language: English, with videos featuring autogenerated English subtitles.

The Tutors

This course is created by Manuel Amunategui, who is a quantitative developer and data scientist which over 20 years’ experience in the technology industry.

Requirements

  • Internet-enabled PC.
  • Basic understanding of data science process and concepts.
  • Python programming skills.

Course Content

The key topics covered in the course include the following:
  • Setup of Python IDE and Local Flask.
  • Building application for predicting earthquakes and comes with Google Maps.
  • Building application for making recommendations about the stock trade market.
  • Building a paywall that manages subscription-based access to web resources.
  • Building a plagiarism defender web application.

This course can be accessed via an internet-enabled PC, smartphone, internet-enabled mobile device, or Smart TV.

7. BEST FOR PYTHON: Scientific Python: Data Science Visualization Bundle 18 Hrs!

7

This is a comprehensive intermediate-level data science course that focuses on how to use Python-based software packages to manage data science tasks.

The packages covered in the course are MatPlotLib, NumPy, SciPy, Pandas, Seaborn, PyTorch, Sci-kit Learn, and REGEX. It also provides introductory lessons on how Python programming language can be used to code programs for handling data.

Likewise, this course requires one to Scientific Python, and as expected introduces the learner to its Python tools.

This 18-hour long course has 10 sections that cover 87 lectures. The course contents are video lectures lasting for 18 hours, 5 articles, and 4 downloadable resources.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in April 2021. At the time of writing this review, 12,468 students have taken this course. 55 students have given the course a rating of 3.8.

Key aspects of this course are explained below.

Target Audience: Students who have a basic understanding of data science and Python programming.
Course Type: Tutorials with exercises.
Rating: 3.8 out of 5 following 55 ratings.
Language: English.

The Tutors

This course is created by the Scientific Programmer™ Team and uses the learning platform of the Scientific Programming School. The Team focuses on teaching courses about scientific programming languages.

Requirements

  • Internet-enabled PC.
  • Basic understanding of data science process and concepts.
  • Python programming skills.

Course Content

The key topics covered in the course include the following:
  • Setup of Scientific Python.
  • Python Bootcamp.
  • Array processing with NumPy.
  • Data frames and processing with Pandas.
  • Plotting and visualization with Matplotlib.
  • Regular expressions with REGEX.
  • Machine learning with Scikit-learn.
  • Statistical plots with Seaborn.
  • PyTorch for TensorFlow projects.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

8. BEST FOR INTERMEDIATES: R Programming: Advanced Analytics In R For Data Science

8

This is an intermediate-level R programming course for data science that can be taken after one completes the introductory R Programming for Data Science course provided by Kirill Eremenko and the Ligency Team.

This course is created by the same duo, and it covers advanced R Studio skills for data and statistical analysis. It also trains the learner on how to apply a family of functions and factual analysis method.

This specialist course has 5 sections that cover 53 lectures. Regarding the duration of the course, all the sections add up to about 6 hours.

The course contents are video lectures lasting for 6 hours and 5 articles. Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in August 2021, which makes it a recently updated specialist data science course. At the time of writing this review, 51,733 students have taken this popular course. 7,259 students have given the course a rating of 4.7, and this makes it a best-seller course.

Key aspects of this course are explained below.
  • Target Audience: Students who have a basic understanding of data science and R programming.
  • Course Type: Tutorials with exercises.
  • Rating: 4.7 out of 5 following 7,259 ratings.
  • Language: English, with videos featuring autogenerated English, German, Portuguese, French, Romanian, Spanish, Polish, Italian, and Indonesian subtitles.

The Tutors

This course is created by Kirill Eremenko and the Ligency Team. Summaries of each of their biographies has been provided in the first course reviewed here.

Requirements

  • Internet-enabled PC.
  • Basic understanding of data science process and concepts.
  • R programming skills.

Course Content

The key topics covered in the course include the following:
  • Functions and packages of R.
  • Data preparation with R.
  • Data frames.
  • List with R.
  • GGPlot2 for data visualization.
  • Family of functions.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

Best Advanced Level Data Science Courses

9. BEST FOR SPSS USERS: Advanced Data Science Techniques in SPSS

9

These advanced data science courses focus on how to use the statistics platform, Statistical Package for the Social Sciences (SPSS), for high-level data analysis.

The learner is taught how to use the KNN technique, non-binary CHAIN trees, and predictor selection techniques.

Also, one is taught how to build a neural network based on the radial basis function (RBF). Moreover, one will be able to build the multilayer perception.

This 7-hour long course has 14 sections that cover 87 lectures. The course contents are video lectures lasting for 6.5 hours, 9 articles, and 8 downloadable resources.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.
This course was last updated in December 2020.

At the time of writing this review, 25,071 students have taken this course. 153 students have given the course a rating of 4.5.

Key aspects of this course are explained below.
  • Target Audience: Students who have an understanding of data science, statistics, and basic SPSS knowledge.
  • Course Type: Tutorials with exercises.
  • Rating: 4.8 out of 5 following 153 ratings.
  • Language: English.

The Tutor

This course is created by Bogdan Anastasiei, an assistant professor of business administration.

Requirements

  • Internet-enabled PC.
  • Understanding of statistics and data science.
  • SPSS Knowledge.

Course Content

The key topics covered in the course include the following:
  • Linear regression techniques.
  • Regression analysis.
  • Decision trees.
  • Growing binary trees.
  • Growing non-binary trees.
  • Neural networks.
  • Multilayer perceptron.Two-step cluster analysis.
  • RBF neural network.
  • Survival analysis.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

10. Advanced Data Science: Master Deep Web Experiment Analysis!

10

This is an advanced data science course that trains one to perform deep experiment analysis using methods designed to work with Tableau and SQL. The learner is taught advanced experiment analytics and strategy for exploring experimental metrics, as well as how to build an analytics dashboard.

This is a hands-on course for students who programmers because it features coding exercises.

This 5-hour long course has 5 sections that cover 27 lectures. The course contents are video lectures lasting for 5 hours, 4 articles, and 13 coding exercises.

Upon course completion, one is awarded a certificate of completion and is given full lifetime access to course contents.

This course was last updated in August 2016. At the time of writing this review, 702 students have taken this course. 41 students have given the course a rating of 3.3.

Key aspects of this course are explained below.
  • Target Audience: Students who have an understanding of data science, statistics, SQL, Tableau, and UNIX.
  • Course Type: Tutorials with exercises.
  • Rating: 3.3 out of 5 following 41 ratings.
  • Language: English.

The Tutor

This course is created by Larry Wai, who is a data scientist working at Chegg.

Requirements

  • Internet-enabled PC.
  • Understanding of statistics and data science.
  • Knowledge of SQL, Tableau, and UNIX.

Course Content

The key topics covered in the course include the following:
  • SQL review.
  • Creating statistical units.
  • Data visualization.
  • Experimental metrics.
  • Experiment integrity analysis.
  • Deep experiment analytics.

The lectures can be accessed using an internet-enabled PC, Smart TV, or mobile device, including smartphones and tablets.

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