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3 Superstar Data Science Instructors

3 world-class massive open online course (MOOC) instructors that I learned from in my journey to data science

I. Introduction

Every beginner trying to learn the fundamentals of data science is often faced with the following questions:

  1. What data science courses should I take and in what order?
  2. What platform should I take data science courses from, edX, Coursera, Udemy, DataCamp, etc?
  3. What are the best data science massive open online course (MOOC) specializations?

I started learning about data science about three years ago. It was quite challenging from the beginning as I had these same questions in my mind. After taking several data science MOOCs from a wide variety of platforms, I found three important specializations that I consider to be among the best MOOC specializations in data science. They are (1) Data Science Specialization from Harvard University; (2) Analytics, Essential Tools and Methods from Georgia Institute of Technology; and (3) Applied Data Science with Python from the University of Michigan.

Reasons why I consider these 3 specializations to be among the best

  1. They are taught in Python or R, and Python and R are considered the top 2 technology skills mentioned in most data science job listings (The Most in Demand Skills for Data Scientists). These specializations thus provide an excellent opportunity to learn and implement data science tasks in both languages.
  2. These specializations cover in a significant level of depth, career-oriented courses that will help you develop in-demand skills that will enable you to tackle real-world data science challenges. You will learn skills in Python, R, Statistics & Probability, Data Processing, Data Transformation, Data Engineering, Data Visualization, Machine Learning, Model Building, Model Testing and Evaluation, and Application.
  3. These specializations are taught by world-class experts in the field of data science with diverse backgrounds such as Information Systems, Biostatistics, Computational Science and Engineering, Systems and Industrial Engineering, Computer Science, and Business Analytics. This provides a great opportunity for you to learn a variety of approaches. For example, the HarvardX Professional Certificate specialization in data science is taught by Prof. Rafael Irizarry who is a professor of Biostatistics at Harvard University, so his courses are very rich in statistics. Meanwhile, the Georgia TechX Analytics: Essential Tools and Methods is taught by Prof. Joel Sokol who is a professor of Systems and Industrial Engineering at Georgia Tech, so he delves into lots of applications of data science to fields such as aviation, health care, sports, energy sector, human resource management, etc.
  4. These specializations are offered by universities that are consistently ranked among the best universities in the world, thus providing a great opportunity to learn from ivy-league quality universities.

In this article, I will provide an overview of the different specializations including courses within the specializations and some background information about the world-class instructors teaching the courses.

II. Instructors and Specializations Taught

Here is my list of the top three data science MOOC specializations with information about the great professors teaching the courses.

Instructor 1: Professor Rafael Irizarry

Short Bio: Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also has taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.

Specialization Taught: Professional Certificate in Data Science (HarvardX, through edX)

Includes the following courses, all taught using R (you can audit courses for free or purchase a verified certificate):

  1. Data Science: R Basics;
  2. Data Science: Visualization;
  3. Data Science: Probability;
  4. Data Science: Inference and Modeling;
  5. Data Science: Productivity Tools;
  6. Data Science: Wrangling;
  7. Data Science: Linear Regression;
  8. Data Science: Machine Learning;
  9. Data Science: Capstone

Remarks: These specializations cover in a significant level of depth, career-oriented courses that will help you develop in-demand skills that will enable you to be able to tackle real-world data science challenges. You will learn skills in Python, R, Statistics & Probability, Data Processing, Data Transformation, Data Engineering, Data Visualization, Machine Learning, Model Building, Model Testing and Evaluation, and Application. Prof. Irizarry is a great instructor and he is very passionate about teaching. With his background as a statistician, the course is heavy in statistics. For example in the course Linear Regression, he derives the fundamentals of linear regression using a statistical approach, hence it’s very theoretical and in-depth.

Instructor 2: Professor Joel Sokol

Short Bio: He received his PhD in operations research from MIT and his bachelor’s degrees in mathematics, computer science, and applied sciences in engineering from Rutgers University. His primary research interests are in sports analytics and applied operations research. He has worked with teams or leagues in all three of the major American sports. Dr. Sokol’s LRMC method for predictive modeling of the NCAA basketball tournament is an industry leader, and his non-sports research has won the EURO Management Science Strategic Innovation Prize. Dr. Sokol has also won recognition for his teaching and curriculum development from IIE and the NAE, and is the recipient of Georgia Tech’s highest awards for teaching.

Specialization Taught: Analytics: Essential Tools and Methods (Georgia TechX, through edX)

Includes the following courses, all taught using R, Python, and SQL (you can audit for free or purchase a verified certificate):

  1. Introduction to Analytics Modeling;
  2. Introduction to Computing for Data Analysis;
  3. Data Analytics for Business.

Remarks: With a background in Systems and Industrial Engineering, Professor Sokol delves into lots of applications of data science to fields such as aviation, health care, sports, energy sector, human resource management, etc. His courses are less theoretical and full of real-world applications. Professor Sokol is full of energy and enthusiasm. I find his lectures to be very motivating and inspiring.

Instructor 3: Dr. Christopher Brooks

Short Bio: Christopher Brooks is a Research Assistant Professor in the School of Information and Director of Learning Analytics and Research in the Office of Digital Education & Innovation at the University of Michigan. His research focus is on the design of tools to better the teaching and learning experience in higher education, with a particular interest in understanding how learning analytics can be applied to human computer interaction through educational data mining, machine learning, and information visualization

Specialization Taught: Applied Data Science with Python Specialization (University of Michigan, through Coursera)

Includes the following courses, all taught using python (you can audit most courses for free, some require the purchase of a verified certificate):

  1. Introduction to Data Science in Python;
  2. Applied Plotting, Charting & Data Representation in Python;
  3. Applied Machine Learning in Python;
  4. Applied Text Mining in Python;
  5. Applied Social Network Analysis in Python.

Remarks: Anyone who has completed this specialization would agree that this is one of the most in-depth MOOC specialization in data science. Be prepared for a very in-depth treatment with lots of real-world applications. The homework assignments are pretty tough and challenging, but you learn a lot after completing each.

III. Summary

In summary, we have discussed the three important data science MOOC specializations that can enable a beginner to learn the basics. The journey to become a data scientist might be different for different individuals based on their backgrounds, but the three data science specializations we’ve discussed in this article will enable anyone new in the field of data science to master the fundamentals by learning from world-class instructors that have made significant contributions to the field of data science and analytics.

IV. References

  1. edX website: https://www.edx.org/.
  2. Coursera website: https://www.coursera.org/.

Additional Data Science/Machine Learning Resources

How Much Math do I need in Data Science?

Data Science Curriculum

5 Best Degrees for Getting into Data Science

Theoretical Foundations of Data Science — Should I Care or Simply Focus on Hands-on Skills?

Machine Learning Project Planning

How to Organize Your Data Science Project

Productivity Tools for Large-scale Data Science Projects

A Data Science Portfolio is More Valuable than a Resume

For questions and inquiries, please email me: benjaminobi@gmail.com

Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Personal Finance Analytics, Materials Sciences, Biophysics

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