Top MOOCs for Data Science in 2019 Top MOOCs for Data Science in 2019
With all the people now trying to transition into the field of data science, it’s no wonder that “data science education” has become a... Top MOOCs for Data Science in 2019

With all the people now trying to transition into the field of data science, it’s no wonder that “data science education” has become a pretty big commodity these days. Just about every week I hear of some new academic program, boot camp, specialization series, or Massive Open Online Course (MOOC) offering designed to jump-start your career in this exciting profession. I realize how confusing all these options are to learners who are new to the field. The field of cognitive science has a term for having too many choices: overchoice or choice overloading. It can result in decision paralysis. In this article, I want to help you get past any decision paralysis you may experience by providing a list of what I feel are the top MOOCs for data science in 2019.

 

Top MOOCs for Data Science

The following list contains my favorite options for MOOC learning designed to open the door for you to enter the field of data science.

 

Data Science Specialization offered by John’s Hopkins University – a 10-course series taught by 3 biostatistics professors: Jeff Leek, Ph.D., Roger Peng, Ph.D., and Brian Caffo, Ph.D. The courses are offered through the Coursera platform. The series takes around 8 months to complete with a 5 hour/week time commitment. Course materials are all based on the R statistical environment. Learners can audit the course content for free, or pay a fee of $49/month to access the content for earning a certificate.

When this series was first being developed, as one of the community TAs, I was asked to beta test each course. I found the learning materials to be top-rate for both theory and practice. Plus, the professors are excellent educators and communicators. This series offers a very well-rounded introduction to data science.

Here is a list of the 10 courses included in the specialization:

  1. The Data Scientist’s Toolbox 
  2. R Programming
  3. Getting and Cleaning Data
  4. Exploratory Data Analysis
  5. Reproducible Research
  6. Statistical Inference
  7. Regression Models
  8. Practical Machine Learning
  9. Developing Data Products
  10. Data Science Capstone

[Related article: How to Balance Work and Learn More About Data Science]

 

Deep Learning Specialization Offered by deeplearning.ai – a 5 course series by luminary AI researcher and Stanford professor Andrew Ng. The courses are offered through the Coursera platform. Both theory and practice are included in the courses, but be prepared for a significant dose of calculus, linear algebra, and partial differential equations. You should definitely brush up on these areas of mathematics before attempting these courses. The series takes around 4 months to complete with a 3-6 hour/week time commitment. Learners must pay the $49/month Coursera fee to access the course content. While you’re taking the courses, you’re free to download all the slides, notes, videos and Jupyter notebooks for future review and learning.

Here is a list of the 5 courses included in the specialization:

  1. Neural Networks and Deep Learning
  2. Improving Deep Neural Networks
  3. Structuring Machine Learning Projects
  4. Convolutional Neural Networks
  5. Sequence Models

As a brief intro to set the stage for your learning, you might consider taking the just launched AI for Everyone 4-week course with the following weekly topics:

  1. What is AI
  2. Building AI Projects
  3. AI in Your Company
  4. AI and Society

 

Applied Data Science with Python Specialization by the University of Michigan – a 5-course series that focuses on data science using the Python language and associated libraries. This would be a good course to take if you also have an interest in data engineering which is often Python based. The courses are offered through the Coursera platform. The series takes 3-4 months to complete with a 3-4 hour/week time commitment. Learners can audit the course content for free, or pay a fee of $49/month to access the content for earning a certificate.

 

Here is a list of the 3 courses included in the specialization:

  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

Machine Learning by Stanford University – an 11-week course in machine learning, this is the granddaddy of them all, and the course that pioneered the MOOC industry. Taught by Andrew Ng, this class changed my life. After being away from a graduate program in CS and applied statistics for over a decade, I used this course to edge back into the field. Coupled with Ng’s excellent teaching style, and a fresh look at the subject (that included a lot of math), I felt right back in the game after completing this course. I highly recommend it. The course is offered through the Coursera platform. The course requires a minimum of 4 hour/week time commitment. Learners can audit the course content for free, or pay a fee of $49/month to access the content for earning a certificate.

 

Here is a list of the 11 weekly topics:

  1. Introduction, Linear Regression with One Variable, Linear Algebra Review
  2. Linear Regression with Multiple Variables, Octave/Matlab Tutorial
  3. Logistic Regression, Regularization
  4. Neural Networks: Representation
  5. Neural Networks: Learning
  6. Advice for Applying Machine Learning, Machine Learning System Design
  7. Support Vector Machines
  8. Unsupervised Learning, Dimensionality Reduction
  9. Anomaly Detection, Recommender Systems
  10. Large Scale Machine Learning
  11. Application Example: Photo OCR

 

Mathematics for Machine Learning Specialization by Imperial College in London – a 3 course series that focuses on a very important aspect of being a data scientist – mathematics. The courses are offered through the Coursera platform. The series takes 3-4 months to complete with a 3-4 hour/week time commitment. Learners can audit the course content for free, or pay a fee of $49/month to access the content for earning a certificate. You might consider taking this math refresher before attempting the other specializations.

Here is a list of the 3 courses:

  1. Mathematics for Machine Learning: Linear Algebra
  2. Mathematics for Machine Learning: Multivariate Calculus
  3. Mathematics for Machine Learning: Principal Component Analysis (PCA)

[Related article: 3 Types Of Certifications in Data Science That Make You Stand Out]

The Value of Specializations

I get asked all the time how much are “specializations” worth to potential employers. Frankly, a specialization from a MOOC is not going to be weighed as much as a formal academic program and associated degree, but it will show you’ve taken initiative to get up to speed with the subject and I think there’s some value in that. You have to start somewhere, and if you don’t have a couple of years to complete a Masters in Data Science program, MOOC specializations is definitely the way to go.

The MOOC companies make it easy to show off your new specialization. For example, Coursera presents a button to click once you’ve completed of each course, and also at the completion of the series, that automatically adds an entry to the “License & Certifications” section of your LinkedIn profile. Nice touch.

 

Conclusion

I hope these learning options will help you narrow down the plethora of alternatives which can be quite overwhelming, and I hope you acquire some great new knowledge that you can apply in your career. Happy learning!

Daniel Gutierrez, ODSC

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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