Are you a fan of data science and online learning experiences?  Check out UC Berkeley’s new online data science course Foundations of Data Science: Computational Thinking with Python.  The course is being offered via edX, an online learning platform, for free!

This five week MOOC (massive open online course) is based on Data 8, an undergraduate course that launched in the Fall of 2015 and has become Berkeley’s fastest growing course. Why? Students are drawn to the innovative mix of computing and statistical concepts, hands-on python instruction in a cloud-based environment, fantastic instructors, and a great support system.  

Now you can access that magic via the edX course which started April 2, 2018. It’s not too late to join as the first week has a very light workload.  As an instructor at the D-Lab with a keen interest in pedagogy, I have checked out the first week’s materials and have done my homework! I encourage you to do the same.  What especially interests me is that this course is part of a three part series, which you can take as a certificate program for a fee if you wish. The other courses are:

Foundations of Data Science: Inferential Thinking by Resampling, beginning May 21, 2018

and

Foundations of Data Science: Prediction and Machine Learning, beginning July 9, 2018.

Together these three course can give you a solid foundation for moving forward with data science.

Alternatively or in conjunction, check out the D-Lab’s upcoming python and machine learning workshops which are a great on ramp for applying data science to your research domain.

 

Author: 

Patty Frontiera

Dr. Patty Frontiera is the D-Lab geospatial topic area lead. As such, she develops the geospatial workshop curriculum, teaches workshops and consults on geospatial topics.  Patty has been with the D-Lab since 2014 and served as the the Academic Coordinator through Spring 2017. Patty received her Ph.D. in Environmental Planning from UC Berkeley where her dissertation explored the application and effectiveness of generalized spatial representations in geographic information retrieval.