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Learner Reviews & Feedback for Design Thinking and Predictive Analytics for Data Products by University of California San Diego

61 ratings

About the Course

This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization....
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1 - 11 of 11 Reviews for Design Thinking and Predictive Analytics for Data Products

By Nguyen T

Jun 13, 2020

While the instructor does appear to be very knowledgeable, many mathematic concepts are brought up during this course that are not always followed up with implementation in Python. For instance, there is a demo in Python for Linear Regression and Autoregression, but some brought up methods are not demonstrated in Python. It is a shame, though, because this course had a lot of promise.

By surendar r

Jun 14, 2019

Course contents are very good, able to learn a lot.

However, very frustrating system is - project assignment submissions of last week has to wait for infinite time to be graded by peers. Wait time to get feedback on your submission is extremely long and very annoying to have such a long wait.

Either, mentors of this course should step forward and help in this review process at periodic intervals or, this system should go away and it should NOT be mandatory requirement to complete this course.

For poor grading system that is in place for project submission - am submitting 2 stars, otherwise I would have gone for 4 or 5 hands down

By Pratik P

Jul 10, 2019

This course takes you from learning to do many data analytics and Machine learning tasks manually to all the way doing it much more efficiently using the standard libraries. Overall, a great course to give you a rock solid foundation in this field.


Mar 26, 2021

Excellent course.

By Yassine E

Feb 21, 2020



Mar 8, 2021


By Clarence E Y

Jan 3, 2020

This course provides practical techniques used for regression and classification of datasets. These techniques are important to gain understanding and experience in building a data pipeline in the design process. Logistic Regression, Support Vector Machines, and K-Means approaches are covered along with Jaccard, F-1 error evaluation and Gradient Descent.

By Anshu P M

May 8, 2021

It was great course ,helped me in getting better understanding of data and do predictive modeling.

By Reinhold L

Jun 21, 2019

Very informative course and very good documentation as well as practical examples.

By Sebastian R B

Feb 20, 2021

Good introduction to the concepts of machine learning: Linear Regression and Classification (Logistic Regression)); however, not good emphasis was made to the application nor code.

By Olugbenga O A

Jan 18, 2020

The Technical parts felt too rushed.