Ratings and Reviews for Introduction to Data Science in Python

A very clear introduction to using Pandas for handling data. The integration of Jupyter Notebooks in both the assignments and the in-lecture pop-ups was very effective. I also appreciated the balance of covering most of the material in lectures, but leaving you to track down some things for the assignments. I can only hope the next courses in this progression are this well done.

It's a great course with attention to detail. The assignments are very detailed which help a lot in grasping the key concepts.

Fully recommended course.

I learnded how to effectively use pandas to process data, very good lesssons

Great learning experience with a very good teacher. A little bit difficult but, hey this is science!

They follow a practical approach and present you only the very relevant material so you do not stuck too much into theory. Note that this is an intermediate-level course and you need some experience, self-learning skills, and some patience to be successful.

In the beginning I thought that the course should have more Tips and Help to the student.After finishing it, I understand that having the ability to search for the solution without too helping is good to produce the data science skills.

Awesome course to start study datascience using python, nice and hard projects to do!

Excellent course with very challenging assignments!

The main focus of the course is the introduction of the Pandas (series and data frames) library, which is very useful in data analysis. The last two assignments are quite challenging and time consuming, if you are not familiar with Pandas. Why the poor review: I'm sure that the intention of the teacher (Prof. Brooks) is for the student to be challenged and obtain familiarity with several "advanced" functionalities of Python. When I had finished the last assignment I felt that way, but not due to the lectures (only ~2.5 hours all in all). The pace of these lectures is too fast (probably because they are scripted). The teacher should slow down a bit and show some more examples (for inspiration watch Prof. Andrew Ng from Stanford lecture on machine learning). I'm not suggesting to show explicit solutions of the assignments, but just a few more examples such that the transition from lecture to problem solving is less "frustrating". Furthermore, the students are paying $79 for this course expecting thorough lectures on the topic. Reading the documentation of the Pandas library can be done for free...