캘리포니아 샌디에고 대학교의 Basic Data Processing and Visualization 학습자 리뷰 및 피드백

The course is easy to follow, well organized, and assumes very little background. It effectively demonstrates the power of Python in large data applications and provides insights and guidance on which tools are best used.

Great first class in this series.

Over all a great course for beginner

Excellent to start your career in machine learning!!!

Excellent and interesting course

Great course for an absolute beginner!

This course enables students to learn intermediate level skills in data wrangling, data exploration, and visualization. The final project requires selecting a topic of personal interest and constructing a complete project work flow. By doing this, areas of weakness in data wrangling, cleaning/QA, data exploration, and visualization may to uncovered and addressed. The result is to build greater skills and confidence.

The positives: I liked the design of the final project, and how users were encouraged to 'get out there' and find some interesting open source data sets. The lectures were well structured with good narratives and good examples.

The negatives: I would have liked a bit more focus on actual visualization libraries like matplotlib and maybe seaborn. When covering the data types (date, string, boolean etc.), it might be worth adding an extra week or so were these things are done with the help of the standard library pandas. I feel like this is what people will end up doing anyway bc there are so little alternatives in python to do processing, so a course on data processing should ideally cover that library.

Quick intro to data processing. More material on numpy and pandas would have been nice. Im still trying to figure out why the specialization mentions "Design Thinking". At least in this course...not a single design thinking concept was mentioned.

The test scripts make no sense.

This is not a Python introduction, but the authors approach it as if it were. Even the most basic data scientist will not calculate averages in the way described here. We'd use pandas or similar to get not just means, but other summary stats as well. For a Python course, I could understand doing it the way shown here. But not for data science.