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學生對 加州大学圣地亚哥分校 提供的 Basic Data Processing and Visualization 的評價和反饋

4.5
14 個評分
2 個審閱

課程概述

This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning 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 - Basic Data Processing and Visualization 的 5 個評論(共 5 個)

創建者 Zakir U S

Jun 24, 2019

Over all a great course for beginner

創建者 Sebastian S

Jun 22, 2019

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.

創建者 Davide C

Jun 18, 2019

The test scripts make no sense.

創建者 Cambron T D

May 22, 2019

Great first class in this series.

創建者 Carl W

Apr 27, 2019

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.