此课程适用人群: This course is part of the skills-based specialization “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.


制作方:   University of Michigan

Basic Info
LevelIntermediate
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.3 stars
Average User Rating 4.3See what learners said
授课大纲

常见问题解答
运作方式
课程作业
课程作业

每门课程都像是一本互动的教科书,具有预先录制的视频、测验和项目。

来自同学的帮助
来自同学的帮助

与其他成千上万的学生相联系,对想法进行辩论,讨论课程材料,并寻求帮助来掌握概念。

证书
证书

获得正式认证的作业,并与朋友、同事和雇主分享您的成功。

制作方
University of Michigan
价格
旁听购买课程
访问课程材料

可用

可用

访问评分的材料

不可用

可用

收到最终成绩

不可用

可用

获得可共享的证书

不可用

可用

评分和审阅
已评分 4.3,总共 5 个 29 评分

This a pretty good introduction to plotting libraries in python. I would have preferred a deeper dive into some of the built-in methods. A little more on visualizations from libraries like seaborn, bokeh, or plotly would have been nice. Overall, great work.

I learned quite a lot about plotting, especially in Python. However, there was a bit too much theory (Cairos principles etc) for my taste.

Good intro to plotting, charting and visualization in Python. Focuses mainly on matplotlib. I feel good about the content that I learned, but also feel like I wanted to learn more in this class. Maybe more coverage of other python charting libraries. More examples of financial type charts -- High/Low/Open/Close etc.

I found the lectures interesting and thorough yet short and to the point.