This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
课程信息
您将学到的内容有
Understand techniques such as lambdas and manipulating csv files
Describe common Python functionality and features used for data science
Query DataFrame structures for cleaning and processing
Explain distributions, sampling, and t-tests
您将获得的技能
- Python Programming
- Numpy
- Pandas
- Data Cleansing
提供方
立即开始攻读硕士学位
授课大纲 - 您将从这门课程中学到什么
Fundamentals of Data Manipulation with Python
Basic Data Processing with Pandas
More Data Processing with Pandas
Answering Questions with Messy Data
审阅
- 5 stars66.26%
- 4 stars24.47%
- 3 stars5.35%
- 2 stars1.90%
- 1 star1.99%
来自INTRODUCTION TO DATA SCIENCE IN PYTHON的热门评论
This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.
The speaker is so dull, And the way he speaks doesn't motivate one to learn the things with interested. The speaker of the videos should be the one who speaks passionately about what he is saying..
Enjoyed every bit of it. The timings for the assignment could be a little bit more though. Also the expected output could be provided for validation, rather than the grader printing cryptic messages.
Awesome course! I haven't done any course like this. Explanations were very clear and deep, which is very helpful to learn the content. Thanks a lot to the professor and the University of Michigan.
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