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.
提供方
课程信息
992,123 次近期查看
学生职业成果
29%
完成这些课程后已开始新的职业生涯
32%
通过此课程获得实实在在的工作福利
11%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为31 小时
英语（English）
字幕：英语（English）
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 ProgrammingNumpyPandasData Cleansing
学生职业成果
29%
完成这些课程后已开始新的职业生涯
32%
通过此课程获得实实在在的工作福利
11%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为31 小时
英语（English）
字幕：英语（English）
提供方
密歇根大学
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
教学大纲 - 您将从这门课程中学到什么
完成时间为 12 小时
Fundamentals of Data Manipulation with Python
In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.
完成时间为 12 小时
12 个视频 （总计 111 分钟）, 7 个阅读材料, 2 个测验
12 个视频
Introduction to the Course4分钟
The Coursera Jupyter Notebook System8分钟
Python Functions8分钟
Python Types and Sequences8分钟
Python More on Strings3分钟
Python Demonstration: Reading and Writing CSV files3分钟
Python Dates and Times2分钟
Advanced Python Objects, map()5分钟
Advanced Python Lambda and List Comprehensions2分钟
Numerical Python Library (NumPy)32分钟
Manipulating Text with Regular Expression27分钟
7 个阅读材料
Syllabus10分钟
Notice for Auditing Learners: Assignment Submission10分钟
Help Us Learn More About You!10分钟
Week 1 Textbook Reading Assignment2小时
50 years of Data Science, David Donoho (Optional)1 小时 30 分
Regular Expression Operations documentation1小时
Regular Expressions Debugging10分钟
完成时间为 6 小时
Basic Data Processing with Pandas
In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.
完成时间为 6 小时
9 个视频 （总计 89 分钟）, 1 个阅读材料, 2 个测验
9 个视频
The Series Data Structure10分钟
Querying a Series15分钟
DataFrame Data Structure12分钟
DataFrame Indexing and Loading8分钟
Querying a DataFrame9分钟
Indexing Dataframes8分钟
Missing Values11分钟
Example: Manipulating DataFrame8分钟
1 个阅读材料
Week 2 Reading Assignments1小时
完成时间为 7 小时
More Data Processing with Pandas
In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.
完成时间为 7 小时
6 个视频 （总计 84 分钟）, 1 个阅读材料, 2 个测验
6 个视频
Pandas Idioms15分钟
Group by19分钟
Scales10分钟
Pivot Table9分钟
Date/Time Functionality12分钟
1 个阅读材料
Week 3 Reading Assignments2小时
完成时间为 6 小时
Answering Questions with Messy Data
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
完成时间为 6 小时
2 个视频 （总计 20 分钟）, 5 个阅读材料, 2 个测验
5 个阅读材料
Science Isn't Broken: p-hacking45分钟
Goodhart's Law (Optional)30分钟
The 5 Graph Algorithms that you should know10分钟
Post-course Survey10分钟
Keep Learning with Michigan Online!10分钟
1 个练习
Final Quiz30分钟
审阅
来自INTRODUCTION TO DATA SCIENCE IN PYTHON的热门评论
由 PK 提供May 9, 2020
The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans
由 PS 提供Aug 11, 2020
Assignments are way tougher than what is taught in the class, but they are challenging and the help in discussion forums is speechless. Without that, completion of assignments will take too much time.
由 BN 提供Mar 1, 2017
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.
由 BZ 提供May 17, 2020
super hard course I enjoyed it but the thing that I didn't like specially when this is supposed to be the learning phase the assignments were hard and it sometimes pushes you to look for the solution
关于 借助 Python 应用数据科学 专项课程
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
常见问题
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
Is financial aid available?
完成课程后，我会获得大学学分吗？
还有其他问题吗？请访问 学生帮助中心。
