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
1,560,971 次近期查看
学生职业成果
33%
完成这些课程后已开始新的职业生涯
35%
通过此课程获得实实在在的工作福利
10%
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）
您将获得的技能
Python ProgrammingNumpyPandasData Cleansing
学生职业成果
33%
完成这些课程后已开始新的职业生涯
35%
通过此课程获得实实在在的工作福利
10%
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 5 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 5 小时
2 个视频 （总计 20 分钟）, 4 个阅读材料, 2 个测验
4 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
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 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
由 AU 提供Dec 9, 2017
Wow, this was amazing. Learned a lot (mostly thanks to stack overflow) but the course also opened my eyes to all the possibilities available out there and I feel like i'm only scratching the surface!
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.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
常见问题
我什么时候能够访问课程视频和作业？
讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程，则可以免费查看大多数课程资料。要访问评分作业并获得证书，您需要在旁听期间或之后购买证书体验。如果看不到旁听选项：
- 课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
- 课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
Is financial aid available?
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。
还有其他问题吗？请访问 学生帮助中心。
