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
2,691,014 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16 小时
英语（English）
字幕：中文（繁体）, 巴西葡萄牙语, 越南语, 韩语, 英语（English）, 希伯来语
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
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16 小时
英语（English）
字幕：中文（繁体）, 巴西葡萄牙语, 越南语, 韩语, 英语（English）, 希伯来语
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 3 小时
Week 1
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 3 小时
11 个视频 （总计 58 分钟）, 4 个阅读材料, 1 个测验
11 个视频
Data Science7分钟
The Coursera Jupyter Notebook System3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
Advanced Python Demonstration: The Numerical Python Library (NumPy)7分钟
4 个阅读材料
Syllabus10分钟
Help us learn more about you!10分钟
50 years of Data Science, David Donoho (optional)1 小时 30 分
Notice for Auditing Learners: Assignment Submission10分钟
1 个练习
Week One Quiz12分钟
完成时间为 3 小时
Week 2
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. The module ends with a programming assignment and a discussion question.
完成时间为 3 小时
8 个视频 （总计 45 分钟）, 1 个阅读材料, 2 个测验
8 个视频
Introduction2分钟
The Series Data Structure4分钟
Querying a Series8分钟
The DataFrame Data Structure7分钟
DataFrame Indexing and Loading5分钟
Querying a DataFrame5分钟
Indexing Dataframes5分钟
Missing Values4分钟
1 个阅读材料
Common Assignment Pitfalls10分钟
完成时间为 3 小时
Week 3
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 3 小时
6 个视频 （总计 35 分钟）
完成时间为 6 小时
Week 4
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The majority of the week will be dedicated to your course project, where you'll engage in a real-world data cleaning activity and provide evidence for (or against!) a given hypothesis. This project is suitable for a data science portfolio, and will test your knowledge of cleaning, merging, manipulating, and test for significance in data. 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
4 个视频 （总计 25 分钟）, 1 个阅读材料, 2 个测验
1 个阅读材料
Post-course Survey10分钟
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？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
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。
退款政策是如何规定的？
如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。
有助学金吗？
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。
