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.
This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
提供方
课程信息
4.5
10,018 个评分
您将学到的内容有
Describe common Python functionality and features used for data science
Explain distributions, sampling, and t-tests
Query DataFrame structures for cleaning and processing
Understand techniques such as lambdas and manipulating csv files
您将获得的技能
Python ProgrammingNumpyPandasData Cleansing
教学大纲 - 您将从这门课程中学到什么
周
1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.
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)30分钟
Notice for Auditing Learners: Assignment Submission10分钟
1 个练习
Week One Quiz12分钟
周
2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.
8 个视频 （总计 45 分钟）, 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
周
3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.
6 个视频 （总计 35 分钟）, 1 个测验
周
4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.
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
4.5
2,388 个审阅33%
完成这些课程后已开始新的职业生涯
34%
通过此课程获得实实在在的工作福利
11%
加薪或升职
热门审阅
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!
overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .
关于 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.
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.
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