Applied Data Science with Python 专项课程

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.

100% 在线课程

立即开始，按照自己的计划学习。

中级

Approx. 18 hours to complete

建议：6 hours/week

English

字幕：English, Korean, Vietnamese, Chinese (Traditional)

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

PandasPython ProgrammingData CleansingData Analysis

100% 在线课程

立即开始，按照自己的计划学习。

中级

Approx. 18 hours to complete

建议：6 hours/week

English

字幕：English, Korean, Vietnamese, Chinese (Traditional)

Syllabus - What you will learn from this course

1

Section

3 hours to complete

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 videos (Total 58 min), 4 readings, 1 quiz

11 videos

Introduction to Specialization3m

Data Science7m

The Coursera Jupyter Notebook System3m

Python Functions8m

Python Types and Sequences8m

Python More on Strings3m

Python Demonstration: Reading and Writing CSV files3m

Python Dates and Times2m

Advanced Python Objects, map()5m

Advanced Python Lambda and List Comprehensions2m

Advanced Python Demonstration: The Numerical Python Library (NumPy)7m

4 readings

Syllabus10m

Help us learn more about you!10m

50 years of Data Science, David Donoho (optional)30m

Notice for Auditing Learners: Assignment Submission10m

1 practice exercises

Week One Quiz12m

2

Section

3 hours to complete

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 videos (Total 45 min), 2 quizzes

8 videos

Introduction2m

The Series Data Structure4m

Querying a Series8m

The DataFrame Data Structure7m

DataFrame Indexing and Loading5m

Querying a DataFrame5m

Indexing Dataframes5m

Missing Values4m

3

Section

3 hours to complete

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 videos (Total 35 min), 1 quiz

6 videos

Merging Dataframes7m

Pandas Idioms6m

Group by6m

Scales7m

Pivot Tables2m

Date Functionality5m

4

Section

6 hours to complete

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 videos (Total 25 min), 1 reading, 2 quizzes

4 videos

Introduction1m

Distributions4m

More Distributions8m

Hypothesis Testing in Python10m

1 readings

Post-course Survey10m

Instructor

Christopher Brooks

About University of Michigan

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....

Frequently Asked Questions

When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I pay for this course?
If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.
What is the refund policy?
Is financial aid available?
Yes! Coursera provides financial aid to learners who would like to complete a course but cannot afford the course fee. To apply for aid, select "Learn more and apply" in the Financial Aid section below the "Enroll" button. You'll be prompted to complete a simple application; no other paperwork is required.

More questions? Visit the Learner Help Center

Applied Data Science with Python 专项课程