This course is part of the Applied Data Science with Python Specialization

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Christopher Brooks

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Part of the Master of Applied Data Science degree
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Applied Data Science with Python SpecializationUniversity of Michigan

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 1 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 35 hours to complete

English

What you will learn

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

Skills you will gain

Python Programming
Numpy
Pandas
Data Cleansing

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 35 hours to complete

English

Instructor

Instructor rating

4.41/5 (3,718 Ratings)

Christopher Brooks

Assistant Professor

School of Information

775,790 Learners

14 Courses

Offered by

University of Michigan

Start working towards your Master's degree

This course is part of the 100% online Master of Applied Data Science from University of Michigan. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Content Rating92%(75,605 ratings)

Week

Week 1

13 hours to complete

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.

13 hours to complete

12 videos (Total 111 min), 6 readings, 2 quizzes

12 videos

Introduction to Specialization3m

Introduction to the Course4m

The Coursera Jupyter Notebook System8m

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

Numerical Python Library (NumPy)32m

Manipulating Text with Regular Expression27m

6 readings

Syllabus10m

Notice for Auditing Learners: Assignment Submission10m

Help Us Learn More About You!10m

Week 1 Textbook Reading Assignment (Optional)2h

50 years of Data Science, David Donoho (Optional)1h 30m

Regular Expression Operations documentation1h

1 practice exercise

Quiz 130m

Week

Week 2

7 hours to complete

Basic Data Processing with Pandas

7 hours to complete

9 videos (Total 89 min), 1 reading, 2 quizzes

Week

Week 3

8 hours to complete

More Data Processing with Pandas

8 hours to complete

6 videos (Total 84 min), 1 reading, 2 quizzes

Week

Week 4

7 hours to complete

Answering Questions with Messy Data

7 hours to complete

2 videos (Total 20 min), 5 readings, 2 quizzes

Reviews

4.5

5890 reviews

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TOP REVIEWS FROM INTRODUCTION TO DATA SCIENCE IN PYTHON

by PTJan 15, 2017

Very good course assignments and projects. Really learnt good by exploring stackoverflow and other forums to complete the assignments. Please include some detailed lectures in the upcoming modules.

by SIMar 15, 2018

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 .

by TGApr 13, 2020

Awesome course! I haven't done any course like this. Explanations were very clear and deep, which is very helpful to learn the content. Thanks a lot to the professor and the University of Michigan.

by BKJul 17, 2022

An excellent course offered by the university of michigan which provides the basic knowledge required for starting career in data science and the concepts explianing by the proffesors were profound.

View all reviews

About the Applied Data Science with Python Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Introduction to Data Science in Python

About this Course

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