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

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Christopher Brooks

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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

Reset deadlines in accordance to your schedule.

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. 34 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. 34 hours to complete

English

Instructor

Instructor rating

4.41/5 (3,731 Ratings)

Christopher Brooks

Assistant Professor

School of Information

784,308 Learners

14 Courses

Offered by

University of Michigan

Start working towards your Master's degree

This course is related to the 100% online Master of Applied Data Science from University of Michigan. It will provide you with a preview of the topics, materials and instructors so you can decide if the full online degree program is right for you.

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Syllabus - What you will learn from this course

Content Rating92%(76,175 ratings)

Week1

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 Specialization3mIntroduction to the Course4mThe Coursera Jupyter Notebook System8mPython Functions8mPython Types and Sequences8mPython More on Strings3mPython Demonstration: Reading and Writing CSV files3mPython Dates and Times2mAdvanced Python Objects, map()5mAdvanced Python Lambda and List Comprehensions2mNumerical Python Library (NumPy)32mManipulating Text with Regular Expression27m

6 readings

Syllabus10mNotice for Auditing Learners: Assignment Submission10mHelp Us Learn More About You!10mWeek 1 Textbook Reading Assignment (Optional)2h50 years of Data Science, David Donoho (Optional)1h 30mRegular Expression Operations documentation1h

1 practice exercise

Quiz 130m

Week2

Week 2

7 hours to complete

Basic Data Processing with Pandas

7 hours to complete

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

Week3

Week 3

8 hours to complete

More Data Processing with Pandas

8 hours to complete

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

Week4

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

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TOP REVIEWS FROM INTRODUCTION TO DATA SCIENCE IN PYTHON

by CJun 17, 2020

Um curso intenso e bastante prazeroso. Gostei de todas as etapas, os videos funcionam bem e estão construidos numa base introdutória, mas o desafio é pesquisar e pesquisar. Muito interessante mesmo!

by PSAug 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.

by KLMar 11, 2018

A very nice introduction to libraries/skills used by data scientists. The auto-grader was extremely annoying though. Also, I felt that some of the questions on the assignments were a bit ambiguous.

by ANAug 15, 2020

I found this course appealing because it was more practical based.it helped me alot in getting hands on experience and most of all I have learned how to solve real world problem with python libraries

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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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

What you will learn

Skills you will gain