About this Course
4.5
8,020 ratings
2,047 reviews
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 18 hours to complete

Suggested: 6 hours/week...
Available languages

English

Subtitles: English, Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, Hebrew...

What you will learn

  • Check

    Describe common Python functionality and features used for data science

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    Explain distributions, sampling, and t-tests

  • Check

    Query DataFrame structures for cleaning and processing

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    Understand techniques such as lambdas and manipulating csv files

Skills you will gain

Python ProgrammingNumpyPandasData Cleansing
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 18 hours to complete

Suggested: 6 hours/week...
Available languages

English

Subtitles: English, Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, Hebrew...

Syllabus - What you will learn from this course

Week
1
Hours to complete
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....
Reading
11 videos (Total 58 min), 4 readings, 1 quiz
Video11 videos
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
Reading4 readings
Syllabus10m
Help us learn more about you!10m
50 years of Data Science, David Donoho (optional)30m
Notice for Auditing Learners: Assignment Submission10m
Quiz1 practice exercise
Week One Quiz12m
Week
2
Hours to complete
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....
Reading
8 videos (Total 45 min), 2 quizzes
Video8 videos
The Series Data Structure4m
Querying a Series8m
The DataFrame Data Structure7m
DataFrame Indexing and Loading5m
Querying a DataFrame5m
Indexing Dataframes5m
Missing Values4m
Week
3
Hours to complete
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....
Reading
6 videos (Total 35 min), 1 quiz
Video6 videos
Pandas Idioms6m
Group by6m
Scales7m
Pivot Tables2m
Date Functionality5m
Week
4
Hours to complete
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....
Reading
4 videos (Total 25 min), 1 reading, 2 quizzes
Video4 videos
Distributions4m
More Distributions8m
Hypothesis Testing in Python10m
Reading1 reading
Post-course Survey10m
4.5
2,047 ReviewsChevron Right
Career direction

34%

started a new career after completing these courses
Career Benefit

83%

got a tangible career benefit from this course
Career promotion

11%

got a pay increase or promotion

Top Reviews

By SIMar 16th 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 AUDec 10th 2017

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!

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....

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. 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....
Applied Data Science with Python

Frequently Asked Questions

  • 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.

  • 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.

More questions? Visit the Learner Help Center.