About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 18 hours to complete

Suggested: 7 hours/week...

English

Subtitles: Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, English, Hebrew...
User
Learners taking this Course are
  • Risk Managers
  • Data Analysts
  • Data Scientists
  • Process Analysts
  • Business Analysts

What you will learn

  • Check

    Describe common Python functionality and features used for data science

  • Check

    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
User
Learners taking this Course are
  • Risk Managers
  • Data Analysts
  • Data Scientists
  • Process Analysts
  • Business Analysts

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 18 hours to complete

Suggested: 7 hours/week...

English

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

Syllabus - What you will learn from this course

Week
1
3 hours to complete

Week 1

11 videos (Total 58 min), 4 readings, 1 quiz
11 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
4 readings
Syllabus10m
Help us learn more about you!10m
50 years of Data Science, David Donoho (optional)1h 30m
Notice for Auditing Learners: Assignment Submission10m
1 practice exercise
Week One Quiz12m
Week
2
3 hours to complete

Week 2

8 videos (Total 45 min), 2 quizzes
8 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
3 hours to complete

Week 3

6 videos (Total 35 min), 1 quiz
6 videos
Pandas Idioms6m
Group by6m
Scales7m
Pivot Tables2m
Date Functionality5m
Week
4
6 hours to complete

Week 4

4 videos (Total 25 min), 1 reading, 2 quizzes
4 videos
Distributions4m
More Distributions8m
Hypothesis Testing in Python10m
1 reading
Post-course Survey10m
4.5
2884 ReviewsChevron Right

32%

started a new career after completing these courses

33%

got a tangible career benefit from this course

Top reviews from Introduction to Data Science in Python

By AVJan 1st 2017

To be an introductory course I struggled a lot, is a very practical course, and the assignements encourage you to learn more. This is the best technical course I have taken. Lo recomiendo ampliamente

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.