About this Course

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Learner Career Outcomes

33%

started a new career after completing these courses

35%

got a tangible career benefit from this course

10%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 16 hours to complete
English
Subtitles: Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, English, Hebrew...

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 ProgrammingNumpyPandasData Cleansing

Learner Career Outcomes

33%

started a new career after completing these courses

35%

got a tangible career benefit from this course

10%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 16 hours to complete
English
Subtitles: Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, English, Hebrew...

Instructor

Offered by

University of Michigan logo

University of Michigan

Syllabus - What you will learn from this course

Content RatingThumbs Up91%(47,516 ratings)Info
Week
1

Week 1

3 hours to complete

Week 1

3 hours to complete
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

Week 2

3 hours to complete

Week 2

3 hours to complete
8 videos (Total 45 min), 1 reading, 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
1 reading
Common Assignment Pitfalls10m
Week
3

Week 3

3 hours to complete

Week 3

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

Week 4

6 hours to complete

Week 4

6 hours to complete
4 videos (Total 25 min), 1 reading, 2 quizzes
4 videos
Distributions4m
More Distributions8m
Hypothesis Testing in Python10m
1 reading
Post-course Survey10m

Reviews

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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

  • 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.
  • 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.

  • 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.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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