University of Michigan
Introduction to Data Science in Python
University of Michigan

Introduction to Data Science in Python

Christopher Brooks

Instructor: Christopher Brooks

783,193 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(26,958 reviews)

|

92%

Intermediate level
Some related experience required
34 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

What you'll 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

Details to know

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Assessments

4 quizzes

Taught in English

Course

Gain insight into a topic and learn the fundamentals

4.5

(26,958 reviews)

|

92%

Intermediate level
Some related experience required
34 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

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Build your subject-matter expertise

This course is part of the Applied Data Science with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
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There are 4 modules in this course

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.

What's included

12 videos6 readings1 quiz1 programming assignment2 ungraded labs1 plugin

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.

What's included

9 videos1 reading1 quiz1 programming assignment1 ungraded lab

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.

What's included

6 videos1 reading1 quiz1 programming assignment1 ungraded lab

In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.

What's included

2 videos6 readings1 quiz1 programming assignment1 ungraded lab

Instructor

Instructor ratings
4.4 (3,830 ratings)
Christopher Brooks
15 Courses863,881 learners

Offered by

Recommended if you're interested in Data Analysis

Prepare for a degree

Taking this course by University of Michigan may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.

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4.5

26,958 reviews

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