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.
Introduction to Data Science in Python
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Introduction to Data Science in Python
This course is part of Applied Data Science with Python Specialization
Instructor: Christopher Brooks
803,600 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.5
(27,080 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 34 hours
Learn at your own pace
92%
Most learners liked this course
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
Skills you'll gain
Details to know
Shareable certificate
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Assessments
4 assignments
Taught in English
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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.
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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 assignment1 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 assignment1 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 assignment1 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 assignment1 programming assignment1 ungraded lab
Instructor
Instructor ratings
4.4 (3,859 ratings)15 Courses888,222 learners
Offered by
Recommended if you're interested in Data Analysis
Duke University
University of Michigan
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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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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:
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