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Introduction to Data Science in Python

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambd...
4.5
4.5/5
(24,259 reviews)
612,033 students
Created by

9.2

CourseMarks Score®

N/A

Freshness

N/A

Feedback

9.0

Content

Platform: Coursera
Price: $0
Video: 5h 4m
Language: English

Top Data Analysis courses:

Detailed Analysis

CourseMarks Score®

9.2 / 10

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Content Score

9.0 / 10
Video Score: 8.3 / 10
The course includes 5h 4m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 8.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.8 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

14 articles.
0 resource.
0 exercise.
8 tests or quizzes.

Table of contents

Description

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.

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

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

Requirements

Basic knowledge of Data Analysis is required to start this course, as this is an intermediate level course.

This course is for

This course was made for intermediate-level students.

How much does the Introduction to Data Science in Python course cost? Is it worth it?

Access to most course materials is FREE in audit mode on Coursera. If you wish to earn a certificate and access graded assignments, you must purchase the certificate experience during or after your audit.

If the course does not offer the audit option, you can still take a free 7-day trial.

Does the Introduction to Data Science in Python course have a money back guarantee or refund policy?

Coursera offers a 7-day free trial for subscribers.

Are there any SCHOLARSHIPS for this course?

YES, you can get a scholarship or Financial Aid for Coursera courses. The first step is to fill out an application about your educational background, career goals, and financial circumstances. Learn more about financial aid on Coursera.

Who is the instructor? Is Christopher Brooks a SCAM or a TRUSTED instructor?

Christopher Brooks has created 6 courses that got 3,121 reviews which are generally positive. Christopher Brooks has taught 652165 students and received a 4.4 average review out of 3,121 reviews. Depending on the information available, Christopher Brooks is a TRUSTED instructor.
School of Information
University of Michigan
Christopher Brooks is a Research Assistant Professor in the School of Information and Director of Learning Analytics and Research in the Office of Digital Education & Innovation at the University of Michigan. His research focus is on the design of tools to better the teaching and learning experience in higher education, with a particular interest in understanding how learning analytics can be applied to human computer interaction through educational data mining, machine learning, and information visualization.

9.2

CourseMarks Score®

N/A

Freshness

N/A

Feedback

9.0

Content

Platform: Coursera
Price: $0
Video: 5h 4m
Language: English

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