Disclosure: when you buy through links on our site, we may earn an affiliate commission.

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

8.8

CourseMarks Score®

N/A

Freshness

8.2

Feedback

9.0

Content

Platform: Coursera
Video: 5h 4m
Language: English
Next start: Start anytime

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.
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.
Platform: Coursera
Video: 5h 4m
Language: English
Next start: Start anytime

Students are also interested in