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Introduction to Data Science in Python

Course Cover

5

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Course Features

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Duration

31 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Beginner

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Teaching Type

Self Paced

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Video Content

31 hours

Course Description

This course introduces the basics of python programming, including basic programming techniques like lambdas, reading and manipulating CSV files, and using the numpy library. This course will teach data manipulation and cleaning using the popular Python pandas data science library. It will also introduce the abstractions of Series and DataFrame, which are the central data structures for data analytics. Tutorials will be given on how to effectively use functions like groupby, merge, pivot tables, and other useful functions. Students will be able take tabular data and clean it up, manipulate it, as well as run basic inferential statistical analysis.

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

Course Overview

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Skills You Will Gain

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

Course Instructors

Christopher Brooks

Assistant Professor

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 ...

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