CMPSC X404.CODE - Data Analytics Using Python
41362597
Delivery Options
Online Asynchronous
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Course Description
In this course, you will learn the rich set of tools, libraries, and packages that comprise the highly popular and practical Python data analysis ecosystem. This course is primarily taught via screen-sharing programming videos. Topics taught range from basic Python syntax all the way to more advanced topics like supervised and unsupervised machine learning techniques.
Prior knowledge of the Python language is required for this course. Students should have completed Intro to Programming (Python) or have equivalent knowledge before taking this course.
Learner Outcomes
- Python Basics (variables, strings, simple math, conditional logic, for loops, lists, tuples, dictionaries, etc.)
- Using the Pandas library to manipulate data (filtering and sorting data, combining files, GroupBy, etc.)
- Plotting data in Python using Matplotlib and Seaborn
- Logistic Regression using Scikit-Learn
- Classification and Regression Metrics
- Decision Trees using Scikit-Learn
- Random Forests (Scikit-Learn)
- Clustering Algorithms (K-Means, Hierarchical Clustering)
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