Data Analytics Using Python
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.
- Installing Python/Jupyter/IPython on Windows and Mac
- 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, Decision Trees, and Random Forests using Scikit-Learn
- Classification and Regression Metrics
- Clustering Algorithms (K-Means, Hierarchical Clustering)
- Dimensionality Reduction (Principal Component Analysis)
- Hands on programming assignments that are reviewed weekly via screen sharing videos
- The primary assignment for this class is a project of a student’s choice.
Course typically offered: Online in Fall, Winter, Spring, and Summer.
Software: Students will use Python 3.x/Jupyter/IPython to complete hands-on assignments. These tools are free and open-source.
Prerequisites: None. General programming knowledge is helpful.
Next steps: After completion of this course, students are encouraged to consider taking additional coursework in the Machine Learning Methods or Python Programming certificates.
Contact: For more information about this course, please contact email@example.com.
Course Number: CSE-41204
Credit: 3.00 unit(s)
Related Certificate Programs: Business Intelligence Analysis, Data Mining for Advanced Analytics, Geographic Information Systems, Machine Learning Methods, Python Programming
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6/27/2019 - 8/22/2019
9/24/2019 - 11/19/2019