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 using Scikit-Learn
- Classification and Regression Metrics
- Decision Trees using Scikit-Learn
- Random Forests (Scikit-Learn)
- Clustering Algorithms (K-Means, Hierarchical Clustering)
- Hands on programming assignments that are reviewed weekly via screen sharing videos
- Student's will be tasked to complete a final project, utilizing skills learned throughout the course
Course typically offered: Online, quarterly.
Software: Students will use Python to complete hands-on assignments. These tools are free and open-source.
Prerequisites: None, although general programming knowledge will be beneficial.
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, R for Data Analytics
+ Expand All
1/6/2020 - 3/6/2020