Data Analytics Using Python

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.

Key topics: 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) Practical experience: 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: Prior knowledge of the Python language is required for this course.

Students should have completed Intro to Programming (Python) or Crash Course in Python for Data Analytics ( CSE-41386 ) or have equivalent knowledge before taking this course.

Next steps: After completion of this course, students are encouraged to consider taking additional coursework in the Machine Learning Methods or Python Programming certificates.