math3080

Math 3080 - Fundamentals of Data Science - Snow College

The materials in this GitHub Repository include information about the course (Syllabus, Schedule, etc.) as well as lecture notes and codes that we use in class.

Introduction

• Introduction to Data Science
• Nature of Data (ipynb)
• Python Basics (ipynb)
  • Pandas (ipynb)

ETL (Extract, Transform, Loading data)

• Loading Data (ipynb)
  • Webscraping
  • Loading Data from an API (ipynb)
• Data Wrangling
  • Missing Values (ipynb)
  • Feature Engineering
  • Data Wrangling (ipynb)
  • Merging Data

EDA (Exploratory Data Analysis)

• Graphing in Matplotlib (ipynb)
• Graphing in Seaborn
• Ethics of Graphing

Machine Learning - modeling

• Statistical Learning
  • Cross Validation
  • Feature Scaling
• Regression
  • Regression Models
  • Linear Regression
• Classification
  • Classification Models
  • k-Nearest Neighbors (kNN)
• Clustering (ipynb)
  • Clustering Models
  • KMeans
• Ethics
  • Responsible AI