Skip to content

Latest commit

 

History

History
92 lines (65 loc) · 2.53 KB

README.md

File metadata and controls

92 lines (65 loc) · 2.53 KB

CS 5622: Applied Data Science with Python

University of Idaho

Course Overview

CS 5622 Applied Data Science with Python is a graduate-level course that introduces students to the complete lifecycle of a data science project, from data collection to model deployment. The course emphasizes the practical application of Python programming and modern data science libraries to solve real-world problems.

This course is joint-listed with CS 4622 (undergraduate equivalent).

Curriculum Structure

The curriculum is organized into four main themes:

1. Python Fundamentals

  • Review of Python programming basics
  • Functions, modules, and best practices
  • Object-oriented programming concepts relevant to data science

2. Data Engineering

  • Data collection and preprocessing
  • Data exploration and visualization
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn

3. Model Engineering

  • Predictive model design, selection, and evaluation
  • Key application domains:
    • Image Processing
    • Natural Language Processing (NLP)
    • Time Series Analysis
  • Frameworks: Scikit-Learn, Keras, TensorFlow, PyTorch

4. Data Science Operations (DSOps)

  • Deploying data science projects into production
  • Model serving and scaling
  • Performance monitoring and diagnosis
  • Ensuring reproducibility and version control

Weekly Assignments

This section documents weekly tasks, projects, and deliverables. This will be updated regularly.

  • Week 1:
    Assignment description goes here…

  • Week 2:
    Assignment description goes here…

  • Week 3:
    Assignment description goes here…

(Continue for additional weeks...)

Prerequisites

  • Strong Python programming skills
  • Familiarity with statistics, probability, and linear algebra
  • Prior experience with data analysis is helpful but not required

Tools & Libraries

  • Python 3.x
  • NumPy, Pandas, Matplotlib, Seaborn
  • Scikit-Learn
  • TensorFlow, Keras, PyTorch
  • Jupyter Notebook / JupyterLab
  • Git & GitHub for version control

Grading (Tentative)

  • Assignments: 40%
  • Midterm Project: 20%
  • Final Project: 30%
  • Participation & Discussion: 10%

Instructor Information

  • Instructor: [Add Name]
  • Email: [Add Email]
  • Office Hours: [Add Schedule]

License

This repository is for educational purposes only. All course materials belong to the University of Idaho and respective authors.