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• Course Title: Introduction to Data Science
• Course Description: An overview of data science principles, including data collection, cleaning, analysis, and visualization. The course will cover statistical methods, machine learning algorithms, and practical applications using Python.
• Course Objectives:
  • Understand the fundamentals of data science and its applications.
  • Learn to collect, clean, and analyze data.
  • Gain proficiency in using Python for data science tasks.
  • Apply statistical methods and machine learning algorithms to real-world problems.
  • Develop skills in data visualization and interpretation.
• Weekly Topics:
  • Week 1: Introduction to Data Science
  • Week 2: Data Collection and Cleaning
  • Week 3: Exploratory Data Analysis
  • Week 4: Statistical Methods
  • Week 5: Introduction to Python for Data Science
  • Week 6: Data Visualization
  • Week 7: Machine Learning Basics
  • Week 8: Supervised Learning
  • Week 9: Unsupervised Learning
  • Week 10: Model Evaluation and Selection
  • Week 11: Advanced Topics in Data Science
  • Week 12: Project Work and Presentations
• Assessment Methods:
  • Assignments (40%)
  • Midterm Exam (20%)
  • Final Project (30%)
  • Class Participation (10%)
• Reading Materials:
  • "Python for Data Analysis" by Wes McKinney
  • "Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
  • Selected research papers and online resources