Week 7 : Supervised and Unsupervised Learning


Course Objectives:

  • Understand the fundamental concepts and principles of data science, including data collection, preprocessing, analysis, and interpretation.
  • Gain familiarity with machine learning algorithms and their practical applications.
  • Develop proficiency in using data science tools and programming languages.
  • Engage in critical thinking and problem-solving through project-based assignments.
  • Explore the ethical considerations associated with data-driven decision-making.
  • Stay informed about current trends and developments in data science and artificial intelligence.

Learning Outcomes:

  • Explain the difference between different measures of centrality and variability (means vs. medians, variance vs. interquartile range, etc.)
  • Convert a raw data source into a version appropriate for downstream analysis using Python. | Week 4 & 5 .. cont to 7
  • Select the appropriate evaluation measure for the dataset and task being solved
  • Articulate the difference between supervised and unsupervised machine learning, as well as select the appropriate methodology for a given problem | Week 6 .. cont to 7
  • Demonstrate awareness of bias and ethics in data science.




Homework 4

Weekly Checkin

  • Weekly Checkin for Week 6 | Due 5 pm , July 23, Sunday ( Based on Lecture 6.1,6.2,6.3 )
  • Weekly Checkin for Week 7 opens