Syllabus (Key Info)


The course introduces students to the principles of data science, which are essential for computer scientists to make effective decisions in their professional careers. In today’s data-driven world, a wide range of computer science sub-disciplines heavily rely on data collection, analysis, and interpretation. With the pervasive presence of artificial intelligence (AI) in our lives, understanding the basics of how these systems work is becoming increasingly important. Additionally, it covers the basics of artificial intelligence (AI) systems and examines practical use cases, current news, and ethical considerations through readings and discussions.

Course Objective

Course Objectives

This course aims to introduce students to the principles and techniques of data science, enabling them to make effective decisions in their computer science careers. During this course, the student will,

  • Understand the fundamental concepts and principles of data science, including data collection, preprocessing, analysis, and interpretation.

  • Apply data analysis and visualization techniques to derive insights from diverse datasets on Week 6

  • 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.

Expected Learning Outcomes

A student who successfully completes this course will be able to:

  • 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

  • Write appropriate visualizations for different sources and types of data | Week 6

  • Explain why we seek to build machine learning models that generalize rather than memorize their input. | Week 6

  • Explain the different uses for training, validation, and testing datasets | Week 6

  • 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

  • Demonstrate awareness of bias and ethics in data science.