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CSC 202 — An Introduction To Data Science

Computer Science · 200 Level · Second Semester

About this course

CSC 202 An Introduction To Data Science is a 200 Level course in the Computer Science department, taught in the Second Semester. On La-Net Hub you can watch all 30 recorded lectures for free, grouped into 10 series, read the course lecture note online or download it as a PDF, and test yourself with practice questions.

The Applied Case Studies in Data Science topic focuses on practical, real-world applications of data science concepts across diverse domains such as business, healthcare, social sciences, and technology. The course objective is to enable learners to integrate theoretical knowledge with hands-on problem-solving, applying data collection, analysis, visualization, and machine learning techniques to real datasets. By working through case studies, students will develop critical thinking, adaptability, and the ability to design data-driven solutions that address complex challenges in professional and societal contexts.

The Data Types, Sources, and Workflow topic explores the fundamental structures of data, the diverse origins from which data is collected, and the systematic processes used to manage and analyze it. The course objective is to help learners understand how to classify data into meaningful types, identify reliable sources, and design efficient workflows for data preparation, integration, and analysis. By mastering these concepts, students will be able to streamline data-driven tasks, ensure accuracy, and build a strong foundation for advanced analytical and computational applications.

Lectures in this course

Series 1: Applied Case Studies in Data Science (SERIES 5) · Part 1

  1. Applied Case Studies in Data Science 1 10 minutes 18 seconds
  2. Applied Case Studies in Data Science 2 9 minutes 49 seconds
  3. Applied Case Studies in Data Science 3 9 minutes 8 seconds

Series 2: Applied Case Studies in Data Science (SERIES 5) · Part 2

  1. Applied Case Studies in Data Science 1 9 minutes 28 seconds
  2. Applied Case Studies in Data Science 2 9 minutes 1 second
  3. Applied Case Studies in Data Science 3 8 minutes 24 seconds

Series 3: Data Types, Sources, and Workflow (SERIES 2) · Part 1

  1. Data Types, Sources, and Workflow 1 11 minutes 11 seconds
  2. Data Types, Sources, and Workflow 2 10 minutes 45 seconds
  3. Data Types, Sources, and Workflow 3 9 minutes 55 seconds

Series 4: Data Types, Sources, and Workflow (SERIES 2) · Part 2

  1. Data Types, Sources, and Workflow 1 10 minutes 13 seconds
  2. Data Types, Sources, and Workflow 2 9 minutes 56 seconds
  3. Data Types, Sources, and Workflow 3 9 minutes 8 seconds

Series 5: Foundations of Data Science (SERIES 1) · Part 1

  1. Foundations of Data Science 1 8 minutes 55 seconds
  2. Foundations of Data Science 2 8 minutes 19 seconds
  3. Foundations of Data Science 3 8 minutes 21 seconds

Series 6: Foundations of Data Science (SERIES 1) · Part 2

  1. Foundations of Data Science 1 8 minutes 11 seconds
  2. Foundations of Data Science 2 7 minutes 39 seconds
  3. Foundations of Data Science 3 7 minutes 40 seconds

Series 7: Statistics, Mathematics, and Programming Tools (SERIES 3) · Part 1

  1. Statistics, Mathematics, and Programming Tools 1 9 minutes 46 seconds
  2. Statistics, Mathematics, and Programming Tools 2 8 minutes 54 seconds
  3. Statistics, Mathematics, and Programming Tools 3 10 minutes

Series 8: Statistics, Mathematics, and Programming Tools (SERIES 3) · Part 2

  1. Statistics, Mathematics, and Programming Tools 1 9 minutes
  2. Statistics, Mathematics, and Programming Tools 2 8 minutes 9 seconds
  3. Statistics, Mathematics, and Programming Tools 9 minutes 12 seconds

Series 9: Visualisation, Machine Learning, and Ethnics (SERIES 4) · Part 1

  1. Visualisation, Machine Learning, and Ethnics 1 8 minutes 24 seconds
  2. Visualisation, Machine Learning, and Ethnics 2 7 minutes 29 seconds
  3. Visualisation, Machine Learning, and Ethnics 3 8 minutes 30 seconds

Series 10: Visualisation, Machine Learning, and Ethnics (SERIES 4) · Part 2

  1. Visualisation, Machine Learning, and Ethnics 1 7 minutes 40 seconds
  2. Visualisation, Machine Learning, and Ethnics 2 6 minutes 53 seconds
  3. Visualisation, Machine Learning, and Ethnics 3 7 minutes 51 seconds