Data Science Module Information:
Objectives:
Gain an in-depth understanding of different types of data, including Structural Data (e.g., relational databases, CSV, geo-spatial, JSON, graphs) and Non-Structural Data (e.g., text, images, audio).
Learn how to identify and define data-driven problems effectively.
Develop problem-solving skills to address data-related challenges.
Explore a wide range of data analysis techniques, algorithms, and programming tools to develop solutions that meet defined objectives.
Master the art of data visualization, creating meaningful representations across diverse applications.
Build a strong foundation in mathematics, including concepts from statistics, algebra, and calculus, to support data analysis.
Acquire practical programming skills to apply data science concepts effectively.

Topics Covered:
Week 1-2: Regression
Week 3-4: Classification
Week 5: Time Series Analysis
Week 6: Clustering
Week 7-8: Recommender System
Week 9: Social Network Analysis
Week 10: Association Rule Mining

Assessment
In this section, we'll discuss how your performance in this module will be assessed.

Coursework (40%)
The coursework assessment comprises a collection of questions and small problems. Its objective is to evaluate your comprehension of fundamental theoretical topics and basic concepts in data science up to the end of the sixth week.

Case Study (40%)
In this final assessment for the data science module, we will delve into one or two case studies that align with the data science topics and concepts studied throughout the semester. These case studies will be accompanied by relevant questions designed to assess your understanding and application of data science principles.

Class Test (20%)
Throughout the semester, each session (lectures or labs) will include quizzes that correspond to the topics covered in that particular session. These quizzes will be accessible on the Blackboard platform in the form of multiple-choice questions. It's important to note that these quizzes will have a deadline set for completion during the same lecture or lab session.