Data ScienceSemester 2-1 syllabus

The opening semester of the Data Science branch balances foundational mathematics and programming with the student’s first dedicated data-science course. Students move from discrete math and human-values training into Java, advanced data structures, and Python, while a lab-heavy skill track builds hands-on coding fluency early. An audit course in environmental science and a required summer community-service internship round out the term.

Semester load: roughly 15 lecture, 2 tutorial and 10 practical hours per week, totaling 20 credits, plus the non-credited audit course.

Subjects

Discrete Mathematics and Graph Theory

• Unit 1: Propositional and predicate logic — statement forms, truth tables, equivalence, and inference rules
• Unit 2: Set theory, relations, functions, and lattice structures
• Unit 3: Counting principles, permutations/combinations, and recurrence relations solved via generating functions
• Unit 4: Graph fundamentals — representations, isomorphism, and Eulerian/Hamiltonian paths
• Unit 5: Multigraphs, planarity, graph coloring, and spanning-tree algorithms (Prim, Kruskal, BFS/DFS)

Universal Human Values — Understanding Harmony and Ethical Human Conduct

• Unit 1: Foundations of value education and the case for self-exploration
• Unit 2: Harmony within the individual — distinguishing the self from the body
• Unit 3: Harmony in family and society, built around trust and mutual respect
• Unit 4: Harmony with nature and the wider order of existence
• Unit 5: Applying holistic understanding to professional ethics and organizational life
• Delivered as a mix of lecture sessions and practice-based tutorials with reflective exercises

Introduction to Data Science

• Unit 1: What data science is, the end-to-end process, and its place within the big-data ecosystem
• Unit 2: Where machine learning fits into the data science pipeline, common Python tooling, and strategies for large datasets
• Unit 3: NoSQL and distributed storage concepts, including Hadoop, the CAP theorem, and illustrative case studies
• Unit 4: Graph databases (Neo4j/Cypher) and text-mining libraries for unstructured data
• Unit 5: Visualization techniques and building an interactive dashboard as a capstone case study

Advanced Data Structures & Algorithm Analysis

• Unit 1: Complexity analysis fundamentals, AVL trees, and B-trees
• Unit 2: Heaps, graph traversal, and divide-and-conquer algorithms
• Unit 3: Greedy strategies and dynamic programming
• Unit 4: Backtracking and branch-and-bound techniques
• Unit 5: NP-hard and NP-complete problem classes

Object-Oriented Programming through Java

• Unit 1: Java program structure, data types, operators, and control flow
• Unit 2: Classes, objects, constructors, and method design
• Unit 3: Arrays and inheritance mechanics
• Unit 4: Packages, the Java standard library, and exception handling
• Unit 5: String handling, multithreading, JDBC connectivity, and building simple JavaFX interfaces

Data Science Lab

• Practical work on NumPy array creation, reshaping, and slicing
• Pandas-based data wrangling: building dataframes, handling missing values, reading multiple file formats
• Web scraping and preprocessing techniques such as scaling, standardization, and encoding
• Visualization exercises using Matplotlib
• Introductory exposure to NLTK and scikit-learn for text and predictive tasks
• Total: 3 practical hours per week, 1.5 credits

Object-Oriented Programming through Java Lab

• Core language exercises: primitive types, control structures, and basic algorithms (searching, sorting)
• Class design, constructors, method and constructor overloading
• Single and multilevel inheritance, abstract classes, and interfaces
• Exception handling, both built-in and custom
• Multithreading exercises and producer-consumer style problems
• JavaFX GUI building and JDBC database connectivity
• Total: 3 practical hours per week, 1.5 credits

Python Programming (Skill Enhancement Course)

• Unit 1: Language basics, control flow, and the Jupyter/Anaconda environment
• Unit 2: Functions, string handling, and list operations
• Unit 3: Dictionaries, tuples, and sets
• Unit 4: File handling and object-oriented programming in Python
• Unit 5: An introduction to data-science-oriented Python — JSON handling, NumPy, and Pandas
• Paired with weekly lab exercises that reinforce each unit’s concepts
• Total: 1 tutorial and 2 practical hours per week, 2 credits

Environmental Science (Audit Course)

• Unit 1: Multidisciplinary scope of environmental studies and natural resource use
• Unit 2: Ecosystem structure, biodiversity, and conservation
• Unit 3: Pollution types, causes, and solid-waste management
• Unit 4: Sustainable development, environmental law, and disaster-related social issues
• Unit 5: Population growth, human health, and field-based environmental observation
• Ungraded audit course; no credits attached