IT25201 Foundations of Data Science using Python – Semester II – IT – R-2025

Subject Code & Name: IT25201 – Foundations of Data Science using Python

Regulation: R-2025

Semester: II (Second Semester)

Branch: B.Tech. IT (IT)

Credits / L-T-P: 4 Credits | L-T-P: 3-0-2

Course Objectives

• To equip students with a strong foundational understanding of data science concepts.
• To collect, clean, manipulate, and analyse data using Python libraries.
• To perform data operations and derive insights from real-world datasets.

Full Unit-wise Syllabus

Unit I – Python Language Basics and Data Structures

Python Language Basics – Scalar Types – Control Flow. Data Structures and Sequences: Tuple – List – Built-in Sequence Functions – dict – set – List, Set, and Dict Comprehensions. Functions: Namespaces, Scope, and Local Functions – Returning Multiple Values – Functions Are Objects – Files and the Operating System.

Practical: Programs using Data Frames; Programs using functions and files.

Unit II – Numpy Basics

The NumPy ndarray: A Multidimensional Array Object – Universal Functions: Fast Element-Wise Array Functions – Array-Oriented Programming with Arrays – File Input and Output with Arrays – Linear Algebra – Pseudorandom Number Generation.

Practical: Programs using numpy; Programs to solve linear algebra problems with numpy functions.

Unit III – Pandas Basics

Introduction to pandas Data Structures – Loading and Understanding Data – Data aggregation for computing Descriptive Statistics – Data Cleaning and Preprocessing.

Practical: Programs using numpy; Solving linear algebra problems.

Unit IV – Data Loading, Storage, and File Formats

Reading and Writing Data in Text Format – Binary Data Formats – Interacting with Web APIs – Interacting with Databases.

Practical: Data and Databases; Web APIs.

Unit V – Data Exploration and Wrangling

Data Transformation – String Manipulation. Data Wrangling: Hierarchical Indexing – Combining and Merging Datasets – Reshaping and Pivoting. Data Aggregation and Group Operations: GroupBy Mechanics – Data Aggregation – Apply: General split-apply-combine – Pivot Tables and Cross-Tabulation – Date and Time Data Types.

Practical: String manipulations; Data wrangling; Data aggregation operations; Handle time series data.

Unit VI – Data Visualization

Introduction to Data Visualization – Visualizing categorical data, visualizing time series data, Visualizing multiple variables – Visualizing Distribution & Relationships – Multivariate and Time Series Visualization exploration.

Practical: Visualization of Different kinds of Data; Distribution Analysis.

Course Outcomes (COs)

• CO1: Develop simple programs in Python with built-in data structures.
• CO2: Apply NumPy and Pandas libraries to organize and manipulate data efficiently.
• CO3: Design and analyze solutions involving APIs, databases, and real-world datasets.
• CO4: Enhance life-long learning skills to explore new data science tools and libraries beyond the classroom.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Assignments (10%), Quiz (5%), Project based learning (20%), Flipped Classroom (5%), Review of GATE questions (10%) & Internal Assessment 50%

Source: Official Anna University – B.Tech. Information Technology R-2025 Syllabus
Last Updated: September 2026