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CS3352Actively Used

Foundations of Data Science

This course is part of the B.E. Computer Science Engineering curriculum under Anna University Regulation 2021. The knowledge from this course continues to be actively applied in professional software development.

Semester 3 (Second Year)
3 Credits
45 Lecture Hours

Course Overview

  • UniversityAnna University
  • Regulation2021
  • Semester3
  • Credits3
  • TypeCore
  • Units5

Course Objectives

1

To understand data science fundamentals

2

To learn data preprocessing techniques

3

To understand statistical analysis

4

To learn data visualization

5

To understand machine learning basics

Syllabus

Detailed unit-wise breakdown of the course curriculum as per Anna University Regulation 2021.

1

INTRODUCTION TO DATA SCIENCE

9 Hours
Data Science overviewBig Data and Data ScienceData Science processData types and sourcesData science tools and technologiesPython for Data ScienceNumPy and Pandas basics
2

DATA PREPROCESSING

9 Hours
Data cleaningHandling missing dataData transformationNormalization and standardizationFeature engineeringFeature selectionDimensionality reduction
3

STATISTICAL ANALYSIS

9 Hours
Descriptive statisticsProbability distributionsHypothesis testingCorrelation analysisRegression analysisANOVAStatistical inference
4

DATA VISUALIZATION

9 Hours
Principles of visualizationMatplotlib and SeabornCharts and graphsInteractive visualizationsDashboard creationStorytelling with data
5

INTRODUCTION TO MACHINE LEARNING

9 Hours
Machine learning basicsSupervised vs unsupervised learningClassification algorithmsClustering algorithmsModel evaluationCross-validation

Course Outcomes

Upon completion of this course, students will be able to:

CO1

Understand data science workflow

CO2

Preprocess and clean data

CO3

Perform statistical analysis

CO4

Create effective visualizations

CO5

Apply basic machine learning algorithms

Industry Application & Relevance

How the concepts learned in this course are applied in real-world software development projects across Banking, Healthcare, and Enterprise domains over 20+ years of experience.

Professional Application

Big data analytics, business intelligence, data-driven decisions

Textbooks & References

Textbooks

  • Joel Grus, 'Data Science from Scratch', O'Reilly
  • Wes McKinney, 'Python for Data Analysis', O'Reilly

Reference Books

  • Jake VanderPlas, 'Python Data Science Handbook', O'Reilly
  • Foster Provost, Tom Fawcett, 'Data Science for Business', O'Reilly

Related Courses from Semester 3

Other courses from the same semester that are actively used in professional work.

MA3354Discrete Mathematics

Algorithm analysis, cryptography, database theory

CS3351Digital Principles and Computer Organization

Hardware-software interface, performance optimization

CS3301Data Structures

Core of all software development - Arrays, Trees, Graphs, HashMaps

CS3391Object Oriented Programming

Java, Spring Boot, enterprise application development