Syllabus (COMPUTER SCIENCE)

Course Type: SEC-1

Semester: 1

Course Code: BCOSSEC01T

Course Title: Data Science

(L-P-Tu): 3-0-0

Credit: 3

Practical/Theory: Theory

Course Objective: • Understanding key technologies of Data Science. • Understand the concept of Association Rules, Classification, Regression, and Clustering. • Analyse Data Mining Models. • Demonstrate knowledge of Data Analysis Techniques.

Learning Outcome: • Ability to perform data pre-processing. • Ability to apply Mining Techniques. • Implementation of Data Mining tools to solve complex problems. • Hands-on experience with Data Analysis using Python Programming.

Fundamental of Data Science and Data Mining:

Data Analysis, Data Analytics, Need for analytics, Introduction to Data Warehouse, OLAP, OLTP, Data pre-process, Structured and Unstructured Data. Dataset Centralization, Basic insights from Datasets, cleaning and preparing the data, Data mart, Data Mining Concepts, Data Mining Algorithms, Classification, Association Rule Mining. (15 Lectures)

Introduction to AI and Statistical Methods:

Concepts of AI, Types of Machine Learning, Supervised Learning, Unsupervised Learning, Mean, Median, Mode, Standard Deviation, Correlation, Regression, Covariance, Curve Fitting, Principal Component Analysis, Clustering. (20 Lectures)

Python Fundamentals:

Object Oriented Programming concept, class, object, methods, python data structure, control statements, user defined module, packages in python, file handling in python. (10 Lectures)

Reference :

1. David Ascher and Mark Lutz, Learning Python, O’Reilly Media.

2. N G Das, “Statistical Methods”, Mc Graw Hill Publication.

3. ReemaThareja, “Python Programming using Problem Solving approach”, Oxford University press.

4. Wes Mckinney “Python for Data Analysis”, First edition, O’Reilly Media.

5. I. T. Jolliffe, “Principal Component Analysis”, Springer.

6. C. O’Neil, & R. Schutt, Doing Data Science: Straight Talk from the Frontline, O’Reilly Media.

7. Allen Downey, Jeffrey Elkner,Chris Meyers, Learning with Python, Dreamtech Press.

8. David Taieb, Data Analysis with Python: A Modern Approach, Packt Publishing.

9. Tomm Mitchell, “Machine Learning”, Mc Graw Hill Publication.