This course introduces students to Python and form foundation for further analysis of Datasets.
Learning outcome (at course level) | Learning and teaching strategies | Assessment Strategies |
| Approach in teaching: Interactive Lectures, Discussion, Demonstrations, Group activities, Teaching using advanced IT audio-video tools Learning activities for the students: Effective assignments, Giving tasks. | One Annual Practical Test, File/Seminar/Reports etc. |
Data Science, Why Python for Data Science, Jupyter Installation for Python, Features of Python, Python Applications. Flowchart based on simple computations, iterations
Variables, data types, operators & expressions, decision statements. Loop control statements
Introduction to list: Need, creation and accessing list. Inbuilt functions for lists.
Sets and dictionaries: Need, Creation, Operations and in-built functions.
SQL Process, SQL Commands – DDL, DML, DCL, DQL, SQL Constraints, Data Integrity, Data Types, SQL Operators, Expressions, Querying Database, Retrieving result sets, Sub Queries, Syntax for various Clauses of SQL, Functions and Joins.
e-Resources:
Journals: