DSMA 113: Introduction to Python Programming

Welcome to the official repository for DSMA 113: Introduction to Python Programming, part of the Bachelor in Data Science program at Kathmandu University. This course provides a comprehensive foundation in Python, covering fundamental programming concepts, data structures, object-oriented principles, and essential libraries for data science.

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Course Information

• Course Code: DSMA 113
• Credits: 3
• Lecture Hours: 45
• Institution: Kathmandu University
• Program: Bachelor in Data Science

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Course Objectives

By the end of this course, students will be able to:

• Understand essential programming concepts: syntax, data types, variables, operators.
• Develop efficient algorithms and apply computational thinking.
• Utilize Python’s built-in data structures (lists, tuples, dictionaries, sets).
• Implement object-oriented programming (OOP) concepts.
• Handle files for reading/writing operations.
• Utilize popular Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.

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Course Outline

Unit 1: Introduction

• Overview of Python, installation, using IDLE and Jupyter Notebook
• Virtual environments, comments, indentation, tokens, operators

Unit 2: Decision and Loops

• Conditional Statements: if, if-else, if-elif-else
• Looping Statements: for, while
• Exception Handling

Unit 3: Built-In Data Types

• Numerics, Strings, Lists, Tuples, Sets, Dictionaries

Unit 4: Functions

• Benefits, creation, argument handling, lambda and recursive functions

Unit 5: Object-Oriented Programming (OOP)

• Classes, encapsulation, inheritance, polymorphism, data hiding

Unit 6: File Handling

• File modes, reading/writing, os module

Unit 7: Common Python Libraries

• NumPy: Arrays, indexing, broadcasting, statistical functions
• Pandas: DataFrames, indexing, missing data, merging, CSVs
• Matplotlib: Plotting and visualization (scatter, bar, histogram, etc.)

Unit 8: Developing a Project

• Project design, module decomposition, data flow diagrams

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Laboratory Work and Final Project

This repository contains lab exercises for each course unit. The final project will apply skills learned throughout the course. Project ideas include:

1. Exploratory Data Analysis (EDA): Analyzing datasets to uncover patterns.
2. Regression Analysis: Implementing regression models for predicting outcomes.

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