https://github.com/n-sapkota/dsma-113-python-programming
This repository is for DSMA 113: Introduction to Python Programming at Kathmandu University. The course introduces foundational Python concepts, including syntax, data structures, object-oriented programming, and essential libraries like NumPy, Pandas, and Matplotlib.
https://github.com/n-sapkota/dsma-113-python-programming
data-science jupyter-notebook machine-learning python3
Last synced: 3 months ago
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This repository is for DSMA 113: Introduction to Python Programming at Kathmandu University. The course introduces foundational Python concepts, including syntax, data structures, object-oriented programming, and essential libraries like NumPy, Pandas, and Matplotlib.
- Host: GitHub
- URL: https://github.com/n-sapkota/dsma-113-python-programming
- Owner: n-sapkota
- Created: 2024-11-14T15:11:17.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-12T16:49:01.000Z (6 months ago)
- Last Synced: 2025-01-24T01:52:08.814Z (5 months ago)
- Topics: data-science, jupyter-notebook, machine-learning, python3
- Homepage:
- Size: 22.8 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 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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