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https://github.com/pinedah/escom_programming-for-data-science
This repository contains personal notes, exercises, and programs from the Programming for Data Science course at Instituto Politécnico Nacional (IPN). The course focuses on using Python programming for data handling, statistical analysis, and machine learning techniques.
https://github.com/pinedah/escom_programming-for-data-science
data-science escom matplotlib numpy pandas python python-algorithms python-library
Last synced: 4 days ago
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This repository contains personal notes, exercises, and programs from the Programming for Data Science course at Instituto Politécnico Nacional (IPN). The course focuses on using Python programming for data handling, statistical analysis, and machine learning techniques.
- Host: GitHub
- URL: https://github.com/pinedah/escom_programming-for-data-science
- Owner: Pinedah
- Created: 2024-09-04T16:37:19.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-23T14:30:30.000Z (26 days ago)
- Last Synced: 2024-10-23T17:20:09.354Z (26 days ago)
- Topics: data-science, escom, matplotlib, numpy, pandas, python, python-algorithms, python-library
- Language: Jupyter Notebook
- Homepage:
- Size: 70.3 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Programming for Data Science
## Overview
This repository contains my personal notes, exercises, and programming assignments related to the **Programming for Data Science** course, part of the **Bachelor’s in Data Science** program at **Instituto Politécnico Nacional (IPN)**. The course emphasizes the use of **Python programming** for data handling, statistical analysis, and modeling techniques. All the materials are for academic purposes and reflect my journey through this class.
## Contents
- **Python Scripts:** Various programs written in Python, including data manipulation, statistical methods, and machine learning techniques.
- **Practice Exercises:** Tasks designed to improve understanding of Python programming, data structures, and the application of statistical techniques.
- **Notes:** Personal notes on key topics, such as exploratory data analysis, probability distributions, hypothesis testing, and dimensionality reduction.
- **Projects:** Larger projects focusing on the integration of course concepts, from basic Python programming to advanced statistical modeling and machine learning.
## Course Topics
The course covers essential programming and data analysis skills, including:
- **Python Programming:** Fundamentals of Python, control structures, data handling with libraries like NumPy and Pandas, and file operations.
- **Exploratory Data Analysis (EDA):** Techniques like descriptive statistics, regression analysis, and probability distributions.
- **Modeling Techniques:** Dimensionality reduction, clustering methods, and predictive models using libraries like scikit-learn.
## Why This Repository?
This repository serves as my academic portfolio for the course. It documents the progress I made in learning Python programming and applying it to solve data science problems. The exercises and projects help reinforce core concepts, providing a strong foundation for future applications in data science.
## Usage
The materials in this repository are intended for personal and educational use. They are not to be shared or redistributed without permission. Feel free to explore and use the content for learning purposes, but please give proper credit where applicable.
## About
- **Institution:** Instituto Politécnico Nacional (IPN)
- **Program:** Bachelor's in Data Science (Licenciatura en Ciencia de Datos)
- **Course:** Programming for Data Science
- **Semester:** III
This repository is a reflection of my learning and progress in understanding how to use Python programming for statistical analysis and data modeling, which are essential skills in data science.
yit :)