https://github.com/ryanga09/digitalent_fundamentaldatascience-selfpractice

A repository of hands-on projects from DigiTalent’s Fundamental Data Science training, covering web scraping, data exploration, data cleaning, and data annotation. Includes Jupyter notebooks and example code for practical learning.
https://github.com/ryanga09/digitalent_fundamentaldatascience-selfpractice

data data-analysis data-science data-visualization dataset digitalent komdigi notebook-jupyter notebooks

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A repository of hands-on projects from DigiTalent’s Fundamental Data Science training, covering web scraping, data exploration, data cleaning, and data annotation. Includes Jupyter notebooks and example code for practical learning.

• Host: GitHub
• URL: https://github.com/ryanga09/digitalent_fundamentaldatascience-selfpractice
• Owner: RyanGA09
• Created: 2025-06-27T02:09:43.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-08-01T12:39:05.000Z (11 months ago)
• Last Synced: 2025-08-01T14:45:04.160Z (11 months ago)
• Topics: data, data-analysis, data-science, data-visualization, dataset, digitalent, komdigi, notebook-jupyter, notebooks
• Language: Jupyter Notebook
• Homepage:
• Size: 1.17 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📊 DigiTalent Fundamental Data Science - Self Practice

## 📅 Created On

June 2025

## 📜 Description

This repository contains hands-on exercises and learning materials from DigiTalent’s _Fundamental Data Science_ training. The focus topics include:

- 🌐 Data Scraping

  Learn how to acquire data from various web sources using automated tools.

  Subtopics:

  - What is Data?

  - Data Collection Methods

  - Data Scraping Tools

  - Data Integrity & Ethics

  - Hands-on Practice through the included self-practice exercises

- 📈 Data Exploration

  Analyze and understand the structure and patterns in your data.

  Subtopics:

  - Data Understanding

  - Reviewing Dataset Structure

  - Data Validation Techniques

  - Hands-on Practice through the included self-practice exercises

- 🧹 Data Cleansing

  Clean and refine your dataset to ensure quality and reliability.

  Subtopics:

  - Data Cleaning Concepts

  - Handling Missing & Duplicate Values

  - Data Reduction Strategies

  - Hands-on Practice through the included self-practice exercises

- 🏷️ Data Annotation

  Prepare labeled datasets for use in supervised machine learning tasks.

  Subtopics:

  - Defining Labels & Categories

  - Data Annotation Techniques

  - Manual & Assisted Labeling Tools

  - Hands-on Practice through the included self-practice exercises

## 🗂️ Repository Structure

```bash

DigiTalentPractice-FundamentalDataScience/

├── data/                          # Contains raw/external datasets

│   ├── Data_Nasabah.csv           # Local dataset

│   └── train_prices.csv           # Kaggle dataset (not included in repo)

│

├── notebooks/                     # Jupyter notebooks

│   ├── self_practice-1.ipynb

│   ├── self_practice-2.ipynb

│   ├── self_practice-3.ipynb

│   └── self_practice-4.ipynb

│

├── requirements.txt               # Python dependencies

├── README.md                      # Project overview and setup instructions

└── .gitignore                     # Files/folders to exclude from version control

```

**⚠️ Note: data/train_prices.csv is downloaded via the Kaggle API and is not included in this repository. Make sure to download it manually before running related notebooks.**

## 🚀 How to Use

1. 📥 Clone this repository to your local machine:

   ```bash

   git clone https://github.com/RyanGA09/DigiTalentPractice-FundamentalDataScience.git

   ```

2. 📦 Install the environment (recommended to use venv or conda):

   ```bash

   pip install -r requirements.txt

   ```

3. 📘 Open the notebook corresponding to the topic you want to learn and run the code cells sequentially.

## 👨‍💻 Author

Ryan Gading Abdullah

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