📊 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

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:

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

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

   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