• 🎯 Learn Scala Data Analysis with Free, Hands-On Labs
• 🌟 Why Choose This Repository?
• 🏗️ Modern Project Structure
• 🚀 Quick Start
• 📚 Structured Learning Path
• 🛠️ Core Technologies Covered
• 📖 Comprehensive Lab Curriculum
• 🔧 Development Workflow
• 📊 Real-World Datasets Included
• 🤝 Contributing & Community
• 🔗 Related Practice Repositories
• 📄 License
• 🔗 Additional Resources
• 🎓 Educational Mission

A comprehensive Scala data analysis learning environment designed for developers, data engineers, and data scientists who want to master modern data analysis concepts through practical, hands-on experience.

7 progressive chapters with 50+ exercises. Completely free and open source. Built for learners, by learners.

This educational resource bridges the gap between theoretical knowledge and practical skills in Scala data analysis:

• 🎓 Learn by Doing: Progressive hands-on labs build real-world skills
• 🔧 Vendor Independent: Master concepts applicable across all platforms
• 🏭 Production Patterns: Learn best practices used in real data engineering
• ⚡ Multi-Technology Experience: Work with Breeze, Spark, MLlib, and streaming
• 👥 Community Driven: Built and improved by the data engineering community

scala-dataanalysis-code-practice/
├── src/main/scala/com/scalaanalysis/  # Unified source code by chapter
├── labs/                              # 7 comprehensive lab guides
├── docs/                              # Complete documentation
├── wiki/                              # Detailed wiki with tutorials
├── scripts/                           # Automation and utility scripts
├── data/                              # Sample datasets for practice
├── config/                            # Configuration files
├── docker-compose.yaml                # Docker setup for easy deployment
└── .github/workflows/                 # CI/CD automation

• JDK 1.7+ Java Development Kit
• Scala 2.10.4+ Scala programming language
• SBT 0.13.8+ Scala Build Tool
• Python 3.8+ For utility scripts

# 1. Clone the repository
git clone https://github.com/nellaivijay/scala-dataanalysis-code-practice.git
cd scala-dataanalysis-code-practice

# 2. Run setup script
./scripts/setup.sh

# 3. Compile and start learning
sbt clean compile

cp .env.example .env
docker-compose up -d

• Chapter 1: Breeze numerical computing & Spark fundamentals
• Chapter 2: Spark DataFrames and basic operations

• Chapter 3: Data loading, cleaning, and preparation
• Chapter 4: Data visualization with Zeppelin and Bokeh

• Chapter 5: Machine learning with MLlib
• Chapter 6: Scaling and deployment strategies
• Chapter 7: Streaming and GraphX

• Vectors and matrices operations
• Random number generation
• Linear algebra fundamentals
• Skills: Numerical computing, Breeze library

• Spark DataFrames and RDDs
• Data loading and transformation
• Basic data analysis
• Skills: Apache Spark, distributed computing

• CSV, JSON, Parquet data loading
• Data cleaning and preprocessing
• Missing value handling
• Skills: Data engineering, ETL processes

• Apache Zeppelin integration
• Bokeh Scala visualizations
• Interactive dashboards
• Skills: Data visualization, storytelling

• Linear regression and classification
• Clustering with K-Means
• Dimensionality reduction with PCA
• Skills: Machine learning, MLlib

• Spark cluster deployment
• Performance tuning and optimization
• Resource management
• Skills: Production deployment, DevOps

• Real-time streaming with Kafka
• Graph processing with GraphX
• Twitter integration
• Skills: Streaming, graph algorithms, real-time analytics

# Compile the project
sbt compile

# Run tests
sbt test

# Create JAR package
sbt package

# Start Scala REPL
sbt console

# Run specific class
sbt 'runMain com.scalaanalysis.chapter1.YourClassName'

# Compile specific chapter
./scripts/build_helper.sh chapter1 compile

# Package the project
./scripts/build_helper.sh chapter1 package

• 🌸 Iris Dataset: Classic machine learning dataset (150 samples)
• 🎓 Student Data: Educational performance metrics (1,000+ records)
• 📈 Dow Jones Index: Financial time series data
• 🚗 MT Cars: Automobile performance data
• 👤 Profile Data: User profile information

This is an educational repository built for the community. We welcome contributions!

• 📝 Improve documentation
• 🐛 Report bugs and issues
• 💡 Suggest new lab topics
• 🔧 Fix bugs and add features
• 🌍 Translate content

See CONTRIBUTING.md for detailed guidelines.

• 📖 Wiki Documentation
• 💬 GitHub Discussions
• 🐛 Issue Tracker
• ⭐ Star the repo to show your support!

Continue your learning journey with these related repositories:

• 🤖 DSPy Code Practice - Declarative LLM programming
• 🧠 LLM Fine-Tuning Practice - Model fine-tuning techniques

• 🦆 DuckDB Code Practice - Analytics & SQL optimization
• ⚡ Apache Spark Code Practice - Big data processing
• 🏔️ Apache Iceberg Code Practice - Lakehouse architecture
• 🔧 Apache Beam Code Practice - Data pipelines

• 📚 Awesome My Notes - Comprehensive technical notes and learning resources

Apache License 2.0 - Free for educational and commercial use

• Scala Documentation
• Apache Spark Documentation
• Breeze Documentation

• Setup Guide - Detailed installation instructions
• Troubleshooting - Common issues and solutions
• Dataset Documentation - Available datasets and schemas

• Wiki Home - Comprehensive tutorials
• Installation Guide - Step-by-step setup
• Quick Start - Get started fast

This repository helps data professionals:

• 🎯 Practice Scala data analysis and data science concepts
• 🌐 Learn vendor-independent data engineering patterns
• ⚡ Understand modern data processing with Spark and Breeze
• 🤖 Build hands-on experience with machine learning and streaming
• 🚀 Prepare for real-world data science challenges

Disclaimer: This is an independent educational resource for learning Scala data analysis and data science concepts. It is not affiliated with, endorsed by, or sponsored by Apache Spark, Scala, or any vendor.

Ready to start learning? Begin with Lab 1: Breeze Basics or check out our Quick Start Guide!

⭐ Star this repository to help others discover it!