Data-Intensive Programming Exercises & Projects

This repository contains the exercises and projects completed for the Data-Intensive Programming course at Tampere University.

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Repository Structure

• Exercises/
  Contains course assignments focused on foundational concepts in data-intensive programming. These may include algorithmic tasks, data processing exercises, or hands-on coding assignments.

• Projects/
  Contains larger-scale, self-contained projects applying data-intensive programming techniques. These may involve real-world data sets, performance optimizations, or interactive applications.

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Technologies Used

• Scala – Appears to be the primary language (≈50% of the codebase)
• Python – Also heavily used (≈47% of the code)
• Jupyter Notebooks – Likely used for exploratory data analysis or interactive demonstrations (≈3%)

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📘 Project Descriptions

Exercises

• Exercise 1 – Data Processing Basics
  Learn to manipulate collections, perform transformations, and implement simple algorithms in Scala/Python.

• Exercise 2 – Parallelism
  Use parallel collections and concurrency constructs to speed up computation on large datasets.

• Exercise 3 – File & Data Formats
  Parse structured data (CSV, JSON) and implement simple aggregations.

• Exercise 4 – Distributed Data Processing
  Introduction to MapReduce and Spark, implementing word count and other classic tasks.

• (add more based on your repo content)

Final Project

The final project applies the course concepts to a larger data-intensive problem, such as:

• Analyzing a real-world dataset (e.g., logs, social media, sensor data).
• Implementing efficient data pipelines for ETL (Extract–Transform–Load).
• Using Spark/parallel algorithms for large-scale analysis.
• Presenting findings in a short report or notebook.

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🚀 How to Run

Requirements

• Scala 2.13+ (or as specified in repo)
• Python 3.8+
• Jupyter Notebook (if using .ipynb files)
• Optional: Apache Spark (if project/exercises use it)