📘 Big Data Fundamentals

📘 Course Title: Big Data Fundamentals

Course Code: 27021-COs

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🧩 Introduction

In the era of digital transformation, organizations generate and collect vast amounts of data daily. This course introduces learners to the world of Big Data — what it is, why it matters, and how it’s transforming industries. Through a combination of theory, hands-on exercises, and a mini-project, participants will explore the tools, technologies, and methodologies used to store, process, and analyze large datasets.

🎯 Objective

To understand the fundamentals of Big Data, its applications, and the use of Big Data technologies for data analysis.

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📚 Part 1: Theoretical Questions

1. Introduction to Big Data

• What is the definition of Big Data?

• What are the main characteristics of Big Data known as the 5 V’s?

2. Big Data Technologies and Tools

• List some tools and technologies used in Big Data analysis such as Hadoop and Spark.

• What is the difference between traditional storage and distributed storage in Big Data?

3. Applications of Big Data

• Mention three real-world applications of Big Data in the following domains:

  • Healthcare

  • E-commerce

  • Smart Cities

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🔍 Part 2: Data Analysis Exercise

Instructions:

Choose an open dataset from one of the following platforms:

• Kaggle

• Google Dataset Search

Analyze the dataset using a tool of your choice, such as:

• Python (with libraries like Pandas, Matplotlib, or Seaborn)

• Apache Spark

Questions to Answer:

• What are the key insights extracted from the data?

• Are there any clear relationships or patterns in the data?

• What are your recommendations based on the analysis?

Include graphs or visualizations if applicable.

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📝 Part 3: Research and Discussion

Write a short essay (500–800 words) discussing:

• The challenges of handling Big Data, such as:

  • Security

  • Privacy

  • Managing massive volumes of data

• The future trends in Big Data analytics:

  • AI integration

  • Real-time analytics

  • Edge computing

  • Cloud-based solutions

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📤 Submission Guidelines

Submit your report in PDF format including:

• ✅ Answers to the theoretical questions

• ✅ Results of your data analysis with visualizations (if available)

• ✅ The research essay

Additionally:

• Upload your code and analysis files to GitHub or Google Drive

• Include a shareable link in your report

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🧪 Evaluation Criteria