Big Data 101

This introductory course provides a comprehensive overview of the concepts, technologies, and tools that form the foundation of Big Data. Students will explore how massive volumes of data are collected, stored, processed, and analyzed to uncover meaningful insights and support data-driven decision-making. The course introduces the key principles of data management, distributed computing, and scalable architectures that power modern analytics.

Through a mix of theory and hands-on practice, students will learn to work with real-world datasets using fundamental tools such as Hadoop, Spark, and SQL-based frameworks. Topics include the 5Vs of Big Data (Volume, Velocity, Variety, Veracity, and Value), data pipelines, cloud storage, and the role of machine learning in Big Data ecosystems.

By the end of the course, students will be able to:

• Understand the architecture and components of Big Data systems.

• Identify the challenges and opportunities presented by large-scale data.

• Recognize the ethical, social, and privacy implications of data use.

Target Audience:
Beginners with an interest in data science, analytics, or software engineering. No prior experience with Big Data technologies is required.

Prerequisites:
Basic understanding of programming and databases is recommended but not mandatory.