Big Data Analytics and Management

Big Data Analytics and Management is a broad field focused on the challenges and benefits of dealing with large amounts of data. In the course, we started by learning PostgreSQL, getting a handle on how to manage relational databases using SQL. This basic skill is essential for working with structured data, which is central to traditional database systems.

Next, we moved on to NoSQL databases, exploring MongoDB, which offers a more flexible way to handle data. This shift allowed us to work with different types of data, from text to images, and showed us how crucial it is to choose the right database based on what the data is like and what you need to do with it. Understanding database architecture and how to manipulate data helped us grasp the complexities of big data.

Later, we explored Neo4J, which introduced us to graph databases. This helped us visualize and understand complex relationships within data. Learning the Cypher query language was key for navigating these networks, a valuable skill for analyzing social media and planning logistics.

A major part of the course was the group project, where my team built a system to classify brain tumors. This project tested our technical skills and our ability to work together on design, documentation, and presentations. By applying what we learned about big data to such an important health issue, we could see the real-world impact of our work. This course has prepared me well for managing and analyzing high-quality data in my future career.

For the group project in the course, my team decided to create a brain tumor classification system capable of identifying "Glioma," "Meningioma," and "Pituitary" tumors.