Diploma in Big Data Analytics Using Apache Hadoop and Python

Big data is a cornerstone of modern analytics, and this course equips participants with the skills to process and analyze massive datasets using Apache Hadoop and Python. The curriculum starts with an introduction to big data concepts, covering the Hadoop ecosystem, including HDFS and MapReduce. Participants learn to set up a Hadoop cluster and perform distributed data processing. Python modules include working with libraries such as PySpark for real-time data processing, Pandas for data manipulation, and Matplotlib for visualization. Advanced topics include integrating Hadoop with machine learning algorithms, optimizing data workflows, and deploying analytics pipelines.

Real-world case studies from industries like finance, healthcare, and e-commerce are included to demonstrate practical applications of big data analytics. Weekly assignments and coding exercises ensure hands-on learning. The final capstone project involves designing and executing a big data solution for a complex problem. By the end of the course, learners will be proficient in managing and analyzing big data, making them valuable assets in data-driven industries.

Basic understanding of programming and data analytics, access to a computer with sufficient processing power, and an internet connection for Hadoop setup.

Data analysts, engineers, and scientists working with large datasets; professionals transitioning to big data roles; and students interested in advanced data analytics.