About This Course

This course, “Big Data Analytics for Industrial Technology Management”, is designed to equip students and professionals with the theoretical foundations and practical competencies required to manage and analyze large-scale data in industrial environments. The course covers the end-to-end pipeline of big data, from data acquisition using IoT sensors to real-time analytics, decision support, and strategic implementation within smart factories and industrial plants.

Students will explore distributed storage and processing frameworks such as Hadoop, Spark, Cassandra, and Flink, and learn how to design robust data architectures for industrial applications. Key concepts include batch and stream processing, NoSQL data modeling, predictive maintenance, energy optimization, and supply chain analytics. The course emphasizes data quality, scalability, fault tolerance, and system integration using real-world industrial use cases.

Hands-on labs are delivered through Dockerized environments, including JupyterLab for Python-based analysis, Kafka for data streaming, and Power BI or Grafana for data visualization. Students will gain exposure to industry-grade tools and platforms through guided projects that simulate production-scale deployments. Whether you're an engineer, analyst, or system architect, this course will help you bridge data science with industrial operations.