Big Data: Storage and Analysis
| Course Code | IOTH-707 |
|---|---|
| Lecture hours per week | 2 |
| Lab hours per week | 2 |
| Course Availability | Open |
| Description | In this course, students will learn about big data, big data analytics in industry verticals and, analytics lifecycle as an end-to-end process. Students will also learn about the concept of big data, its characteristics, and the analytics lifecycle. This includes understanding how to clean, organize, interpret, and visualize data collected from Internet of Things (IoT) systems. These skills are fundamental for extracting meaningful insights and making data-driven decisions. Students will have experiential learning opportunities to develop practical skills with big data tools, particularly within the Hadoop Ecosystem. This ecosystem includes technologies like Hadoop Distributed File System (HDFS) for storage, MapReduce for data processing, and Hive for querying large datasets using SQL-like syntax. Mastery of these tools prepares students to handle large-scale data processing and analysis efficiently. Students will engage in hands-on projects involving live-stream and historical telemetry data generated by Internet of Things (IoT) devices and sensors. With this experiential learning opportunity, students will be able to apply theoretical knowledge in real-world scenarios, preparing them for the complexities and challenges of working with IoT-generated data. Students will learn to communicate and collaborate with team members and stakeholders in a multi-disciplinary environment to explore the opportunities provided by “big data” and the related ethical, social, legal, regulatory and economic requirements of its collection and use. Overall, in this course, students will learn both theoretical knowledge and practical skills necessary to succeed in leveraging big data analytics within Internet of Things (IoT) environments. By learning foundational concepts, practical tools, and ethical considerations, students will have the opportunity to address real-world challenges and opportunities in data-driven industries. |