AID304 Big Data Analytics

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
AID304 Big Data Analytics 3 2 6
Prerequisite None It is a prerequisite to


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Course Objectives • Provide an overview of key platforms like Hadoop, Spark, and other relevant tools.
• Discuss various methods of storing data and explain the processes of uploading, distributing, and processing data.
• Explore diverse approaches for implementing analytics algorithms on different platforms.
• Delve into the challenges related to visualization and mobile integration in the context of Big Data Analytics.
Textbook Raj Kamal and Preeti Saxena, “Big Data Analytics Introduction to Hadoop, Spark, and Machine Learning”, McGraw Hill Education, 2018 ISBN: 9789353164966, 9353164966
Additional Literature
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Acquire knowledge in the field of analyzing Big Data.
  2. Obtain understanding regarding the suitable tools, algorithms, and platforms to utilize for different real-world use cases.
  3. Gain practical experience in addressing Analytics, Mobile, Social, and Security challenges associated with Big Data..
Teaching Methods The course will commence with a one-hour session dedicated to theoretical concepts and providing a comprehensive understanding of the topic's background. Subsequently, we will transition to hands-on programming and practical exercises.
Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
Week 1 Introduction of Big Data Analytics
Week 2 Big Data Platforms
Week 3 Big Data Platforms
Week 4 Big Data Platforms
Week 5 Big Data Analytics Algorithms
Week 6 Big Data Analytics Algorithms
Week 7 Big Data Analytics Algorithms
Week 8 Real-Time Stream Analysis
Week 9 Streaming and Linked Big Data Analysis
Week 10 Big Data Visualization
Week 11 Big Data Visualization
Week 12 Data Visualization and Graph Database
Week 13 Big Data Analytics Applications
Week 14 Big Data Analytics Applications
Week 15
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 30
Semester Evaluation Components
Midterm 1 25
Quizzes 3 15
Term project and presentation 1 15
Lab assignments 7 15
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture hours 3 14 42 Assignments 3 7 21
Active labs 2 14 28 Home study 1 14 14
In-term exam study 10 1 10 Final exam study 11 1 11
Term project/presentation 2 12 24
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 01/09/2023
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