BIG DATA CONCEPTS AND APPLICATIONS

Course Description

This course covers big data, which becomes increasingly available with digitalization of many aspects of human life and with increasingly growth of social media platforms. The course presents a broad understanding of big data and different approaches of big data manipulation. Students will explore how terabytes and petabytes of storage space can be occupied by big data. In this course, the Hadoop software, the Hadoop Distributed File System (HDFS) and the MapReduce technique as well as supervised and unsupervised machine-learning approaches are introduced and artificial neural networks are thoroughly investigated. By completion of the course, students are expected to be able to harvest big data sets from the internet and process by using different methods and tools.

BIG DATA CONCEPTS AND APPLICATIONS

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Reference Books & Course Materials

1. 01 Kamal, R., & Saxena, P. (2019). Big Data Analytics: Introduction to Hadoop, Spark, and Machine Learning (1st ed.). McGraw-Hill Education.

Learning Outcomes

1. L01 Explain the fundamental concepts, characteristics, and challenges of big data in real-world contexts. SOLO 4
2. L02 Describe the architecture and functions of big data ecosystem components, including Hadoop, HDFS, HBase, MapReduce, and YARN. SOLO 3
3. L03 Apply distributed computing methods to store, manage, and process large-scale structured and unstructured datasets. SOLO 3.5
4. L04 Implement data processing and analytics tasks using Spark and related big data tools. SOLO 4
5. L05 Analyze big data problems and select appropriate machine learning approaches, including classification, regression, and unsupervised learning techniques. SOLO 3.5
6. L06 Evaluate the suitability and scalability of different big data technologies for business and real-life applications. SOLO 5
7. L07 Visualize analytical results effectively for technical and non-technical audiences. SOLO 5
8. L08 Develop a data-driven solution by harvesting, processing, and interpreting large datasets while considering ethical, privacy, security, and data ownership issues. SOLO 5

Program Outcomes

1. P01 Be able to apply knowledge of programming
2. P02 Be able to design software systems of varying complexity
3. P03 Be able to identify, categorize, and develop solutions for computer orientated challenges.
4. P04 Be able to demonstrate autonomy and responsibility in managing computer programming projects
5. P05 Be able to follow the state of the arts concepts in computer technology
6. P06 Be able to design, implement, and evaluate a computational system to meet desired needs within realistic constraints.
7. P07 Be able to use appropriate techniques, skills, and tools necessary for computing practice.
8. P08 Be able to apply appropriate technologies and techniques for the collection and analysis of organizational and environmental data to facilitate evidence-based decision making
9. P09 Be able to apply effective communication skills consistent with the professional environment -
10. P10 Be able to apply effective collaboration skills in teamwork consistent with the professional environment -

Po-Lo Matrix