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)

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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 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix