Advanced Big Data Analysis module (CS42003)

Learn how to process, analyse, and store big data.

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Big data refers to very large and complex sets of data. They usually cannot be easily processed or analysed using traditional data processing methods.

In big data analysis, we use specialised techniques and tools to extract insights and trends from these large datasets.

Big data and analysis has become a vital component of business intelligence across industries. Organisations will collect data from various sources, and then process and analyse them.

This allows them to detect patterns in customer interaction, predict market trends and future needs, and optimise manufacturing processes.

The results from big data analysis are highly valuable to organisations as it helps them to understand their user base better, improve internal processes, and foster innovation.

What you will learn

In this module, you will:

  • study the concepts of big data and big data analysis
  • explore different methodologies for big data analysis, such as the Cross Industry Standard Process for Data Mining (CRISP-DM) and lambda architecture
  • learn how to effectively visualise data
  • examine different methods for storage of big data, such as distributed file systems, distributed databases, and NoSQL databases
  • study the concepts of data warehousing
  • explore batch and stream processing methods of big data using open source tools
  • learn how to use concurrent programming for big data applications using the Scala programming language
  • look at machine learning and its applications in big data analysis

By the end of this module, you will be able to:

  • compare and contrast the use of different programming languages to manipulate big data
  • define and explain key algorithms for big data analysis
  • understand and apply industry standard analysis techniques for big data to solve practical problems
  • discuss the issues involved in storing big data sets
  • discuss and appraise the implementation and use of big data in a business context

Assignments / assessment

  • database presentation (20%)
  • big data processing application development (20%)
  • written exam (60%)

Teaching methods / timetable

You will learn by taking a hands-on approach. This will involve taking part in tutorials and practical sessions.

Learning material is provided through videos, review notes, examples, and tutorial questions.


This module is available on following courses: