Big Data Analysis module (AC51011)

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 have 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 such as:

• customer interaction
• predicting market trends and future needs
• optimising manufacturing processes

The results from big data analysis are highly valuable to organisations. It helps them to:

• understand their user base better
• improve internal processes
• 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)
  • lambda architecture
• learn how to effectively visualise data
• examine different methods for storage of big data such as:
  • distributed file systems
  • distributed databases
  • NoSQL databases
• study the concepts of data warehousing
• explore batch and stream processing methods of big data using open-source tools
• learn about machine learning and its applications in big data analysis

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

• 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 assignment (20%)
• Big Data processing assignment (20%)
• formal 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.