Big Data Analytics

Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture

Practical

Module Offerings

7503BDSA-SEP-PAR

Aims

The course provides students with a detailed knowledge about data management tools and techniques. It covers data acquisition, accessing, storing, transferring, cleaning, visualizing, and data preparation for analysis. The course covers topics of information retrieval, entity-relationship model, relational algebra, indexing, query optimization, normal forms, tuning, security, and data analytics skills in both relational and non-relational environments of big data. The course emphasizes on a project work that involves modern relational DBMS and NoSQL environments.

Learning Outcomes

Demonstrate understanding for the basic concepts of big data analytics.

Implement data management tools and techniques appropriately for big data problems.

Set a plan for big data project by implementing all phases of data analytics lifecycle.

Apply modelling and analytical methods for big data related issues.

Implement a software project using modern data science tools for solving a big data problem.

Module Content

Outline Syllabus:Introduction to Big Data Analytics Basic Concepts of Data Management Tools and Techniques Modelling Concepts (relational algebra, entity-relationship model, normal forms) Advanced Topics (indexing, query optimization, tuning, security) Data Analytics Lifecycle Analytical Methods Big Data Tools

Additional Information:The module contributes to the master’s aim to equip the student with the required abilities and skills to perform data science on real-world applications.

Assessments

Exam

Report

Course Catalogue