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
1.
Demonstrate understanding for the basic concepts of big data analytics.
2.
Implement data management tools and techniques appropriately for big data problems.
3.
Set a plan for big data project by implementing all phases of data analytics lifecycle.
4.
Apply modelling and analytical methods for big data related issues.
5.
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
Report