Data Analytics MSc

London Metropolitan University

This module explores fundamental concepts for analysing and visualising data. The module covers descriptive statistics for exploratory data analysis, correlation analysis and linear regression model. Graph and text data analysing techniques for web and big data and reporting the results and presenting the data with visualisation techniques are also discussed. A substantial practical element is integrated into the module to enable students to apply data analysis and visualisation techniques for real world data analytical problems.

This module provides an appreciation of data mining and machine learning fundamental concepts, algorithms, and process. It covers machine learning algorithms and data mining techniques for data analysis, pattern mining, clustering, classification and regression. It equips the students with practical skills in applying data mining and machine learning techniques in real-world analytics problems.

The module aims to strengthen students? skills in data technologies ranging from database and data warehousing to Big Data. First, it will provide students with good understanding of database concepts and database management systems in reference to modern enterprise-level database development. Once gaining good skills in database development, students will be able to study and gain an in-depth understanding of data warehousing which include concepts and analytical foundations as well as data warehousing development. Through intensive hands-on sessions, the students will be able to get familiar with related technological trends and development in the field. the module will leverage a portfolio of SQL server tools that include SQL Server DBMS, SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS) and SQL Server Analysis Service (SSAS) to provide hands-on experience in implementing a reporting solution through a combination of assignments and lab exercises.

The module provides students with the experience of planning and bringing to fruition a major piece of individual work. Also, the module aims to encourage and reward individual inventiveness and application of effort through working on research or company/local government projects. The project is an exercise that may take a variety of forms depending on the nature of the project and the subject area. Particular students will be encouraged to carry out their projects for local companies or government departments.

This module develops students? foundation of programming principles through the introduction of application programming for data analytics. The module covers common programming data structures, flow controls, data input and output, and error handling. In particular, the module places emphasis on data manipulation and presentation for data analysis. A substantial practical element is integrated into the module to enable students to use a programming language (e.g. Python) to prepare data for analysis and develop data analytical applications.

This module will introduce students to modern statistical modelling techniques and how those techniques can be used for prediction and forecasting. Throughout the statistical environment and software R will be used in conjunction with relevant statistical libraries. The module will, introduce modern regression techniques (including smoothing), discuss different model selection techniques (including the classical statistical hypothesis) and how those techniques can be used for prediction purpose.