Master of Science in Data Analytics

Turning mass information into
forward-thinking data

Master of Science in Data Analytics

Overview

Every second of everyday, 1.7MB of data is created by every person. In the last 2 years alone, the world created 90% of today’s data. Data analytics is the science of analyzing raw data in order to make conclusions about that information. 

On this 1-year part-time Data Analytics MSc, you’ll be introduced to subjects including data mining, statistical modelling, business intelligence and data visualisation. The course has been developed with direct input from industry experts who’ll present you with real-life business cases as part of your work-related learning.

By the end of the MSc degree, you’ll be ready to apply for rewarding roles in the data science and big data industries, as well as the many sectors and organisations that increasingly require data analysts.

msc-data-analytics-lmu
12 Months (Part Time)
Ranked 13th in London for Computer Science & Information Systems*

*The Guardian University League Tables 2021

Developing Future Technologists & Data Experts

Programme Structure

Structured around the work commitments and priorities of working professionals, the 1-year (part time) London Metropolitan MSc Data Analytics includes 6 modules and a project

Syllabus

This module provides an appreciation of data mining concepts, techniques, and process for business Intelligence. It covers data mining techniques for both supervised learning (decision tree, logistic regression and neural network models) and unsupervised learning (cluster and association analyses). It is designed to help equip the students with practical skills in applying data mining techniques in a modern business environment.

The module provides an introduction to relational data modelling and multidimensional data modelling techniques for data analytics. It enables students to acquire skills in advanced SQL and OLAP operations (OLAP cube, rollup, drill-down, slice and dice and pivot). The module is designed to help students with practical skills in preparing data for analysis which usually takes 50%-70% of data analytical project time. Big Data analytics platforms will also be introduced.

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 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 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.

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.

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.

Assessment

The course is assessed in a number of ways including written reports, practical and research assignments, demonstrations, presentations, group work and examinations.

Delivery Structure

Each module is delivered over two consecutive full weekends (Sat & Sun, 9am to 5.30pm) per month by qualified academia with extensive years of professional practice experience. You will learn through means of interactive lectures, experiential skill workshops, and group discussions / role plays.

SatSunMon - FriSatSun
9am to
5:30pm
9am to
5:30pm
Revision
Week
9am to
5:30pm
9am to
5:30pm

Career

Our MSc Data Analytics programme will equip you to work in some of the fastest growing sectors of the data science and big data industries. A wide range of career opportunities will be open to you

Possible future career options include that of:

  • Data Scientist
  • Data Analyst
  • Digital Analyst
  • Big Data Consultant
  • Statistical Analyst
  • Data Modeller and more.
The Straits Times

Degree Awarded

Upon successful completion of the programme, you will be awarded a Master of Science in Data Analytics degree by London Metropolitan University.

The Degree awarded will be the same as that awarded on-campus.

Admission Criteria

Participants are expected to contribute to the class learning experiences in a cohort and peer learning environment. You will be assessed based on experience, aptitude, potential and skills.

A 2:2 UK degree (or equivalent) in any discipline that involves an element of data analysis (Applicants with relevant professional experience will also be considered)

Mature candidates above 30 years with a minimum of 8 years working experience may be considered on a case to case basis.

Applicants are required to demonstrate the ability to study in English; IELTS 6.0 overall with 5.5 in each component or equivalent.

Commencement Date

Next intake: September 2021

Fees

Please download the brochure or enquire with us at 9327 5719 for more information.

Enquiries

If you have any questions, please do not hesitate to contact Amanda at 9327 5719.

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