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Key information
Source: HOTCOURSES, June 2017.

DATA SOURCE : HOTCOURSES

Qualification type

MSc - Master of Science

Subject areas

Data Analysis

Course type

Taught

Course summary
Source: HOTCOURSES, June 2017.

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. This postgraduate course will equip you with the theoretical, technical and practical skills required to become a data analyst. With an expert teaching team, access to specialist software and work on real-life business cases, you’ll be well prepared for a career in data analytics upon graduation. The modules on this course have been developed with the help of industry professionals, some of whom will be present to teach you in specific classes. The course is assessed in a number of ways including written reports, practical and research assignments, demonstrations, presentations, group work and examinations. Upon completion of the course, you’ll be well equipped 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 in the commercial, public and financial sectors, especially in areas requiring big data analysis such as consumer, healthcare, scientific, financial, security intelligence, business and social sciences.

Different course options
Source: HOTCOURSES, June 2017.

Full time | Holloway | 12 months | 25-SEP-17

Study mode

Full time

Duration

12 months

Start date

25-SEP-17

Modules
These are the sub-topics that you will study as part of this course.

Source: HOTCOURSES, June 2017.

CC7183 - Data Analysis and Visualization (20 Credits) - Core

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.

CC7164 - Data Mining for Business Intelligence (20 Credits) - Core

This module provides an appreciation of data mining concepts, techniques, andprocess 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.

CC7181 - Data Modelling and OLAP Techniques for Data Analytics (20 Credits) - Core

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.

Tuition fees
Source: HOTCOURSES, June 2017.

UK fees
Course fees for UK / EU students

For this course (per year)

£8,100

Average for all Postgrad courses (per year)

£5,202

International fees
Course fees for non-UK / EU students

For this course

£12,150

Average for all Postgrad courses (per year)

£12,227

Entry requirements
Source: HOTCOURSES, June 2017.

Students will be required to have a 2:2 UK degree (or equivalent) in any discipline that involves an element of data analysis (students with relevant professional experience will also be considered).

University Information

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