Data Analytics for Business - MSc
Master
In Nottingham
Description
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Type
Master
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Location
Nottingham
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Duration
2 Years
Learn new analytical tools and skills needed to maximise the big data revolution, developed specifically for you to fulfil the needs of employers.
This is a part-time industry-led data analytics Masters with modules from Nottingham Trent University's School of Science and Technology and Nottingham Business School.
Facilities
Location
Start date
Start date
About this course
You will gain a more comprehensive understanding of data science and the processes involved in the entire data life-cycle, giving you the flexibility to gain the skills and confidence to help your organisation harness the power of big data.
We have tailored this Data Analytics postgraduate degree to provide you with the knowledge and experience needed to take the next step in your career.
You may already work in one of these fields or are aspiring to further your career in:
ecommerce
professional, scientific and technical services
finance and insurance
engineering and manufacturing
science and research
healthcare
telecommunications
retail
media, communication and entertainment
transportation and automotive
A 2.2 honours degree or above in a related subject
Applicants should be in employment or be able to gain access to an organisation upon which to base assignments
Reviews
Subjects
- Marketing
- Planning
- Technology
- Big Data
- CRISP-DM
- Components of Machine
- Data Analyst
- Systems analyst
- Project Identification
- Systems development
- Management
- Project planning
- Strategies
Course programme
Big Data & its Infrastructure (20 cp)
The module content is designed to develop and structure your understanding according to the stages an organisation moves through in order to develop and manage the infrastructure necessary to derive business value from large volumes of data. The module is organised as follows:
- The Big Picture of Big Data
- Overview of Database Technology for Big Data
- Technology and Infrastructure for Managing Big Data
- Deriving Business value
- Social, Legal and ethical issues
Statistical Approaches to Data Analysis (20 cp)
The aim of this module is to provide students with an introduction to the statistical principles and statistical methods required for the analysis of large datasets. The module uses a statistical computing tool such as R, Minitab or SPSS for initial exploration and visualisation of data and for predictive modelling. This module includes hands-on labs to familiarise students with the concepts taught.
Types of data:
- Statistical inference: population and sample
- Descriptive statistics
- Exploration and visualisation of data
- Probability and normal distribution
- Principles of hypothesis testing
- One sample t-test, two-sample t-test, paired t-test
- Nonparametric tests
- Correlation and regression
- Chi-square tests.
Delivering Value (20 cp)
- Developing the marketing infrastructure and the operational aspects of marketing to create value
- Managing new and existing brands, products and services in a range of markets
- Managing channel and stakeholder relationships
- Understanding what it is to deliver value from the customer perspective
- Trade-off choices and why operations and marketing need to be aligned
- Managing and reducing variability in a delivery system
- Principles, theories and concepts that support decision making
- Continual improvement – culture, practice and tools
Effective Change Management (20 cp)
This module will consider:
- Difficulties of Driving Change Through Organisations
- Common Models and Theories of Change Management Practices
- Project Management and Operational Considerations
- The Role of Leadership and Personal Effectiveness
Practical Machine Learning Methods for Data Mining (20 cp)
The module is designed to develop you as a Data Analyst who is able to competently work with large volumes of data to extract, interpret and present meaningful information. Subsequently, the content is organised as follows:
- Reminder: CRISP-DM
- The Basic Components of Machine Learning Models
- Machine Learning Methods for Classification and Prediction
- Machine Learning Methods for Clustering
Project Conceptualisation and Planning (20 cp)
The module will consist of the following indicative content:
- Skills of a Systems Analyst
- Project Identification and Selection
- Systems Development Lifecycle and Methodologies
- Project Planning and Management
- Business Process Improvement, Automation and Redesign
- Requirements Elicitation
- Requirements Modelling
Work-based Project (60 cp)
You will apply your new skills and knowledge to a three to six month project that is directly relevant to your employer's needs. Your learning will be largely independent, under the guidance of your academic mentor. They will help you to identify and access suitable learning resources. It is likely that you will engage with materials relating to:
- Research methodology, strategies, methods & techniques
- Research and/or practitioner skills
- Leading edge theory and practice in big data systems (here you will be able to use prior learning from earlier modules as a starting point to direct your efforts)
- Project management and modelling solutions (you can draw here upon your experiences in the core module ‘Project Conceptualisation and Planning’).
At the end of this module, you will produce a Professional Portfolio - compiled over the entire course to evidence your skills, experience and achievements, this portfolio will be summatively assessed. The portfolio will also be an opportunity to evidence how you have considered appropriate professional, legal and ethical issues associated with big data systems.
Data Analytics for Business - MSc