MSc Applied Data Analytics - Student Information

Special features

• Study towards career-enhancing qualifications of PgCert, PgDip or a full MSc degree. You can take individual modules for personal or professional development too.
• Three optional themes to choose modules from: Business, Computational and Environmental.
• Take away concrete ideas, techniques, skills and templates you can use as launch pads for you and others in your current and future workplace - get things done & make a difference.

Intro to R and Data Visualisation

Analysing data often begins with visualising it, observing the relationships that appear and discovering anything unexpected. This module will teach students how to use the open source statistical software R to visualise data including cleaning, preparing and summarising the data sets. Alongside the data visualisations, reproducible reporting mechanisms will be introduced, using a document format that mixes code and text. The skills learned in the module are used in many fields and are applicable for use with many datasets, regardless of size.

R is a widely used tool throughout various industries and businesses. It has a large open-source community who contribute to the development of new tools and techniques, along with providing community support. Other industry tools, including an integrated development environment and version control, will also be used throughout the module.

Intro to R and Data Visualisation Reading list

Fundamental Statistics

This module introduces the statistical theories and practices that underpin the statistical techniques used in data science. The focus of the module is on practical computational exercises and case studies, with theory introduced through exercises, simulations and visualisations. Publicly available datasets will be used throughout the module with students using web technologies to work collaboratively in their analyses.

Fundamental Statistics Reading list

Ethics and Data Science

Ethics is of undeniable importance to the modern data-driven business and is encompassed by all elements of data science. By using case studies, including those from business, and up to date research, students will have the opportunity to discuss ethical issues as they relate to data science and different industries/applications. All aspects of a data pipeline will be discussed from gathering data and ownership through to data analysis and application of results, with areas where unconscious bias can have an effect investigated. Throughout the module, the virtual learning environment and web technologies (either face to face or instant messaging) will be used to facilitate group discussion. The module will also allow students to refine skills in critically analysing and evaluating literature and prepare them for written work in future modules.

Ethics and Data Science Reading list

Applications of Statistics

In the prerequisites to this module, students will have learnt the fundamental statistical theories and analytical techniques along with gaining a thorough understanding of the statistical software R. The Applications of Statistics module will extend these concepts and technical skills whilst examining case studies of 4 groups of additional statistical and analytical skills. These methods are currently used by data scientists across a wide range of industries and businesses, solving problems on a myriad of different datasets.

Two families of statistical models (generalized linear and generalized linear mixed models) will be critically evaluated, applications of regression modelling applied to time-series data explored, whilst Natural Language Processing allows very different types of data to investigated.

Applications of Statistics Reading list

Dissertation

The final core module is the dissertation.

This module aims to provide students with an opportunity to undertake a sustained, rigorous and independent investigation or project on one area or topic within Data Analytics under academic supervision.

The complete project must consist of original work but can take different forms. It should be informed by the theoretical and practical knowledge and expertise, which the participant will have developed through the taught modules of the programme and/or previous experience in this field.

Some possible examples are:

• A specific research or business question(s) that can be investigated through combining and analysing existing datasets.
• A specific research or business question(s) that can be investigated through gathering, combing and analysing new datasets.
• Solving an existing research or business questions through construction of a data analytics pipeline, dashboard or new infrastructure.

The final written dissertation should not only present and interpret the research findings but also critically evaluate the research design and methodology employed and identify the outcomes of the research in terms of actual or planned developments and changes.

Business

The business modules are timetabled as follows.

Effective Communication

Blanchard undertook research including 1400 people in leadership positions between 2003 and 2006 with the result that the ability to communicate appropriately and effectively was considered to be an essential component of effective leadership. In the follow-up study, 43% of respondents identified communication skills as the most critical skill set, while 41% identified the inappropriate use of communication as the number one mistake leaders make. There are more means of communication in the world than there ever have been. Communication is faster than ever and there is increasing volume of communication. However lack of effective communication is often cited as a cause of the perception of poor leadership, from health to police to SMEs to multinationals. The range of communication tools and the speed of communication makes the skill of communicating effectively in today’s business environment, very challenging. Combine this complexity with the challenges of communicating across functional disciplines and across industries and geographies. The skill of effective communication is one of ongoing challenge and development.

Effective Communication Reading list

Information Decision Making

This module aims to explore and develop students’ critical understanding of information and to explore how effective information management can improve organisational decision making. The module will have a particular focus on the understanding and management of quality information, to aid decision making and will, alongside these areas, provide skills to leaders/managers to manage the flow of quality well targeted information to help ensure that they can operate more effectively in the complex environments contemporary leaders/managers face.

Information Decision Making Reading list

Computational

The computational theme modules are timetabled as

Applications of GIS

This module aims to introduce students to the practical use of one freely available GIS software, namely QGIS. It will comprehensively cover the different levels of technical competency to the level of problem-solving including geoprocessing, one of the most sought after skills in a wide range of jobs (particularly planning and resource management). It will also link these skills to practical applications of how GIS is used in the workplace.

Applications of GIS Reading list

Applications of Machine Learning in Python

The Applications of Machine Learning in Python module, delves into the principles and techniques of machine learning, focusing both the theoretical foundations and practical applications. Utilising both supervised and unsupervised learning, and using popular machine learning libraries, students will work on real-world datasets.

Alongside a variety of algorithms, students will critically evaluate the ethical considerations of using machine learning techniques.

This module aims to equip students with the skills to develop and apply machine learning models, preparing them for industry roles in this rapidly evolving field.

Environmental

The environmental theme modules are timetabled as

Applications of GIS

This module aims to introduce students to the practical use of one freely available GIS software, namely QGIS. It will comprehensively cover the different levels of technical competency to the level of problem-solving including geoprocessing, one of the most sought after skills in a wide range of jobs (particularly planning and resource management). It will also link these skills to practical applications of how GIS is used in the workplace.

Applications of GIS Reading list

Advanced GIS & Remote Sensing

Students will have already become technically competent in QGIS, and this module will enable them to achieve technical competence in Remote Sensing skills using that software.

By completing this module, students will, therefore, be able to claim advanced technical GIS and programming skills, both of which are highly sought after in the current job market.

Advanced GIS & Remote Sensing Reading list

Student Support

No issue is too minor to speak to student support about - and doing so early can help to avoid small issues becoming major issues. At UHI Inverness we have an experienced student support team to assist all of our students. More information on the support available can be found on the UHI Inverness student support team page.

Each UHI student is assigned a named Personal Academic Tutor (PAT) whose responsibility it is to provide you with academic support throughout the duration of your studies. Your PAT will be in contact with you at least once each semester to review your academic progress; this is in addition to an introductory meeting at the start of each academic year. If you are unsure who your PAT is, please contact your programme leader.

Teaching

The part-time MSc Applied Data Analytics will be delivered entirely online using blended learning. That is, modules may contain some synchronous teaching – using video technologies to connect you in real time with other students and the lecturer. In conjunction with this, you will use the virtual learning environment (VLE) Brightspace to work through material, both before and after the video classes. Where a module contains video classes, these will take place at a time as mutually convenient as possible but are expected to occur between 5pm and 9pm GMT/BST Monday to Friday. Where possible these classes will be recorded.

Each module will be divided into sections with activities and content presented in manageable chunks. The sections of the module – and the module overall – are progressive, structure and flexibility are therefore available through the VLE. You can work through the module in your own way – either at the speed suggested or by working ahead.

Collaborative activities including discussion boards and code reviews will be used and will promote professional collaborations through the entirety of the programme.

Each module (except the dissertation) comprises 200 hours of study and so you should be prepared for between 10 and 12 hours study per module, per week.

Assessment

The MSc Applied Data Analytics uses a wide range of coursework based assessment methods including reports, presentations, technical programming tasks and building interactive data visualisations - we do not use written exams on the programme.

ICT

When studying on the MSc Applied Data Analytics are expected to have access to a computer that they have complete control over. Generally, the UHI minimum system requirements can be followed.

We use a wide range of open source software on the programme but this software is compatible with Windows, Mac and Linux operating systems.

If you have any questions about this or the software used on the MSc, please contact the programme team via data.science.ic@uhi.ac.uk.

Destinations

After completing the MSc Applied Data Analytics you will be able to choose your route depending in part on any previous qualifications and optional modules taken. Smaller businesses may take the opportunity to employ you in a dual skill role, utilising both your business and analytical skills, whilst in larger companies you could be employed in a purely data analytics role.