KV6015 - Data Analytics

You will learn through lectures, workshops, and independent learning. The lectures will cover theories and concepts that will enable you to tackle a series of guided exercises. You will work on these during workshops and hands-on sessions in Northumbria’s CIS building computer labs, which are fully equipped with the latest industry-standard software.

Outside class contact time, you are expected to read research papers, conduct research in relevant areas and if possible pursue research publications based on your implemented creative research work. Guidance on accessing online or library resources will be given by the module team. In addition, outside class time, student volunteers may also have opportunities to be involved in the evaluation of e.g., PhD projects as testing subjects (i.e., you will be guided by the module team on how to work with any PhD student and their project to further your data analytical skills). There may also be an opportunity to apply your data analysis skills to a live academic research project led by a member of the teaching team.

Inquiry-based learning is used throughout the module.

The module team will guide and support you in the lecture and workshop practical sessions. During the lectures, you will be required to conduct interactive activities based on the lecturers’ guidance. The module team will prepare diverse examples of real-life applications to support you in learning approaches to data analytics. The module team will work closely with you in the practical sessions to conduct discussions and provide further detailed guidance on the subjects covered.

You can also request appointments with the module teaching team outside of scheduled class time to ask questions and seek advice.

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

Knowledge & Understanding:
ML01. Demonstrate critical understanding of foundations and principles of data analytics
ML02. Demonstrate deep knowledge of fundamental statistical methods, techniques
and applications in data analytics.

Intellectual / Professional skills & abilities:
ML03. Critically assess, select, and apply data collection and cleaning, visualization, statistical inference, predictive modelling, and decision making for statistical analysis in the context of applied data analysis problems.

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
ML04 – Critically evaluate the choice of data science techniques and tools for particular
scenarios.

Summative assessment
The module will be assessed by two assignment components, a data analysis process (e.g., code) described and presented with a 4000-word report (100%). All work to be undertaken individually.

For the assignment, you will practise the knowledge and skills learnt from this module by investigating and analysing a known data set from a list of given topics. This will assess MLOs KU1, KU2, KU3, IPSA34 and 5.

The assignment includes a report on research into e.g., the data and analytics used. The report will mainly assess your ability to present research work and your knowledge of relevant techniques and topics. It will assess MLOs KU1, KU2, KU3, KU4, PVA1, PVA5.

You will be provided with written, electronic, feedback for each of the summative assignments.

Formative assessment and feedback
The exercises in the practical sessions provide opportunities for formative assessment, helping you and your tutors to assess your progress. You will receive guidance and ongoing feedback on your work and progress verbally in lab sessions.

You will also have the opportunity to discuss your progress and the needs of the summative assessment informally in the practical class sessions.

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This module introduces you to the theory, principles and practice behind data analytics. During the semester long delivery of this module you will be exposed to a range of topics, including but not limited to: statistics, business intelligence systems, research methods, data mining techniques, and clinical informatics. Theory is followed by practical workshops where you will be introduced to different software kits as well as guided exercises to ensure you become comfortable/familiar with adopting data analytics processes in your own work. You will apply the acquired theoretical and practical knowledge in building a substantial individual analytical project to ensure your work is addressing real world problems. You will have weekly contact with the academic team to ensure some continuous assessment with feedback during workshop tasks. The structure of the module will encourage you have good communication and pragmatic skills to aid your personal and professional development and employability factor.