OVERVIEW OF THE PROGRAMME
The Diploma is designed to enable learners to gain skills in maths, statistics and programming in R, Python and SQL. The Diploma also provides a sound basis for a progression to Masters Degrees in a number of relevant disciplines. We hope that learners take the opportunity to learn a great deal from this programme that will provide relevant new skills and knowledge. It is envisaged that this programme will encourage both academic and professional development so that learners move forward to realise not just their own potential but also that of organisations across multiple sectors and industries.
RATIONALE FOR THE DIPLOMA
This qualification is suitable for part-time learners in the workplace but is equally appropriate for full-time learners who can also participate in formal work placements or part-time employment. Learners can progress into or within employment in a wide range of industries and sectors that require the skills and knowledge of data scientists.
AIMS OF THE DIPLOMA
The Qualifi Level 7 Diploma in Data Science aims to give learners the opportunity to:
1. Gain a recognised qualification from an internationally recognised awarding organisation.
2. Learn from a curriculum supported by the most recent content relevant to a contemporary business environment.
3. Develop new skills and knowledge that can be applied immediately and in the field of data science and analytics.
4. Have assessments marked and moderated by respected academic and practitioner professionals with practical experience in data science and analytics.
5. Progress along a pathway to gain a Master degree or beyond.
LEARNING OUTCOMES OF THE DIPLOMA
The overall learning outcomes of the Postgraduate Diploma in Data Science are to:
1. Gain the mathematical and statistical knowledge and understanding required to carry out basic and advanced data analysis.
2. Develop sufficient skill in the R, Python and SQL programming languages to use them to successfully carry out data analysis to an advanced level.
3. Develop a strong understanding of data management, including evaluation, structuring and cleaning of data for analysis.
4. Become familiar with and use the tools and techniques used in data visualisation
5. Develop a comprehensive knowledge of classical data analytics, including statistical inference, predictive modelling, time series analysis and data reduction.
6. Become familiar with and apply common machine learning techniques to business and other problems in order to uncover options and solutions for them.
7. Develop an understanding of essential concepts from contemporary themes in business.
8. Understand, evaluate and apply data science and analytics within business and organisational contexts.
ENTRY CRITERIA
The qualification has been designed to be accessible without artificial barriers that restrict access and progression. Entry to the qualifications will be through centre interview and learners will be expected to hold the following:
- A minimum of a Level 6 qualification in a related sector or;
- Bachelor degree or;
- A minimum of 3 years’ work experience which demonstrates current and relevant industry knowledge.
In certain circumstances, individuals with considerable experience but no formal qualifications may be considered, subject to interview and being able to demonstrate their ability to cope with the demands of the programme.
PROGRAMME SUMMARY
Unit Reference | Mandatory Units | Level | TQT | GLH | Credits |
DS01 | Exploratory Data Analysis | 7 | 80 | 50 | 8 |
DS02 | Statistical Inference | 7 | 120 | 70 | 12 |
DS03 | Fundamentals of Predictive Modelling | 7 | 150 | 90 | 15 |
DS04 | Advanced Predictive Modelling | 7 | 150 | 90 | 15 |
DS05 | Time Series Analysis | 7 | 150 | 90 | 15 |
DS06 | Unsupervised Multivariate Methods | 7 | 150 | 90 | 15 |
DS07 | Machine Learning | 7 | 150 | 90 | 15 |
DS08 | Further Topics in Data Science | 7 | 150 | 90 | 15 |
DS09 | Contemporary Themes in Business Strategy | 7 | 100 | 60 | 10 |