Entry Requirements
  • A minimum of a Level 6 qualification in a related sector or;
  • Bachelors degree or;
  • A minimum of 3 years’ work experience which demonstrates current and relevant industry knowledge


Course Overview

With the emergence of cloud computing, big data and artificial intelligence, data science has become a key fourth generation profession. The Level 7 Postgraduate Diploma in Data Science has been developed to prepare aspiring Data Scientists, Data Analysts and Artificial Intelligence specialists to take advantage of the growing business and employment opportunities in these fields.

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.

  • Exploratory Data Analysis
  • Statistical Inference
  • Fundamentals of Predictive Modelling Advanced Predictive Modelling
  • Time Series Analysis
  • Unsupervised Multivariate Methods Machine Learning
Learning Outcomes
  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.


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