MSc Data Science and Analytics: What this Masters Entails

By Anne Sexton - Last update

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MSc in Data Analytics teaches you how to implement data and analytics. This is very important for a number of industries. This is because good data analysis can give a business a huge advantage over rivals. In addition, it allows a company to glean insights into consumer behaviour, or predict what customers may want.

What does the course entail?

A successful graduate of MSc Data Science and Analytics knows how to collect, handle and analyse as well as store enormous and intricate sets of data. It combines skills from Computer Science, Informatics and Mathematics. You will learn about informatics, programming, problem-solving, modelling and machine learning skills. The course will have lectures as well as projects implementing problem-solving skills.

Particular skills attained in this course include:

  1. The ability to empower organisations in competitiveness, efficiency and effectiveness by application of data analytics.
  2. Practical experience in management of data in environments and frameworks that require advanced data analytics.
  3. The development of data warehouses by use of data from separate origins and their manipulation using visualisation of data to draw relevant conclusions.
  4. Skills in data preprocessing and data mining
  5. An understanding of the issues in integration and deployment of data management systems and a practical approach to solutions.

MSc Data Science and Analytics is all about asking questions, discovery and learning. It requires the ability to think creatively as well as the inventiveness to solve new complex problems. Data analysis goes beyond making relevant discoveries – it also seeks to uncover of the meaning and the truth data holds.


Data analytics courses teach students to use raw data in creating predictive algorithms. Analytics is the general term that highlight skills in business, math and technology.

Machine Learning

This involves two parts in data modelling. Firstly, deciphering of data patterns and next, making algorithmic predictions. In data pattern discovery, algorithms detect why and how data natural groupings exist. In predictions making, tagged data is used to manufacture models that are predictive.

Data Mining

Making sense of raw data can be difficult. This is because data can be messy and unstructured. This is often because data may come from different sources, by different means and for different reasons. Data mining polishes this raw data using cohesive means. As a result, the data becomes understandable and analyzable. Data mining heavily relies on an analyst ability to identify patterns and transform and merge it into comprehensible forms.


Programming is important in MSc Data Science and Analytics. Therefore, data scientists need skills to effectively deal with large sets of data. Student learns to code, create and implement quick prototypes for solutions.

Mathematics Expertise

Data is just a bunch of figures until sense can be made out of it. Mathematics expertise allows you to look at data through a qualitative lens. Correlations, textures and dimensions of data largely rely on a mathematics skill.

Business Mind

You will need a head for business to use your data analyses meaningfully. Data Science and Analytics can give companies a competitive advantage over rival businesses. A business mind understands data that has been analysed and uses it to create competitive advantage.

The career opportunities for graduates with MSc in Data Science and Analytics are vast as well as rewarding. There is also a shortage in data scientists globally. Therefore, graduates in this discipline are highly sought after and well paid.


Anne Sexton

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