This course introduces the basic goals and techniques in data science and analytics process with some theoretical foundations which include useful statistical and machine learning concepts so that the process can transform hypotheses and data into actionable predictions. The course provides basic principles on important steps of the process which includes data collecting, curating, analysing, building predictive models and reporting and presenting results to audiences of all levels. R programming language and statistical analysis techniques are introduced based on examples such as from marketing, business intelligence and decision support.

At the end of this course the students will be able to:

CLO 1 – Organize effectively all the necessary steps in any data science and analytics project. (PO1, C3)

CLO 2 – Adapt the R programming language and useful statistical and machine learning techniques in data science and analytics projects. (PO2, P6)

CLO 3 – Practice all the skills needed by the data scientist, which include acquiring the data, managing the data, choosing the modelling technique, and writing the code to solve data science problems. (PO3, C5)

CLO 4 – Demonstrate the ability to communicate and present the data science results effectively. (PO4, A3)