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 – Identify effectively all the necessary steps in any data science and analytics project. (PO1, C4)
CLO 2 – Adapt the data science programming language and useful statistical and machine learning techniques in data science and analytics projects. (PO2, P6)
CLO 3 – Demonstrate the ability to communicate and present the data science results effectively. (PO4, A3)
CLO 4 – Explain statistical approach for data exploration and predictive modeling to draw conclusions in data science and analytics project. (PO11, C5)
At the end of this course the students will be able to:
CLO 1 – Identify effectively all the necessary steps in any data science and analytics project. (PO1, C4)
CLO 2 – Adapt the data science programming language and useful statistical and machine learning techniques in data science and analytics projects. (PO2, P6)
CLO 3 – Demonstrate the ability to communicate and present the data science results effectively. (PO4, A3)
CLO 4 – Explain statistical approach for data exploration and predictive modeling to draw conclusions in data science and analytics project. (PO11, C5)
- Lecturer: DR. MOHD HALIM BIN MOHD NOOR
- Lecturer: DR. NOOR FARIZAH BINTI IBRAHIM
Skill Level: Beginner