IDTS4201 - Data Analytics
Overview
This course introduces fundamental and applied techniques in data analytics, emphasizing the process of transforming raw data into actionable insights. The course covers the data analytics lifecycle, data collection, data cleaning, exploratory data analysis, descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, visualization, dashboarding, and business insight communication.
Students will develop practical skills in using modern analytical tools such as Python, Excel, SQL, Google Colab, Power BI, and Tableau. They will learn to prepare datasets, apply statistical analysis, develop simple predictive models, interpret analytical outputs, and communicate findings through visual reports and interactive dashboards.
The curriculum also integrates ethical, privacy, and governance considerations in data analytics, including responsible data use, bias awareness, transparent reporting, and the protection of data subjects in organizational decision-making contexts.
Through real-world case studies, laboratory practice, and project-based learning, students will gain the ability to design, implement, evaluate, and present data analytics solutions for business, operational, and technology-related problems.
Pre-requisites:
Learning Hours Composition:
- 42 hours face to face, consisting of 14 sessions of lecturing, tutorial or workshop.
- 28 hours of Structured Learning and Guided Group Discussions.
- 65 hours of Independent, Project-Based Learning.
Managing Faculty:
Credit Point:
3 Credit(s)
Learning Hours:
45 hours x 3 CPs = 135 hours
