NUS Module Review: DBA3702 Descriptive Analytics with R

Module: DBA3702 Descriptive Analytics with R

Semester taken: SEM1 AY2020/2021

Lecturer / Tutor: Liu Qizhang

Module Synopsis (taken from

In the era of big data, competitive advantage for enterprises is derived from data analytics and timely sharing of insights derived from data. The ability to understand data, derive valuable insights from data, and thus make objective managerial decisions has become an essential skill that graduates must master in order to excel in their career. This course introduces the basics of R, a powerful analytics environment, to organize, visualize, and analyse data, and uses case studies to teach students on how to analyse and summarise data and present findings in a structured, meaningful, and convincing way.

Main Learning Objectives

  • Understand and apply data cleaning, wrangling, analysis and visualisation (using R)

Course Deliverables / Graded Components

  • Group Project: 30%
  • Test 1: 25%
  • Test 2: 25%
  • Class participation: 20%

Personal Review

I took this module as it was a compulsory module for the Business Analytics (BA) specialisation.

The module mainly covered techniques for exploratory data analysis (data cleaning, wrangling, analysis and visualisation) using R, which is a programming language primarily designed for statistics. It was quite a hands-on module centred on application of techniques learnt.

Regarding lesson format, class was conducted in flipped classroom style. Every week, students were required to watch some videos covering key concepts and techniques. During class, Prof Liu had many discussion questions for students to apply what they learnt and share their work in class. When it came to choosing students for class participation, he had his own system (with a “raise hand” function) to ensure that everyone had a fair chance of being selected and I appreciate that very much! Other than that, students had to submit an assignment almost every week, which served as a good way for us to retain R techniques learnt. Towards the later half of the semester, when all assignments had been submitted, students would then shift their focus to working on the group project.

The group project, which had the highest weightage, required students to solve a current business problem of their choice using the data skills learnt. The main deliverables were a R Shiny application, as well as a final report. Main tools used were RStudio and Sourcetree for development and version control respectively. I would strongly encourage you to take the project seriously because it allows you to practise techniques learnt and you can even showcase your final product to future employers!

Ending Note

If you are interested in being a data analyst or simply want to learn how to utilise R, this module would build a good foundation for you, so I highly recommend it!

Do note that this module is a technical module, which is not common in BBA curriculum. Hence, the workload may be higher than average, as you would spend a significant amount of time weekly doing R programming.

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Thank you for reading!