Objectives
The main objective of the Practice Module is for participants to assimilate the knowledge gained through the four component courses and be able to apply them in a holistic manner to solve real-world analytics problems. The practice module consists of two parts i.e. a Practice Project and an Examination.
Intended Audience
This practice module is targeted at the participants who wish to complete the certification process for the GC in Analytics for Business Decisions.
Prerequisites
Participants must have successfully obtained a competent score (or have been exempted from) the four component courses for the Graduate Certificate in Analytics for Business Decisions as listed in the introduction to the Graduate Certificate page.
Components
There are two parts in the Practice Module.
- Practice Project:
Participants will need to undertake minimum one project to gain practical experience and demonstrate their understanding and mastery of the skills taught in the four component courses. The practice project will require each participant to expend an estimated 10-man days of effort. These days are not expected to be contiguous and may stretch over many weeks and months. These projects may be conducted by individual participants or in teams depending on the nature of the project requirements. Participants are expected to understand business requirements for data analytics projects, identify business problem and data sources, build business intelligence and analytical models, and apply key data analytic techniques to find out the insights and solutions.
Examination:Each participant is required to sit for an examination on a stipulated date and time.
- The overall grade for the participant will be based on the Practice Project and Examination.
Typical examples of projects to be undertaken
- Problem description:
A regional logistics company currently relies on manual processes to compile weekly performance reports, which include structured key performance indicators (KPIs) such as delivery times, fleet utilization, and customer satisfaction scores, as well as qualitative summaries from operations teams. This manual reporting is time-consuming, prone to inconsistencies, and delays decision-making. As the company scales, leadership needs a more efficient and intelligent reporting system that not only automates data aggregation and visualization but also provides early warnings and actionable insights.
The company aims to implement a solution that uses Generative AI to interpret unstructured feedback, generate executive summaries, and build interactive dashboards that monitor performance trends in real time. The system should help managers identify emerging issues such as delays or resource bottlenecks before they escalate, enabling proactive decision-making.
- Problem description:
A tech startup is preparing to launch a new product in a competitive market and faces critical decisions around pricing and optimal launch timing. While the team has access to historical sales data from previous launches, competitor pricing information, and customer sentiment from online reviews and social media, these data sources are currently underutilized and fragmented.
The challenge is to integrate these diverse datasets to build a robust decision-support framework. The startup wants to identify potential sales under different pricing scenarios and launch windows and wants to make data-driven decisions that reduce risk and maximize market impact.
- Problem description:
An organization is experiencing rising employee attrition and declining engagement scores, which are beginning to impact productivity, morale, and talent retention. While HR has collected a range of data including structured records such as tenure, performance ratings, and exit interviews, as well as unstructured data from engagement surveys and open-ended feedback, the insights remain fragmented and underutilized.
The organization seeks to identify the key drivers of engagement and retention by analyzing both quantitative and qualitative data. This includes uncovering patterns in employee demographics, job roles, and performance metrics, as well as interpreting sentiment and themes from open-ended feedback using Generative AI. The goal is to surface actionable insights that can inform targeted interventions, such as leadership development, workload balancing, recognition programs, or policy changes.
Deliverables and success criteria for the problem statements:
- Clearly define the business problem and demonstrate how project outcomes lead to actionable business impact.
- Select and apply appropriate Data Science and AI techniques for the identified business problem statement
- Implement DS/AI analytics techniques taught within this certificate
- Communicate findings effectively to non-technical stakeholders through compelling data storytelling
- Interpret analytical results with clarity and demonstrate how the insights from data and models support informed business decisions