Advanced Certificate in Business Analytics for Data-Driven Decision Making (with Python)
- Analytics & Tech
- Innovation & Business Improvement
This programme is conducted online.
Please refer to respective modules for dates.
Online Intake
3 days
Weeknights (6.30pm - 10:45pm)
Saturday (9am – 6.15pm)
Who Should Attend
Executives, managers, and working professionals who wish to learn how to link, bridge, and translate Python programming of business analytics to the solving of problems in marketing, HR, finance, and policy.
PREREQUISITES
- 1 year of working experience preferred
- Basic experience in Python programming (does NOT necessarily need to complete any certification, diploma, or degree programs beforehand)
Overview
In today's increasingly data-driven world, one must be capable of reading and analysing data for strategic decision-making.
In this programme, participants will be equipped with the knowledge of applying Python programming skills and data science toolkits to make data-driven decisions in areas of Marketing, HR and Computational Finance. Participants will also learn how to use analytical tools for decision-making, and understand computational programming in the execution of analysis.
This is a sequential programme and is not available on a standalone basis.
Learning Objectives
- Understand when, why, and how to make the best use of Python data science toolkits in response to business and policy questions
- Internalise business analytics workflows with Python programming
- Use computational data analytics with Python programming to improve decisions in business and policy
Topic/Structure
To achieve the Certificate in Business Analytics for Data-Driven Decision Making (with Python), participants will need to complete the following modules offered by SMU Academy in sequential order:
Module 1: Customer Segmentation and User Recommender Systems
Overview
In recent years, popularity in computational data science courses has surged. Yet, there’s still a gap for a course that combines data science with strategic decision-making, enabling participants to harness end-to-end Python data science to make informed, data-driven choices.
This programme goes beyond basic Python programming, empowering participants with the skills to apply Python's powerful data science toolkit to drive strategic decisions in key business functions like Marketing, Human Resources, and Computational Finance. Through hands-on experience, participants will learn how to leverage analytical tools to make informed decisions and understand computational programming in the execution of analysis.
Module 2: Predicting Customer Lifetime Value and User Attrition
Overview
Predicting Customer Lifetime Value (CLV) is an important metric that helps businesses anticipate the future value of a customer by analysing historical data, such as customer lifespan, purchasing behaviour, and revenue generation. Machine learning (ML) is one of the most effective methods for forecasting CLV, as it uses past data to predict future customer behavior. This empowers organisations to fine-tune their marketing strategies, optimise campaigns and allocate resources more effectively.
In this course, participants will use business analytics to address key customer relationship management questions. Through supervised machine learning algorithms, participants will learn how to predict CLV, and how to translate ML performance metrics into business performance metrics. Participants will also explore techniques like Recency, Frequency, and Monetary (RFM) analysis, as well as survival analysis, to enhance their understanding of customer behavior.
Module 3: Causal Inference and Programme Evaluation with A/B Testing & Multi-Armed Bandits
Overview
Strategic decision-makers are constantly grappling with questions about how strategies impact their target audience. To answer these questions effectively, expertise in causal inference is essential. Surprisingly, causal inference is often overlooked in traditional Python courses on computational business analytics.
In this course, participants will learn when business analytics require causal inference, and why it's crucial for. They’ll learn how to execute causal analysis in Python and interpret the results. Additionally, participants will also explore the logic of causation, A/B testing fundamentals, and econometric methods for analysing causality in naturally occurring data.
Module 4: Algorithmic Trading and Computational Finance
Overview
Explore how technology is transforming the finance industry and take a deep dive into algorithmic trading with Python!
Python programming language has become increasingly popular for algorithmic trading, where financial instruments are traded using rules or algorithms with little or no human intervention.
In this course, uncover the mechanics of process-driven investing, learn to extract historical stock data using Python, and formulate based on historical data theory-driven and data-driven trading strategies.
Module 5: Natural Language Processing (NLP), Sentiment Analysis, and Applied Large Language Models (LLMs)
Overview
Real-world business analytics often involves working with vast amounts of text data, whether from customer reviews, employee feedback, leadership speeches, or media articles.
In this course, participants will learn to harness the power of text analytics, with a focus on customer and employee reviews. Participants will explore essential techniques such as text preprocessing, natural language processing (NLP), and text mining into modelling processes. Through hands-on exercises, participants will conduct sentiment analysis and apply unsupervised learning methods like topic modeling to uncover hidden patterns and insights within textual data.
Module 6: Deploying Business Analytics with Interactive Dashboarding for Decision-Making
Overview
Effective business analytics goes beyond static reports - it needs to be deployed for real-world decision making.
This course empowers participants to bring their analytics to life using Python Dash, a user-friendly framework for creating interactive data visualisation interfaces. Learn to build web applications entirely using Python, without needing advanced web development skills.
Through step-by-step guidance, participants will create Dash apps based on their business analytics work from previous modules, transforming complex operational analytics into actionable business intelligence.
This course is also a part of the Industry Graduate Diploma Business Analytics for Data-Driven Decision Making with Python Programming.
Assessment
- Classroom exercises
- Group assignments
CERTIFICATION
Upon completion of all 6 modules within a maximum duration of 3 years, participants will be awarded a digital Certificate in Business Analytics for Data-Driven Decision Making (with Python).
Calculate Programme Fee
Fee Table
| EMPLOYER-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$4,708.80 (After SSG Funding 70%) |
$1,828.80 (After SSG Funding 70% |
$4,708.80 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$1,828.80 (After SSG Funding 70% |
$1,828.80 (After SSG Funding 70% |
$1,828.80 (After SSG Funding 70% |
|
International Participant |
$15,696 (No Funding) |
$15,696 (No Funding) |
$15,696 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Employer Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees are eligible for ETSS funding only if their company's SME status is approved prior to the course commencement date. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Employers who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
Intake Information
| Modules | Next Intake |
|---|---|
| Module 1: Customer Segmentation and User Recommender Systems | 29, 30 Oct & 1 Nov 2025 [Open for Registration] |
| Module 2: Predicting Customer Lifetime Value and User Attrition | 19, 20 & 22 Nov 2025 [Open for Registration] |
| Module 3: Causal Inference and Programme Evaluation with A/B Testing & Multi-Armed Bandits | 10, 11 & 13 Dec 2025 [Open for Registration] |
| Module 4: Algorithmic Trading and Computational Finance | 7, 8 & 10 Jan 2026 [Open for Registration] |
| Module 5: Natural Language Processing (NLP), Sentiment Analysis, and Applied Large Language Models (LLMs) | 28, 29 & 31 Jan 2026 [Open for Registration] |
| Module 6: Deploying Business Analytics with Interactive Dashboarding for Decision-Making | 25, 26 & 28 Feb 2026 [Open for Registration] |
