OVERVIEW
About This Programme
The Professional Certificate in Data Science & AI is designed to equip learners with the skills required to thrive in today’s data-driven economy. As organisations increasingly rely on analytics, machine learning, and artificial intelligence to drive strategic decisions and operational efficiency, this programme provides a strong foundation in Data Science, AI, and ML, enabling participants to transform complex data into meaningful, business-ready insights.
Delivered in a blended weekend format over 6.5 months, the programme combines live masterclasses, an intensive foundation bootcamp, hands-on labs, experiential learning, and a mentored capstone project. Learners gain practical exposure to industry-relevant tools such as Python, SQL, Power BI, GitHub, Azure, IBM Cloud, and Scikit-learn, ensuring real-world application of concepts. Upon completion, participants will be capable of designing, implementing, and optimising AI-powered solutions that enhance decision-making, automate processes, and strengthen organisational competitiveness, while benefiting from structured career guidance and mentorship support.
Practical, Hands-On Training – Learn through bootcamps, live sessions, and a mentored capstone using industry tools.
AI & Machine Learning Focus – Build strong skills in data analytics, ML, generative AI, and cloud technologies.
Weekend-Friendly Format – Designed for working professionals over 6.5 months for seamless career advancement.
Certification & Support – Preparation for credentials like Certified AI Practitioner with career guidance.
what to expect
Course Outline & Learning Outcomes
Course Outline
- Introduction to Data Science & AI – Understand the role of data, analytics, machine learning, and AI in driving business strategy and organisational competitiveness.
- Foundations of Data Literacy & Business Analytics – Learn how to interpret data, apply structured problem-solving frameworks, and align analytics with business objectives.
- Excel for Data Analysis – Use Excel for data cleaning, transformation, and foundational analysis during the bootcamp phase.
- Python Programming for Analytics – Write and execute Python programs for data manipulation, automation, and exploratory analysis.
- Data Handling & Visualisation with Python – Work with libraries such as Pandas, NumPy, Matplotlib, and Seaborn to clean, transform, and visualise datasets.
- Version Control & Collaboration using GitHub – Manage code repositories, track changes, and collaborate effectively in analytics projects.
- Business Analytics Frameworks (CRISP-DM) – Apply structured methodologies to convert business problems into analytical solutions within ethical and regulatory boundaries.
- Database Design & SQL – Design relational databases, query structured datasets, and manage PostgreSQL and NoSQL systems.
- Cloud-Based Data Solutions – Integrate Azure and IBM Cloud platforms into analytical workflows for scalable data management.
- Data Visualisation & Dashboard Development – Build interactive dashboards using Power BI, Plotly, and Dash to communicate insights effectively.
- Machine Learning Foundations – Explore regression, classification, and clustering techniques using Scikit-learn.
- Model Evaluation & Predictive Analytics – Assess model performance and translate predictive insights into actionable business recommendations.
- Generative AI & Prompt Engineering – Use tools such as ChatGPT, Watsonx, and Azure OpenAI to automate workflows, enhance analysis, and address explainability and AI ethics considerations.
- AI Ethics, Governance & Data Privacy – Understand responsible AI deployment, regulatory compliance (e.g., GDPR principles), and risk management.
- Capstone Project – Develop and present an end-to-end AI or data science solution integrating analytics, machine learning, cloud platforms, and business strategy.
- Career & Certification Preparation – Structured guidance aligned with credentials such as the Amazon Web Services Certified AI Practitioner, along with employability and career development support.
Learning Outcomes
- Apply Excel, Python, and GitHub to perform structured data analysis tasks.
- Use business analytics frameworks such as CRISP-DM to solve real-world problems within ethical and regulatory boundaries.
- Write and execute Python programs using Pandas, NumPy, Matplotlib, and Seaborn for data cleaning, transformation, and visualisation.
- Design, query, and integrate SQL databases and cloud-based data solutions into analytical workflows.
- Develop interactive dashboards and compelling visual narratives for business stakeholders.
- Implement and evaluate machine learning models including regression, classification, and clustering.
- Apply generative AI tools and prompt engineering techniques responsibly to automate analytics and enhance decision-making.
- Translate analytical and predictive insights into strategic, data-driven business recommendations.
Modules
A programme designed to explore beyond subject knowledge
Phase 1: Core Skills Bootcamp (8 Hours)
Core Skills Bootcamp (Face-to-Face – 8 Hours)
This intensive foundation phase prepares learners for the technical modules ahead by strengthening core analytical thinking, digital literacy, and problem-solving skills. Participants are introduced to data fundamentals, Excel basics, structured business thinking, and collaborative tools such as GitHub. The bootcamp ensures learners—especially those with minimal programming experience—are confident and ready to transition into Python, analytics, and AI-focused learning.
Phase 2: Professional Certificate in Data Science & AI
Synchronous E-Learning (84 Hours)
Delivered through live, instructor-led weekend sessions, this component forms the core technical learning experience. Learners develop strong capabilities in Python programming, SQL database design, cloud-based data platforms (Azure, IBM Cloud), data visualisation (Power BI, Plotly, Dash), and machine learning using Scikit-learn. The module integrates business analytics frameworks such as CRISP-DM, ethical AI considerations (GDPR, AI governance), and generative AI tools including ChatGPT, Watsonx, and Azure OpenAI. Emphasis is placed on applying theory to real-world business scenarios.
Assessment (4 Hours Feedback Integrated)
Assessment is integrated into the programme through applied assignments and evaluation of practical work. Learners receive structured feedback to strengthen technical accuracy, analytical reasoning, and business interpretation skills.
Practical (Integrated Within Learning Hours)
Hands-on lab work is embedded throughout the bootcamp and synchronous sessions. Learners work on real datasets, build dashboards, develop machine learning models, and implement AI-powered solutions. This practical exposure ensures the ability to design, implement, and optimise data-driven systems.
key details
Who is this programme for
Who should attend
This programme is designed for ambitious professionals who want to build strong capabilities in Data Science and Artificial Intelligence and apply them in real business environments. It is ideal for individuals looking to transition into data-driven roles, enhance their analytical decision-making skills, or integrate AI solutions into their current functions.
It is particularly suited for working professionals from business, technology, marketing, and operations backgrounds who want to upskill in analytics and AI without interrupting their careers. Early- to mid-career professionals seeking structured, hands-on training in Python, machine learning, cloud platforms, and generative AI will benefit significantly. The programme is also valuable for aspiring and current Data Analysts, Data Scientists, Machine Learning Engineers, AI Engineers, Developers, and Technology Managers who want to strengthen their technical expertise and strategic impact. Additionally, professionals preparing for industry certifications such as the Amazon Web Services Certified AI Practitioner will find the curriculum aligned with their career advancement goals.
Certificate of attendance
A certificate of attendance will be issued to participants who have attended and completed the programme.
ELIGIBILITY
Who can apply for this programme
Minimum Academic Entry Requirement
Applicants must have successfully completed an undergraduate degree in any discipline from a recognised university.
Minimum English Language Entry Requirement
Applicants must meet at least one of the following English language requirements:
Applicants without a valid English qualification may be assessed through placement testing.
Grade C6 or better in English at ‘O’ Level.
Pass in English Language in Year 10 High School qualification or equivalent.
IELTS 5.5 / TOEFL 500 / Grade 4 GCSE.
Completion of the LSBF Preparatory Course in English (Upper Intermediate Level).
Successful completion of the LSBF English Placement Test, including External Placement Test (EPT) prior to arrival in Singapore and Internal Placement Test (IPT) upon arrival, where applicable.
Minimum Age Requirement
Applicants must be at least 21 years old at the time of admission.
