Professional Certificate Programme in Data Science and Artificial Intelligence for Business - Batch 13

Programme overview, structure, and deliverables

Role of Data Science & AI in modern organisations

Evolution of analytics → ML → AI in business contexts

Conceptual refresher on mathematical intuition (data, uncertainty, optimisation, non-formulaic)

Overview of the modern DS & AI tool landscape (Python, analytics stacks, cloud platforms, conceptual)

Data-Driven Decision Making

Types of analytics: descriptive, diagnostic, predictive, prescriptive Business problems vs analytical problems

Data Types and Their Comparison

Data Categories

Data Cycle

Analytical Thinking Models

Understanding Data Types of Attributes Data Sources and Data Quality Data Cleaning Identifying Outliers Measures of Centre and Spread

Data Exploration: Hypothesis Testing Vs. Exploratory Data Analysis Data Transformation

Data Scaling

Data Transformation for Normality

What is Data Visualisation and Why is it Important?

Design Principles: Pre-attentive Attributes

Tidy Data Principles Introduction to Basic and Advanced Charts

4C Principles

Dashboard Design Exploratory Vs. Explanatory Dashboards Colour Theory The Use of Colour in Data Visualisation Colour Vision Deficiency Creating a Colour Palette

KPIs, metrics, and OKRs

Designing dashboards for different stakeholders Operational vs strategic dashboards Interpreting dashboards for decision-making Common pitfalls in dashboard-driven management

Statistical intuition: variability, correlation vs causation Confidence, uncertainty, and risk interpretation

Introduction to predictive analytics and forecasting (non-technical) Use cases of predictive analytics in business

Introduction to prescriptive analytics Use cases of prescriptive analytics

Basics of Artificial Intelligence Importance of AI Evolution of AI Classification of AI Generative AI Risks and Limitations of AI

Transformation of Future Job Roles by AI

Machine Learning Concepts

General Learning: Levels of Learning

Machine Learning Approaches

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Deep Learning

Deep Learning Approaches

Machine Learning: A Holistic View Introduction to MLOps

Problem framing: prediction vs pattern-discovery Classification and regression models

Model intuition (no math): simple intuition for how systems learn from past examples

Success metric for the business goal (reducing losses, improving accuracy, improving customer experience)

Recognising common pitfalls in data projects: biased data, misleading correlations, wrong signals, over-complex models

Clustering for sense-making: segmentation (conceptual), business validation Real-world applications: customer segmentation, churn flags, fraud alerts, product grouping, early-warning systems

Reinforcement learning

Real-world applications of reinforcement learning

Understanding when “forecasting” matters: planning for demand, revenue cycles, staffing, inventory, and risk

Recognising patterns in business data: trends, seasonal effects, sudden shifts Explainability (concepts): global vs local explanations; when/why explanations are necessary

Evaluating AI/ML models using business-friendly metrics: linking model performance to ROI, customer outcomes, efficiency gain

Operating norms: human oversight, override/escalation rules, accountability & audit trails

Foundations of Natural Language Processing

Tokens, tokenization, and embeddings: what they represent and why they matter Foundations of Large Language Models (LLMs): transformer intuition (non-technical) SLMs vs LLMs

Prompting patterns: zero-shot, few-shot, role-based prompts, chain-of-thought (conceptual)

Prompt hygiene and instruction quality for reliable outputs Context windows: limits, trade-offs, and design implications Cost drivers in GenAI systems: tokens, latency, caching considerations Strengths and limitations of LLMs in business contexts

Choosing the right approach for each use case

Workflow design over model tuning: map the business process Business evaluation of GenAI pilots: define success in time saved, error reduction Trade-offs between open ecosystems (flexibility, cost, customization) and managed platforms (speed, security, support)

Scale-up playbook: from pilot → playbook → SOP → training, performance dashboards for leaders

Introduction to Agentic AI: why agents are different

Task planning, decomposition, and goal-oriented reasoning (conceptual) Tool use in agents: functions, APIs, search, and external systems Orchestration frameworks: coordinating steps, tools, and decisions Memory types in agentic systems: short-term, long-term, and contextual memory

Credit scoring, fraud detection, AML, risk modelling (traditional AI/ML) GenAI for customer service, report generation, underwriting support Agentic AI for loan processing, dispute resolution, financial ops automation Regulatory constraints, explainability, model risk management Case studies: Retail banking, payments, NBFCs

Predictive analytics for diagnosis, patient risk stratification Computer vision in medical imaging (conceptual)

GenAI for clinical documentation, research summarisation, patient engagement Agentic AI for care coordination, clinical workflow orchestration

Data privacy, ethics, and regulatory considerations (HIPAA-like contexts)

Talent acquisition & recruiting intelligence: AI-assisted screening, skills-based shortlisting, personalized outreach, bias-aware practices

Workforce analytics & employee insights: attrition risk signals, engagement drivers, skills gap mapping, capacity planning for headcount & shifts GenAI for HR documentation & knowledge automation

Agentic AI for HR workflows & employee services: onboarding assistants, HR helpdesk/benefits Q&A

Content automation Segmentation & targeting Campaign optimisation Attribution modeling Personalisation engines Generative brand assets

AI-augmented product discovery

Requirements generation

Roadmap planning

Experimentation frameworks

User research automation

PM copilots

AI as a growth, efficiency, and innovation engine AI-first vs AI-enabled organizations

Platform and ecosystem-based business models Data moats and network effects

Mapping AI use cases to strategic objectives

Case examples of AI-driven value creation

Centralized CoE vs federated AI operating models

Product-led AI vs project-based analytics

Key AI roles: AI PM, Data Scientist, ML/AI Engineer, Business Translator Business–IT–AI collaboration models

AI talent planning and capability roadmaps

AI value measurement, adoption metrics, and ROI

Change management and leadership communication

Modern enterprise data platforms and lakehouse concepts Data products, domain ownership, and data mesh principles Enterprise AI stacks incl. GenAI and RAG (conceptual) Embedding AI into enterprise workflows Batch vs real-time AI integration patterns Agentic AI Systems

AI governance frameworks & oversight models

Responsible AI: fairness, transparency, accountability, safety Regulatory landscapes (DPDP Act India, EU AI Act, managerial lens) Enterprise AI oversight: committees, review boards, risk processes GenAI-related risks: IP, data leakage, compliance Case studies on AI failures & governance best practices

Mini Project 1: Hands-on Analytics-Business Dashboard Challenge (Post Data Science Pillar)

Mini Project 2: GenAI Workflow Automation & Agentic AI Prototype (Post GenAI + Agentic AI Pillars)

Enterprise integration plan: value sizing, adoption roadmap, governance & controls, talent plan, communication pack for leadership

You will have access to the online learning platform, including all videos and programme materials, during the course and for one year after the programme end date. Access to the platform is restricted to registered participants, as per the terms of the agreement.