Professional Certificate Programme in Data Science and AI for Business

Module 2: Introduction to Data Analytics & Data‑Driven Decision Making

• 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

Module 3: Data Cleaning, Preparation & Quality Management

• Understanding Data

• Types of Attributes

• Data Sources and Data Quality - May need refilming for complexity

• 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

Module 4: Exploratory Data Analysis & Data Visualization

• 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

Module 5: Dashboards, Metrics & Performance Analytics

• KPIs, metrics, and OKRs

• Designing dashboards for different stakeholders

• Operational vs strategic dashboards

• Interpreting dashboards for decision‑making

• Common pitfalls in dashboard‑driven management

Module 6: Statistical, Predictive and Prescriptive Analytics for Managers

• 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

Module 7: Introduction to Artificial Intelligence

• 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

Module 8: Introduction to Machine Learning

• 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

Module 9: Supervised & Unsupervised Learning and Reinforcement Techniques for Managers

• 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

Module 10: Strategic Forecasting, AI/ML Model Evaluation for Business

• 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

Module 11: Foundations of Language Models & Prompting

• 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

Module 12: GenAI Solutions for the Enterprise

• 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

Module 13: Agentic AI Foundations, Planning & Tool Use

• 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

Module 14: Agentic Systems: Agentic AI & Emerging Platforms

• Emerging Agentic Platforms

• Single-agent vs multi-agent systems: patterns and trade-offs

• Open-Source vs Closed-Source AI Systems

• Agentic Analytics

• Agent Marketplaces & Plug-and-Play Workflows

• Business Process Re-Design with Agents

• Measuring Value & Impact of Agent Systems

Module 15: AI, GenAI & Agentic AI in Banking & Financial Services

• 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

Module 16: AI, GenAI & Agentic AI in Healthcare & Life Sciences

• 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)

Module 17: AI, GenAI & Agentic AI in Human Resources (HR)

• 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

Module 18: AI for Digital Marketing, Growth & Customer Experience

• Content automation

• Segmentation & targeting

• Campaign optimisation

• Attribution modeling

• Personalisation engines

• Generative brand assets

Module 19: AI for Product Management, Innovation & Tech Strategy

• AI-augmented product discovery

• Requirements generation

• Roadmap planning

• Experimentation frameworks

• User research automation

• PM copilots

Module 20: AI-First Business Models & Value Creation

• 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

Module 21: AI Operating Models, Organization & Change

• 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

Module 22: Enterprise Data, AI Platforms & AI/Agentic Integration

• 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

Module 23: AI Governance, Risk, Policy & Responsible AI

• 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

Module 24: Hands-on Mini Projects

• 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)

Module 25: Strategy & Integration Capstone

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

The eligibility requirements for the IIM Kozhikode Data Science and AI for Business Programme include a bachelor's degree in any discipline. Although no mandatory work experience is required, having a professional background in a related field such as data science or AI would be advantageous.

The data science and artificial intelligence courses is designed for working professionals in mid- to senior management roles who want to build their expertise in data science, machine learning, and artificial intelligence. It is particularly suited for decision-makers who want to leverage data-driven insights and AI techniques to enhance their strategic and operational capabilities.

The curriculum covers key areas such as data analysis, statistical methods, machine learning, deep learning, AI applications, and more. Participants will engage in practical, hands-on learning through projects and case studies, ensuring they develop real-world expertise in data science and AI. This data science AI course is designed to be one of the most comprehensive data science classes available.

The IIM Kozhikode Data Science and Artificial Intelligence for Business Programme is delivered through recorded video sessions, offering flexibility for participants to learn at their own pace. This allows them to balance their professional and personal commitments while taking these data science classes.

The fee for the IIM Kozhikode Data Science and AI for Business Programme is INR 1,89,000 + GST. This includes access to all course materials, recorded video lectures, and the certification upon completion. For those comparing data science course fees, this fee reflects the programme's comprehensive content and the prestige of an IIMK certification.

Yes, there are financing options available in this data science machine learning course, including the possibility of paying the course fee in instalments. This makes the data science and AI certification course accessible to a broader range of participants, especially those who might be concerned about data scientist course fees.

This data science machine learning course will cover a variety of industry-standard tools and technologies, such as Python, R, TensorFlow, Keras, and more. These tools are essential for conducting data analysis, building machine learning models, and applying AI techniques in various business contexts, making it a highly practical data science course.

This data science with gen AI course is highly regarded because it offers a blend of theoretical knowledge and practical application, delivered by experienced IIM Kozhikode faculty. The programme’s comprehensive curriculum is designed to equip mid to senior level managers with the skills needed to implement data-driven strategies and make informed decisions in an AI-driven business environment. When evaluating data science course options, this one stands out among many AI courses in Kozhikode for its depth and focus on managerial applications

Data Science and Artificial Intelligence are applied across industries and business functions to improve decision-making, forecast outcomes, automate workflows, personalize experiences, and support innovation. Their scope now extends from analytics and Machine Learning to Generative AI, Agentic AI, enterprise integration, governance, and responsible AI adoption.

Yes. The curriculum covers Machine Learning approaches, business-focused model evaluation, Large Language Models, prompting, enterprise GenAI solutions, and Agentic AI systems and workflows. Participants also explore applications across industries and business functions, along with enterprise AI integration, governance, risk, and responsible AI adoption.