Applied Data Analytics and AI for Managers

Data Flows: From Sources to Organised Systems

• Data ingestion, ETL, Merging, Joins

• Warehousing Basics

• KNIME Pipeline Introduction

Data Quality Fundamentals

• Handling Missing Values and Outliers

• Imputation Strategies

Data Transformation into Usable Insights

• Encoding (One-Hot, Label, Target)

• Scaling (MinMax, Standard, Robust)

• Transformations (log, box-cox)

Feature Creation and Selection Made Simple

• Feature Engineering

• Interaction Terms

• Variable Selection Logic

AI-Assisted Data Prep: From Profiling to Smart Suggestions

• AI-assisted Data Preparation with Zerve.AI

• Auto-profiling, Schema Detection

• Smart Feature Suggestions

Automating Data Pipelines with AI

• AI-driven Pipeline Generation (Zerve Agents + Julius Agents)

• Build + Validate Data Prep

Understanding Your Data: Core Exploration Methods

• Manual EDA in Python

• Univariate + Bivariate Analysis

Turning Data into Clear Visuals with Tableau

• Visualisation Principles

• Tableau Visual Analytics

Agent AI for Fast Data Exploration

• AI-generated EDA: Zerve Insights

• Automated Graph Generation

• Julius AI EDA Agent

AI-Generated Narratives and Smart Dashboards

• Data Storytelling

• Auto-generated Narratives

• AI-driven Dashboard Insights

Statistical Foundations for Managers

• Probability, Sampling, Distributions

• Introduction to Statistics Fundamentals

Hypothesis Testing Essentials

• Hypothesis Testing Fundamentals

• t-test, ANOVA, chi-square

Correlation, Covariance & Bootstrap - Applied Learning

• Correlation, Covariance, Bootstrap

• Case-based Applications

Understanding Data Better with AI Assistants

• AI-assisted Statistical Explanation

• Julius AI & Zerve Interpretations

Introduction to Regression Models

• Linear Regression, Regularisation (Lasso, Ridge)

• Model Assumptions

Decision Trees and Classification Basics

• Logistic Regression

• Decision Trees

• Gini vs. Entropy

Predictions with Ensemble Models

• Ensemble Models: RF, GBM

• XGBoost & Light GBM Overview

Evaluating Model Performance

• Model Evaluation Metrics

• Cross-validation

• Hyperparameter Tuning

Agentic AI in Action: AutoML and Comparative Insights

• AutoML with Zerve.AI

• AutoML with Julius AI

• Comparing Outputs

Explainable AI for Managers

• Explainable AI (XAI)

• SHAP, LIME, PDP, ICE

AI Ethics and Governance

• Model Fairness, Drift and Bias Detection

• AI Governance

Deploying Agentic AI for Business Impact

• Model Deployment Basics

• APIs, Batch Scoring

• Deployment Inside Zerve.AI

Clustering Essentials for Managers

• Clustering: K-means, Hierarchical

Spotting Anomalies for Smarter Decisions

• DBSCAN, Anomaly Detection

Data Simplification Techniques

• Dimensionality Reduction: PCA, t-SNE, UMAP

Applying AI to Segment Customers Effectively

• AI-driven Clustering (Zerve, KNIME, Orange)

• Customer Segmentation Project

Understanding Text Data: Key Concepts

• Tokenisation, Lemmatization, POS Tagging

Topic and Sentiment Analysis

• Topic Modelling: LDA, BERTopic

• Sentiment Classification

Semantic Search and AI Text Tools

• Text Embeddings

• Vector Databases

• Semantic Search

Building AI Chatbots with RAG

• RAG (Retrieval-Augmented Generation)

• Build a Mini RAG Chatbot

Understanding of Neural Networks

• Neural Network Basics (ANN, CNN, RNN) with TensorFlow Playground

AI for Image Recognition and Classification

• Image Classification Models

• Transfer Learning

AI-Enabled Object Recognition

• Object Detection (YOLO, SSD)

Multimodal AI for Smarter Insights

• Multimodal AI (CLIP: text+image)

• Zerve Vision Tools

Forecasting Business Trends: Time Series Basics

• Time Series Fundamentals

• ARIMA, SARIMA

ML and AI for Forecasting

• ML-based Forecasting

• Prophet + AI Narrative Generation

Building Trustworthy AI: Ethics, Fairness & Governance

• Responsible AI, Fairness, Governance, Trust Frameworks

Capstone Project

• Final Capstone Presentations

• Evaluation and Feedback

The programme uses a blended learning format that includes live online sessions, self-paced learning, case studies, hands-on projects, and interaction with industry experts. This ensures a mix of theoretical and practical learning tailored to real-world applications.

The programme is designed for mid-to-senior-level professionals with atleast 1 year of work experience in management, IT, consulting, marketing, or finance. A bachelor’s degree in any discipline, along with relevant work experience, is required for admission.

While prior knowledge of data analytics tools or statistical methods is not mandatory, a basic understanding of mathematics, statistics, and business operations can be beneficial. The programme starts with foundational modules to bring all participants up to speed.

IIM Kozhikode is a prestigious institution known for its high academic standards. This programme provides:

• Access to top-tier faculty from IIM Kozhikode

• A comprehensive curriculum covering advanced data analytics concepts

• A practical, application-oriented approach to data analytics

• Networking opportunities with peers and industry leaders

Graduates of the programme can explore roles such as Data Analyst, Business Intelligence Manager, Data Scientist, Analytics Consultant, and other senior management positions where data-driven decision-making is crucial. The programme prepares you to apply advanced analytics in business strategy and operations.

Yes, IIM Kozhikode and its partners often provide financing options such as installment payments or educational loans to help participants manage the fees. Details on payment plans and financing can be obtained from here https://emeritus.org/in/students-loans-and-financing/.

Yes, the programme includes real-world case studies from various industries on data science, machine learning algorithms, predictive analytics solutions, etc.

The programme is designed to help managers enhance their decision-making by understanding how to interpret data and apply analytics to optimize business strategies, identify growth opportunities, improve operational efficiencies, and drive innovation.

Absolutely. While the programme is tailored for managers looking to integrate data analytics into their current roles, it also provides a solid foundation for those seeking to pivot into specialised roles in data science, data analytics, or business intelligence.