Professional Certificate Programme in Data Science and Artificial Intelligence for Managers

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 Feature Selection

• Data Modelling Process

• Data Modelling: Overfitting and Underfitting

• Avoiding Overfitting and Underfitting

• Data Modelling: Training and Testing

• Data Model Evaluation

• Errors and Biases

• Data-Driven Decision Making

• Types of Data Analytics

• Data Categories

• Data Cycle

• From Small Data to Big Data

• Levels of Data

• Learning from Data

• Analytical Thinking Models

• Descriptive Analytics

• Diagnostic Analytics

• Predictive Analytics

• Prescriptive Analytics Using Solver

• What is Data Visualisation and Why is it Important?

• Design Principles: Pre-attentive Attributes

• Tidy Data Principles

• Introduction to Basic and Advanced Chats

• 4C Principles

• Dashboard Design

• Exploratory Vs. Explanatory Dashboards

• Colour Theory

• The Use of Colour in Data Visualisation

• Colour Vision Deficiency

• Creating a Colour Palette

Data Analytics Capstone Project

• Basics of Artificial Intelligence (AI)

• Importance of AI

• Evolution of AI

• Classification of AI

• Introduction to 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

• Supervised Learning: Process

• Classification Tasks

• Data Encoding

• Performance Metrics of Classifiers

• Support Vector Machine (SVM) Classifier

• Naïve Bayes Classifier

• Regression: Uses, Types and Related Terminologies

• Linear Regression

• Simple Linear Regression

• Multiple Linear Regression (MLR)

• Regression Performance Metrics

• Advantages and Limitations of Linear Regression

• Nonlinear Regression

• Classification vs Regression

• Logistic Regression

• KNN Classifier

• Decision Tree Classifiers

• Classifying Ensemble Methods

• Bagging

• Boosting

• Stacking

• Random Forest Classifier

• Boosting Classifier

• AdaBoost Classifier

• Gradient Boosting Classifier

• XGBoost Classifier

• SVM Regression

• Decision Tree Regression

• Random Forest Regression

• Ridge and Lasso Regression

• Neural Network Regression

• Multivariate Regression

• Time series forecasting and decomposition

• Components of time series data

• Forecasting models

• Moving average model

• Exponential smoothing

• Autoregressive model

• ARMA model

• ARIMA model

• Introduction to Unsupervised Learning

• Introduction to Clustering

• Similarity or Distance Measures

• Introduction to K-Means Clustering

• K-Means Clustering: An Example

• Issues with the Clustering Method

• Nearest Neighbour Clustering - Cluster Centre: Demo

• Using Dendrogram for Cluster Visualisation

• Using the Elbow Curve for Cluster Visualisation

• Limitations of K Means Clustering

• Hierarchical Clustering

• DBSCAN Clustering

Capstone Project on Machine Learning

• Basics of Artificial Neural Networks

• Backpropagation in Neural Networks

• Applications, Benefits and Limitations of Artificial Neural Networks

• Multi-Layer Neural Networks

• Basics of Deep Learning

• Deep Learning Libraries

• Choosing the Parameters and Hyperparameters of Neural Networks

• Choosing the Remaining Hyperparameters of Neural Networks

• Convolutional Neural Networks (CNNs)

• Recurrent Neural Networks

• Long Short-Term Memory (LSTM) Networks and Generative Pre-Trained Transformers (GPTs)

• Principles of Reinforcement Learning

• Tools for Reinforcement Learning

• Exploitation Vs. Exploration Dilemma

• Markov Decision Process (MDP) and Q-Learning

• Advantages and Disadvantages of Reinforcement Learning

• Applications of Reinforcement Learning

• Understanding Natural Langugage Processing (NLP)

• NLP Tasks - Natural Language: Fundamental Aspects

• Text Preprocessing

• Stemming and Lemmatisation

• Transforming Text into a Structured Form

• Word Embeddings

• Training the Word2vec Model

• Social Media Analytics

• Text Classification

• Sentiment Analysis

• Topic Modelling

• Text Summarisation

• Conversational AI

• Basics of Recommender Systems

• Conditions for Building a Recommender System

• Types of Recommender Systems

• Simple Ranking Recommender System

• Knowledge-based Ranking Recommender System

• Association Rule Mining System

• Collaborative Filtering System

• Transfer Learning and Pre-trained Models

• Advanced Generative AI Models

• GAN Training Techniques

• GAN Evaluation Techniques

• Additional Considerations

• Prompt Engineering

• Prompt Engineering Examples

• Fine-tuning

• Fine-tuning Using Additional Data

• Fine-tuning Model Parameters

• Introduction to Generative AI Creativity Tools

• Generative AI Creativity Tools Examples

• Integrating Generative and Discriminative Models

• Ethical Considerations

• Importance of Ethics in AI

• Notions of Fairness & Ethics

• Explainable AI

• Ethical issues in AI

• Algorithmic bias

• Power imbalance

• Privacy concerns

• Disinformation

• Labour issues

• Strategies to manage ethical concerns of AI

• Technical Approaches

• Non- Technical approaches

• Current regulatory frameworks around AI

• Synergising Digital Marketing with Gen AI

• Benefits of Gen AI in DM

• Gen AI Tools for DM

• Importance of Human Oversight

• Future Potential

• Potential Downsides

• Ethical Considerations for DM

• Introduction to Gen AI Tools for DM

• ChatGPT and Bard Demos

• Mid-Journey Demo

• Customised Tool Demo

• Introduction to Gen AI for PM

• Product Management

• Gen AI for Execution

• Gen AI for User Experience

• Gen AI for Market Research

• Conclusion

• Gen AI Position in Leadership

• Requisites for Leadership

• Gen AI Case Study

• Harnessing Text-based Gen AI Tools

• Harnessing Image-based Gen AI Tools

• Gen AI Tools for Leadership

• ChatGPT Scenario Demos

• Google Bard Scenario Demos

• Customised Tool Demo

• Introduction and Use Cases

• Key Drivers and Benefits

• Gen AI Tools for Finance

• Sentiment Analysis

• Report Generation

• Shareholder Communications

• Challenges in AI Implementation

• Askyourpdf Demo

• FinChat.io Demo

• OpenAI Demo

Gen AI in Supply Chain Management

Capstone Project on Gen AI

• Understanding the Need for AI Integration

• Identifying Potential Applications for AI Integration

• Challenges and Barriers to AI Integration

• Strategies for Seamless Integration of AI into Existing Systems

• Assessing ROI and Business Value of AI Integration

• Case Studies of Successful AI Integration Projects

• Best Practices for Managing Change During AI Integration

• Ensuring Data Security and Privacy in AI Integration

• Future Trends in AI Integration Technologies and Practices

• Overview of Generative AI (Gen AI) and its Potential Impact

• Shifting Paradigms in Leadership and Management with Gen AI

• Transformation of Decision-Making Processes with Gen AI

• Enhancing Creativity and Innovation Through Gen AI

• Implications of Gen AI on Workforce Dynamics and Organizational Structure

• Ethical and Societal Implications of Gen AI in Industry

• Opportunities and Challenges for Industry Disruption with Gen AI

• Case Studies of Gen AI Implementation in Leadership and Management

• Future Outlook: Evolving Role of Leaders and Managers in the Gen AI Era

• Cultivating an AI-Ready Organizational Culture

• Establishing Governance Frameworks for AI Adoption and Implementation

• Ensuring Ethical and Responsible AI Practices

• Addressing Bias and Fairness in AI Algorithms and Systems

• Compliance Considerations for AI Applications in Regulated Industries

• Transparency and Accountability in AI Decision-Making

• Balancing Innovation with Risk Management in AI-led Cultures

• Collaborative Approaches to AI Governance and Compliance

• Continuous Monitoring and Evaluation of AI Systems for Compliance

• Defining AI Strategy Objectives and Goals

• Assessing Organizational Readiness for AI Adoption

• Aligning AI Strategy with Business Goals and Vision

• Developing a Roadmap for AI Implementation

• Building AI Infrastructure and Capabilities

• Establishing AI Governance and Compliance Frameworks

• Identifying Key Stakeholders and Roles in AI Strategy Execution

• Measuring and Evaluating the Success of AI Strategy Implementation

• Iterative Improvement and Adaptation of AI Strategy

Capstone Project on AI Strategy

• Supervised and unsupervised learning approaches

• Covariance and Correlation Building

• Training and Evaluating a Classifier Model

• Building and Evaluating KNN and Perceptron Models

• Building and Evaluating Random Forest

• XGBoost Classifier Models

• Text Processing

• Sentiment Analysis

• Image Recognition

• Recommendation systems Video Analysis

• Deep Learning Model

• Building an AI Classifier Applying Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) for Feature Selection in Classification

The programme 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 course is designed to be one of the most comprehensive data science classes available.

The IIM Kozhikode Data Science and Artificial Intelligence for Managers 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 Artificial Intelligence for Managers Programme is INR 1,79,900 + 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, including the possibility of paying the course fee in instalments. This makes the program accessible to a broader range of participants, especially those who might be concerned about data scientist course fees.

The programme 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 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 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 for its depth and focus on managerial applications.

The scope of artificial intelligence and data science is expansive, covering various industries such as finance, healthcare, retail, and technology. As these fields continue to evolve, they offer numerous opportunities for career growth and are poised to revolutionize how businesses operate globally. AI and data science are essential for driving digital transformation and creating new, innovative solutions across industries, making data science courses highly valuable in today's market.