Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
Professional Certificate Programme in Data Science and Artificial Intelligence for Managers is an eight months online course which is being offered by the Indian Institute of Management Kozhikode (IIMK) in collaboration with Emeritus. This Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course is tailored for mid to senior level professionals aiming to harness the power of data science and AI to solve complex problems and drive innovation within their organisations. The curriculum is designed to provide a practical understanding of data science tools and techniques, enabling participants to apply these skills directly to their work environments.
The Highlights
- 8 months online course
- Pre-recorded video lectures for flexible learning
- 15 assignments
- 5 quizzes
- Hands-on experience with over 10 industry-relevant tools
- 10 hours dedicated to generative AI content
- 4 industry-backed capstone projects for practical application
- Live masterclasses conducted by industry experts
- Certificate from IIM Kozhikode
Programme Offerings
- IIM Kozhikode certificate
- expert faculties
- Comprehensive curriculum
- Pre-recorded videos
- assignments
- quizzes
- Capstone Projects
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIM Kozhikode |
The fees for the course Professional Certificate Programme in Data Science and Artificial Intelligence for Managers is explained below:
Fees components | Amount |
Programme fees | Rs. 1,69,000 |
Eligibility Criteria
The participants need to have a bachelor’s degree or diploma (10+2+3) in any discipline from a recognised institution.
What you will learn
With the help of this Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course, the participants will delve into the fundamental concepts of data science, exploring data cleaning, preparations and visualisation techniques. The participants will gain proficiency in building and evaluating machine learning models,including supervised and unsupervised learning algorithms. The curriculum also covers advanced topics such as neural networks, deep learning, and reinforcement learning, providing a comprehensive understanding of AI methodologies.
The participants will explore the strategic applications of artificial intelligence in various business contexts, including digital marketing, product management, finance and supply chain management. The Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course emphasise ethical considerations and responsible AI practices, ensuring that participants are equipped to implement AI solutions that are both effective and aligned with organisational values.
Who it is for
The target audience for the Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course includes:
Admission Details
The steps to enrol for the Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course are explained in the content that follows:
Step 1: Please check if the program’s commencement date has been communicated. If yes, please proceed to start application now on the homepage.
Step 2: For new registrants, please provide a valid, accessible email address and work experience.
Step 3: Create an account, then add your personal and professional details.
Step 4: In the final step, please make a selection of the payment method you would like to use such as credit/debit card, Internet banking, or UPI to make the payment for the program tuition.
The Syllabus
- Overview of Data Science and AI
- Applications of Data Science and AI
- Data-driven Decision-making
- Impact of Data Science and AI on Industries
- Ethical and Legal Considerations of AI
- AI Tools and Technologies
- Future Trends
- 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
Evaluation process
To earn the certificate from IIM Kozhikode, the participants are required to complete all assignments, quizzes and capstone projects. A minimum score of 70% is required to be eligible for the certificate.
Instructors
IIM Kozhikode Frequently Asked Questions (FAQ's)
The duration of the Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course is 8 months.
This Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course will be taught by Prof. M.P. Sebastian and Prof. Vidushi Pandey.
The participants need to have a bachelor’s degree or diploma (10+2+3) in any discipline from a recognised institution.
The Zoom application is called for to be mounted on your PC/Laptop/Mac. It does function on mobile devices.
This Professional Certificate Programme in Data Science and Artificial Intelligence for Managers course is delivered by a meeting application by the name of Zoom and thus, does not have an in-person aspect to it.