This module introduces students to Python, one of the key programming languages in data science. The journey begins with foundational Python knowledge, installation and setup of the programming environment, and a look into Python's simple syntax. Students learn about Python's variables, data types, and operators, essential for manipulating and storing data. The second part of this module explores control flow tools, and functions in python. Students get hands-on experience with lists, tuples, dictionaries, conditional statements, loops, and functions. Complementing the Python-specific curriculum, the module also intertwines mathematical concepts like number theory, vectors, and matrices, enabling students to use advanced Python libraries like NumPy, effectively for complex calculations and data analytics.

LEARNING OUTCOMES

• Students will be able to recall and explain the basic syntax, variables, data types, and operators in Python.
• Demonstrate comprehension of Python's primary data structures (lists, tuples, dictionaries) and control flow tools (conditional statements, loops) by explaining their usage and significance in data manipulation and storage.
• Students will comprehend the functionality and usage of Python libraries such as NumPy, pandas, and matplotlib for data treatment.
• Utilize Python for problem-solving, integrating the mathematical concepts of number theory, vectors, and matrices to perform complex computations and analyses.

Content Covered:

• Python introduction and setup.
• Python basic syntax.
• Python variables and data types.
• Usage of Python operators.
• Python data structures.
• Python conditional statements.
• Implementation of Python loops.
• Creation of Python functions.
• Mathematical concepts integration.
• NumPy for numerical computations.
• Data manipulation with pandas.
• Data visualization using matplotlib.
• Descriptive and inferential statistics.
• Probability theory application.
• Understanding Bayes' theorem.
• Correlation and Regression Analysis

This module addresses the principles of creating reliable spreadsheet models, translating conceptual models into mathematical models, and applying them in spreadsheets. It also demonstrates a knowledge of three analytic tools in Excel, Excel functions, and the process of auditing spreadsheet models to assure accuracy. Additionally covered in this module are Decision analysis, Payoff Tables, and Decision Trees. Microsoft Power BI helps users derive practical knowledge from data to solve business concerns, bringing analytical models to corporate decision-making. Learners acquire insight into advanced analytic features of Power BI, such as prediction, data visualizations, and data analysis expressions. Students will learn to create interactive dashboards and reports for data-driven decision-making.

LEARNING OUTCOMES

• Students will comprehend the importance of data visualization and the role of Advanced Excel in this process.
• Apply the knowledge to select the appropriate visual representation for various datasets.
• Create interactive dashboards and visual reports using PowerBI/ Advanced Excel.
• Analyze and interpret data visualizations to derive meaningful insights and aid decision-making.

Content Covered:

• Getting started with Excel.
• Backstage view
• Understanding OneDrive
• Creating and opening workbooks
• Saving and sharing workbooks
• Cell Basics
• Modifying Columns, Rows and Cells
• Formatting Cells
• Working with Multiple Worksheets
• Introduction to Formulas
• Cell referencing
• Introduction to Basic Functions
• Pivot Tables
• Get started with Microsoft data analytics.
• Prepare data for analysis.
• Model data in Power BI
• Visualize data in Power BI
• Complete Project on Power BI

This module highlights the importance of database management in today's data-driven world and learn to design, create, and manage databases to store and retrieve data efficiently and securely. This module takes a deep dive into Data mining to discover patterns in large data sets. The course provides an opportunity to learn concepts, principles, and skills to practice and engage in scalable pattern discovery methods on massive data; discuss pattern evaluation measures; study methods for mining diverse kinds of frequent patterns, sequential patterns, and sub-graph patterns; and study constraint-based pattern mining, pattern-based classification, and explore their applications. Following the preprocessing, the course advances into Data cleaning and preprocessing are essential steps in the data mining process. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data to improve its quality. It includes tasks such as removing duplicates, filling in missing values, and correcting invalid data. Preprocessing, on the other hand, involves transforming raw data into a format that is suitable for analysis.

LEARNING OUTCOMES

• Students will understand the importance of EDA and the process of data cleaning and preprocessing.
• Apply various data preprocessing techniques and manage data quality issues effectively.
• Develop Competency in Various Types of Data Cleaning and Preprocessing.
• Acquire Practical Experience in Managing Numerical and Text Data.

Content Covered:

• Hands on with NumPy library
• Hands on with Pandas Library
• Hands on with Matplotlib Library
• Introduction to scikit-learn Library
• Data Collection
• Numerical Data cleaning and Preprocessing
• Hands on learning in dataset
• Text Data Cleaning and preprocessing
• Hands on learning in dataset
• Pattern Discovery
• Clustering and Classification

This module provides an in-depth understanding of established methods of artificial intelligence and machine learning techniques that enable computers to learn without being explicitly programmed. The module discusses various parts of artificial intelligence, which include ML (Machine Learning) and aims to explain real-world application. The module provides foundational understanding of AI concepts and terms, be able to describe several issues and ethical concerns surrounding AI, and articulate advice from experts about learning and starting a career in AI. Following the preprocessing, the course advances into algorithms such as linear regression, k-NN, decision trees, random forest, etc. for machine learning by supervised and unsupervised learning.

LEARNING OUTCOMES

• Understand Artificial Intelligence and Machine Learning fundamentals.
• Understand the nature of the dataset and the methods for pre-processing it for machine learning.
• Introduction to Supervised and Unsupervised Machine Learning Algorithms
• Demonstrate a deep critical understanding of algorithms and mathematics behind established ML approaches

Content Covered:

• Introduction to Machine Learning
• Supervised Machine Learning
• Unsupervised Machine Learning
• Regression
• Linear
• Univariate
• Multivariate
• Selection of an Algorithm
• Random Forest
• Decision Tree
• Logistic Regression
• Training and Testing models
• Checking F1-score, precision, and Accuracy for models
• Industry Based project

This course is designed to explore the integration of artificial intelligence in operations management. It focuses on how AI can enhance efficiency, productivity, and decision-making in various operational processes. The course will cover recent applications of AI in operations management and supply chain management, with a focus on innovations in healthcare, manufacturing, and retail operations. The course will also discuss primary challenges and opportunities for utilizing AI in those industries. Additionally, the course will cover trending research topics with significant value potential in these areas.

LEARNING OUTCOMES

• Understand the key principles of operations management and how AI can be applied to optimize processes.
• Learn to use AI to forecast demand, manage inventory, and improve supply chain operations.
• Explore AI-driven quality control, maintenance, and resource allocation in operations.
• Analyze case studies and implement AI strategies for operational excellence.

Content Covered:

• Good & Service industry
• Types of Operations Management
• Three Basic Functions of an Organization
• Operational Interfaces with other departments
• Key Decisions of Operations Managers
• Strategic objective
• Strategy Formulation, the process
• Defining Corporate Strategy
• Business/Operational Strategy
• Flow of Operational Strategy
• Global vs. Regional Strategies
• Identifying and implementing operations strategy in domestic and global context

International HR Management with AI is a transformative course that blends the realms of human resource management with the cutting-edge advancements of artificial intelligence (AI). This module is designed to provide students with a comprehensive understanding of the complexities associated with managing human resources on a global scale, while also exploring the ways in which AI can revolutionize traditional HR practices. Through a combination of theoretical concepts and practical applications, students will delve into topics such as cross-cultural management, international labour laws, talent acquisition, workforce diversity, and employee engagement. Furthermore, this course will explore the role of AI in HR functions, including the implementation of AI-driven recruitment tools, predictive analytics for talent management, and the ethical implications of AI adoption in the workplace. By the end of this module, students will be equipped with the knowledge and skills required to navigate the ever-evolving landscape of international HR management with AI and be poised to make impactful contributions in the field. LEARNING OUTCOMES Understand the role of AI in international HR management and its impact on global talent acquisition and retention. Explore AI-driven diversity and inclusion strategies in global HR. Analyse ethical considerations and regulatory aspects related to AI in international HR. Develop AI-enhanced HR policies and strategies for managing a diverse, international workforce. Content Covered: Fundamentals of Human Resource Development Principles, theories, and models of HRD Organizational change Organizational learning and loop concepts Strategic overview of developmental activities Establishing a strategic fit Selection, implementation, and evaluation of HRD initiatives Underpinning theories and empirical evidence in HRD and organizational change Case scenarios in Global HRD initiatives in MNC’s

The Industry-based Capstone Project in Master of Science in Artificial Intelligence in Business involves a close collaboration between students and industry mentors. This collaboration brings a real-world perspective and insights into the business challenges that the AI solution aims to solve. Students will learn how to identify business challenges, collect data, apply AI algorithms, and evaluate the performance of the designed solution. The course equips students with established best practices and methodological frameworks to manage project development processes and prioritize the challenges to tackle. It forms an explicit foundation for developing critical project management, time-management, problem-solving, and presentation skills. Upon completion of the Industry-Based Capstone Project, students will have acquired essential skills such as project management, communication, practical problem-solving, and data analysis. Through this course, students will attain significant industry network exposure and thus gain a competitive edge for desired professional experiences.

LEARNING OUTCOMES

• Apply AI methodologies and techniques to address complex business problems.
• Develop and implement end-to-end AI solutions.
• Collaborate effectively with business stakeholders.
• Analyze the effectiveness of AI solutions.

Content Covered:

• Developing and implementing entire AI systems for their chosen industry
• Communicate and collaborate in a professional manner with business stakeholders.
• Understand business needs and requirements.
• Propose solutions.
• Evaluate the performance of the AI solutions that they develop and assess their impact on the business.
• Measure the success of their solutions and identify areas for improvement in future.

Capstone Projects:

• Optimizing Marketing Spend Using AI
• Customer Lifetime Value Prediction with Machine Learning
• Dynamic Pricing Strategy for E-Commerce: An AI-driven Approach
• Supply Chain Forecasting and Optimization using Artificial Intelligence
• Sentiment Analysis in Social Media for Brand Management
• Computer Vision for Retail: Automated Product Recognition
• Predictive Maintenance for Manufacturing Equipment
• Algorithmic Trading Strategies: AI-driven Portfolio Management
• Predictive Analytics for Employee Retention
• Healthcare Predictive Analytics: Patient Readmission Risk
• Ethical AI Auditing Tool: Bias Detection and Mitigation