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MBA in Data Science Artificial Intelligence at Indian Institute of Technology Mandi
Mandi, Himachal Pradesh
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About the Specialization
What is Data Science & Artificial Intelligence at Indian Institute of Technology Mandi Mandi?
This MBA in Data Science & Artificial Intelligence program at IIT Mandi focuses on equipping future leaders with analytical prowess and strategic business acumen. Designed to meet the burgeoning demand for data-savvy managers in India, it integrates advanced concepts of data analytics, machine learning, and AI with core business functions. This program distinguishes itself by fostering interdisciplinary skills crucial for navigating data-driven decision-making in the dynamic Indian industry landscape.
Who Should Apply?
This program is ideal for fresh graduates seeking entry into data-driven managerial roles across sectors like technology, finance, and e-commerce. It also caters to working professionals aiming to upskill their analytical capabilities and transition into leadership positions that leverage AI and data science. Candidates with backgrounds in engineering, science, economics, or business, who possess strong quantitative aptitude and a keen interest in technological innovation, will find this curriculum highly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths such as Data Scientist, Business Analyst, AI/ML Product Manager, or Analytics Consultant in top Indian and multinational companies operating in India. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals commanding significantly higher packages (INR 20-40+ LPA). The program aligns with industry demands for certified data professionals, facilitating growth trajectories in India''''s rapidly expanding digital economy.
Student Success Practices
Foundation Stage
Build a Strong Quantitative and Programming Base- (Semester 1-2)
Dedicate significant time to mastering concepts in business statistics, econometrics, and especially programming for business analytics (Python/R). Actively practice coding through online platforms and solve business case studies involving data.
Tools & Resources
HackerRank, LeetCode, Kaggle, DataCamp, Coursera courses on Python/R for Data Science, Official IIT Mandi computing labs
Career Connection
A solid foundation in these areas is crucial for all data science roles and will be heavily tested in technical interviews for internships and placements.
Engage Actively in Peer Learning & Study Groups- (Semester 1-2)
Form collaborative study groups with classmates to discuss complex concepts, solve assignments together, and prepare for exams. Utilize the diverse backgrounds of your peers to gain different perspectives on business and data problems.
Tools & Resources
Campus study rooms, Online collaboration tools (Google Meet, Microsoft Teams), Internal academic forums
Career Connection
Enhances problem-solving skills, fosters teamwork crucial in professional settings, and builds a strong peer network valuable for future career opportunities.
Seek Early Industry Exposure through Workshops and Guest Lectures- (Semester 1-2)
Actively participate in workshops, seminars, and guest lectures organized by the School of Management or other IIT Mandi departments. Network with industry experts and understand current trends in data science and AI applications in India.
Tools & Resources
IIT Mandi''''s official events calendar, LinkedIn for professional networking, Industry reports
Career Connection
Helps identify potential areas of interest for specialization, builds initial professional contacts, and provides insights into industry expectations for entry-level roles.
Intermediate Stage
Apply Learning to Real-World Problems via Internships- (Semester 3 (summer break after Sem 2, and throughout Sem 3))
Leverage the Term Project (Industrial Internship) in Semester 3 to gain hands-on experience in data science or AI roles within a company. Focus on applying machine learning and analytics skills to solve genuine business challenges, aiming for impactful contributions.
Tools & Resources
Company-specific tools, Internal datasets, Python/R, Cloud platforms (AWS, Azure, GCP), Project management software
Career Connection
Crucial for building a professional portfolio, converting internships into Pre-Placement Offers (PPOs), and gaining a competitive edge in the job market.
Specialize through Electives and Advanced Project Work- (Semester 3)
Strategically choose electives from the Data Science & AI pool based on career aspirations (e.g., Deep Learning for AI roles, Big Data Analytics for data engineering). Pursue mini-projects or research relevant to these specialized areas.
Tools & Resources
Specialized libraries (TensorFlow, PyTorch, Spark), Kaggle competitions, Research papers (arXiv, Google Scholar), Domain-specific datasets
Career Connection
Deepens expertise in a chosen sub-field of DSAI, making you a more attractive candidate for specialized roles and advanced research opportunities.
Participate in Data Science Competitions & Hackathons- (Semester 3)
Actively engage in online data science competitions (Kaggle) or hackathons. These platforms provide exposure to diverse datasets and challenges, helping hone problem-solving skills under time pressure.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub, Collaborative coding environments
Career Connection
Builds a demonstrable portfolio of practical skills, allows networking with other data enthusiasts, and is a strong signal to recruiters about your capabilities and initiative.
Advanced Stage
Focus on Dissertation for Deep Research & Impact- (Semester 4)
Utilize the Dissertation in Semester 4 as an opportunity for in-depth research on a complex data science or AI problem relevant to industry. Aim to publish findings or develop a significant prototype, showcasing your ability to conduct independent, high-quality work.
Tools & Resources
Academic databases, Statistical software (R, Python), Advanced ML/AI frameworks, Institutional mentorship
Career Connection
A well-executed dissertation can serve as a powerful resume booster, demonstrating advanced analytical, research, and problem-solving skills, and can lead to academic or specialized industry roles.
Strategize for Placements with Targeted Skill Refinement- (Semester 4 (early to mid-semester))
Begin rigorous preparation for placement interviews by practicing technical questions (coding, machine learning concepts), case studies, and behavioral questions. Tailor your resume and cover letters to specific job descriptions in the DSAI field.
Tools & Resources
Placement cell resources, Mock interviews, Online coding platforms, Industry interview guides, LinkedIn for company research
Career Connection
Directly impacts placement success, securing desirable roles in analytics, data science, or AI engineering in leading companies.
Build a Professional Brand & Network Effectively- (Throughout the program, intensified in Semester 4)
Cultivate a strong online professional presence through LinkedIn, GitHub, and personal portfolio websites. Actively network with alumni, faculty, and industry professionals. Attend industry conferences and webinars to stay updated and expand your professional contacts.
Tools & Resources
LinkedIn, GitHub, Personal website/blog, Professional conferences (e.g., Data Science Summit India), Alumni networks
Career Connection
Essential for long-term career growth, uncovering hidden job opportunities, mentorship, and building a reputation within the data science and AI community in India.
Program Structure and Curriculum
Eligibility:
- First-class Bachelor''''s degree in any discipline or equivalent from a recognized University/Institution with a minimum 60% aggregate marks (or a CPI/CGPA of 6.0 on a 10-point scale); 55% or 5.5 for SC/ST/PwD candidates. Valid CAT score is required (GMAT/GRE/JEE (Advanced) for international candidates). Work experience is desirable but not mandatory. Final year students can also apply.
Duration: 4 semesters / 2 years
Credits: 78 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HM601 | Business Communication | Core | 2 | Introduction to Communication, Written Communication, Oral Communication, Digital Communication, Presentation Skills, Intercultural Communication |
| HM602 | Managerial Economics | Core | 3 | Introduction to Managerial Economics, Demand and Supply Analysis, Production and Cost Analysis, Market Structures, Pricing Strategies, Risk and Uncertainty |
| HM603 | Financial Reporting & Analysis | Core | 3 | Introduction to Financial Accounting, Financial Statements, Accounting Standards, Analysis of Financial Statements, Ratio Analysis, Cash Flow Statement |
| HM604 | Principles of Marketing | Core | 3 | Introduction to Marketing, Marketing Environment, Consumer Behavior, Market Segmentation, Product Strategy, Pricing Strategy, Promotion and Distribution |
| HM605 | Organizational Behavior | Core | 3 | Introduction to OB, Individual Behavior, Group Dynamics, Leadership, Motivation, Organizational Culture, Conflict Management |
| HM606 | Business Statistics | Core | 3 | Introduction to Statistics, Data Collection and Presentation, Probability Distributions, Sampling and Estimation, Hypothesis Testing, Regression Analysis |
| HM607 | Ethics and Corporate Social Responsibility | Core | 3 | Introduction to Business Ethics, Ethical Theories, Corporate Governance, CSR Models, Stakeholder Management, Ethical Decision Making |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HM608 | Operations Management | Core | 3 | Introduction to Operations Management, Process Design, Capacity Planning, Inventory Management, Quality Management, Supply Chain Management |
| HM609 | Human Resource Management | Core | 3 | Introduction to HRM, HR Planning, Recruitment and Selection, Training and Development, Performance Management, Compensation and Benefits, Employee Relations |
| HM610 | Corporate Finance | Core | 3 | Introduction to Corporate Finance, Time Value of Money, Capital Budgeting, Cost of Capital, Working Capital Management, Dividend Policy |
| HM611 | Research Methodology | Core | 3 | Introduction to Research, Research Design, Data Collection Methods, Sampling, Data Analysis, Report Writing, Ethical Considerations |
| HM612 | Econometrics | Core | 3 | Introduction to Econometrics, Simple Linear Regression, Multiple Regression, Violations of Classical Assumptions, Time Series Analysis, Panel Data |
| HM613 | Programming for Business Analytics | Core | 2 | Introduction to Python/R, Data Structures, Control Flow, Functions, Data Manipulation, Visualization, Basic Scripting for Business Problems |
| HM614 | Data Management for Business Analytics | Core | 3 | Database Concepts, SQL, Data Warehousing, ETL Processes, NoSQL Databases, Data Governance, Data Security |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HM615 | Business Strategy | Core | 3 | Introduction to Strategic Management, Strategic Analysis, Strategy Formulation, Strategy Implementation, Corporate Strategy, International Strategy |
| HM616 | Business Analytics with Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation, Feature Engineering, Deep Learning Basics |
| HM617 | Term Project (Industrial Internship) | Core | 2 | Project Planning, Literature Review, Data Collection, Analysis, Report Writing, Presentation, Industry Problem Solving |
| HM701 | Big Data Analytics | Elective (DSAI Specialization) | 3 | Introduction to Big Data, Hadoop Ecosystem, Spark, NoSQL Databases, Data Lakes, Real-time Analytics, Data Governance for Big Data |
| HM702 | Text Mining and Natural Language Processing | Elective (DSAI Specialization) | 3 | Text Preprocessing, Feature Extraction, Sentiment Analysis, Topic Modeling, Named Entity Recognition, Text Classification, Neural Networks for NLP |
| HM703 | Deep Learning | Elective (DSAI Specialization) | 3 | Neural Network Architectures, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Models, Deep Learning Frameworks |
| HM704 | Reinforcement Learning | Elective (DSAI Specialization) | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Q-Learning, Policy Gradient Methods, Deep Reinforcement Learning |
| HM705 | Advanced Econometrics | Elective (DSAI Specialization) | 3 | Generalized Method of Moments, Maximum Likelihood Estimation, Panel Data Models, Causality, Instrumental Variables, Non-parametric Econometrics |
| HM706 | Business Intelligence and Data Visualization | Elective (DSAI Specialization) | 3 | BI Concepts, Data Warehousing, OLAP, Dashboards, Data Storytelling, Visualization Tools (Tableau, Power BI), Geospatial Visualization |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HM618 | Project Management | Core | 3 | Project Life Cycle, Project Planning, Scheduling, Risk Management, Resource Allocation, Project Monitoring and Control, Agile Project Management |
| HM619 | Management Information Systems | Core | 3 | Role of MIS, Information Systems Planning, Database Management, ERP Systems, E-commerce, Cloud Computing, IT Strategy |
| HM620 | Dissertation | Core | 6 | Research Proposal, Literature Review, Methodology, Data Collection, Analysis, Report Writing, Defense |
| HM707 | Social Media Analytics | Elective (DSAI Specialization) | 3 | Social Media Data Collection, Network Analysis, Influence Detection, Sentiment Analysis, Trend Forecasting, Campaign Measurement, Ethical Considerations |
| HM708 | Cognitive Science for Managers | Elective (DSAI Specialization) | 3 | Cognitive Biases, Decision Making, Problem Solving, Perception, Attention, Memory, Human-Computer Interaction, AI in Management |
| HM709 | Data Privacy & Ethics | Elective (DSAI Specialization) | 3 | Data Privacy Regulations (GDPR, India-specific), Ethical AI Principles, Data Security, Anonymization Techniques, Bias in AI, Responsible AI Deployment |
| HM710 | Forecasting & Predictive Analytics | Elective (DSAI Specialization) | 3 | Time Series Models (ARIMA, Exponential Smoothing), Regression for Forecasting, Machine Learning for Prediction, Model Evaluation, Ensemble Methods, Business Applications |
