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INTEGRATED-M-SC in Quantitative Economics And Data Science at Birla Institute of Technology, Mesra
Ranchi, Jharkhand
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About the Specialization
What is Quantitative Economics and Data Science at Birla Institute of Technology, Mesra Ranchi?
This Integrated M.Sc. Quantitative Economics and Data Science program at Birla Institute of Technology, Mesra, Ranchi, focuses on blending rigorous economic theory with advanced data science techniques. It prepares students for a data-driven economy, meeting the growing demand in Indian industries for professionals adept at economic analysis, statistical modeling, and machine learning, a key differentiator in the evolving job market.
Who Should Apply?
This program is ideal for fresh graduates with a strong mathematics background seeking entry into roles as Data Scientists, Economic Analysts, or Quantitative Researchers. It also suits career changers aspiring to transition into the fast-growing data and analytics industry in India. Prerequisites typically include Class 12 with Mathematics/Business Mathematics, making it accessible for a broad pool of analytical minds.
Why Choose This Course?
Graduates of this program can expect promising career paths in India across banking, finance, consulting, e-commerce, and tech sectors. Entry-level salaries range from INR 6-10 LPA, with experienced professionals earning significantly more. The strong quantitative and data science foundation ensures growth trajectories into leadership roles in analytics and economic strategy within Indian companies and MNCs operating locally.
Student Success Practices
Foundation Stage
Master Core Mathematics & Python- (Semester 1-2)
Dedicate extra time to reinforce fundamental concepts in Calculus, Linear Algebra, Statistics, and Python programming. Utilize online platforms like NPTEL for deep dives into specific topics and HackerRank/LeetCode for consistent coding practice to build a strong analytical and programming base.
Tools & Resources
NPTEL, HackerRank, GeeksforGeeks, Khan Academy
Career Connection
A solid foundation is crucial for advanced data science and economics subjects, enabling stronger conceptual understanding and better performance in technical interviews for future internships and placements.
Active Participation in Academic Societies- (Semester 1-2)
Join and actively participate in academic clubs related to data science, economics, or coding. Engage in discussions, attend workshops, and contribute to small projects. This fosters peer learning, networking, and soft skill development, enhancing your overall academic experience.
Tools & Resources
Departmental clubs, Technical fests, Student-led study groups
Career Connection
Develops teamwork, communication, and leadership skills highly valued by Indian employers, and provides networking opportunities with seniors and faculty that can lead to project collaborations or mentorship.
Develop Strong Study Habits & Time Management- (Semester 1-2)
Establish a consistent study schedule, prioritize tasks, and manage time effectively to balance coursework, lab assignments, and personal development. Utilize digital planners or traditional methods to track progress and avoid last-minute cramming, ensuring comprehensive learning.
Tools & Resources
Google Calendar, Notion, Pomodoro Technique
Career Connection
Efficient time management leads to academic excellence, reduces stress, and cultivates self-discipline—a critical trait for success in challenging professional environments post-graduation in India.
Intermediate Stage
Undertake Mini-Projects & Kaggle Competitions- (Semester 3-5)
Apply theoretical knowledge gained in Data Structures, Machine Learning, and Big Data by working on personal mini-projects. Participate in Kaggle competitions or similar data challenges to gain hands-on experience, build a portfolio, and learn from diverse datasets.
Tools & Resources
Kaggle, GitHub, Jupyter Notebook, Anaconda
Career Connection
This practical exposure is vital for placements in data science roles, demonstrating problem-solving abilities and proficiency with industry-relevant tools. A strong project portfolio significantly boosts resumes in the Indian job market.
Seek Early Internships & Industry Exposure- (Semester 3-5)
Look for summer internships or part-time roles in data analytics, market research, or economic consulting firms, even if unpaid initially. This provides invaluable industry exposure, helps refine career interests, and builds professional networks within the Indian corporate landscape.
Tools & Resources
Internshala, LinkedIn, College placement cell
Career Connection
Early internships are crucial for understanding real-world applications of economics and data science, improving chances for pre-placement offers and providing a competitive edge during final placements.
Network with Alumni & Industry Professionals- (Semester 3-5)
Actively connect with BIT Mesra alumni working in data science and economics, and attend industry webinars or conferences. Leverage platforms like LinkedIn for informational interviews to gain insights into career paths, skill requirements, and potential opportunities in India.
Tools & Resources
LinkedIn, Alumni Network Portal, Industry meetups
Career Connection
Building a strong professional network can lead to mentorship, internship referrals, and direct job opportunities. It offers invaluable guidance on navigating the Indian job market and understanding industry trends.
Advanced Stage
Specialize through Electives and Advanced Certifications- (Semester 6-8)
Strategically choose electives that align with your career aspirations (e.g., Quantitative Finance, NLP, MLOps). Complement coursework with advanced certifications from platforms like Coursera/edX in specialized areas, focusing on tools and techniques highly sought after in India.
Tools & Resources
Coursera, edX, Udemy, Specialized bootcamps
Career Connection
Specialized skills make you a more attractive candidate for niche roles and higher-paying positions in the competitive Indian tech and finance sectors, demonstrating expertise beyond the core curriculum.
Intensive Placement Preparation & Mock Interviews- (Semester 6-8)
Start rigorous preparation for placements well in advance, focusing on quantitative aptitude, logical reasoning, data science case studies, and behavioral questions. Participate in mock interviews conducted by the placement cell or senior students to refine your interview skills.
Tools & Resources
Placement cell resources, Glassdoor, Mock interview platforms, Aptitude books
Career Connection
Thorough preparation is paramount for securing desirable placements in top Indian companies and MNCs. Confidence and readiness for diverse interview formats are key to success.
Develop a Capstone Project or Thesis with Real-World Impact- (Semester 6-8)
Undertake a capstone project or thesis that addresses a real-world problem, ideally in collaboration with an industry partner. Focus on demonstrating end-to-end data science lifecycle skills, from problem definition to deployment and impact assessment.
Tools & Resources
Industry collaboration, Faculty mentorship, Open-source datasets
Career Connection
A high-impact capstone project serves as a compelling demonstration of your capabilities to potential employers, especially for roles requiring practical application and problem-solving in the Indian context.
Program Structure and Curriculum
Eligibility:
- Passed Class 12 / QE or equivalent examination with minimum 50% (45% for SC/ST/PwD) marks with Mathematics / Business Mathematics as one of the subjects.
Duration: 10 semesters / 5 years
Credits: 202 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MQ1001 | General Physics | Core | 4 | Interference and Diffraction, Polarization, Quantum Mechanics, Solid State Physics, Nuclear Physics, Relativity |
| MA1003 | Basic Mathematics for Data Science | Core | 4 | Sets, Relations, Functions, Matrices and Determinants, Sequences and Series, Limits and Continuity, Differentiation and Integration |
| CH1001 | Environmental Science | Core | 2 | Ecosystems, Natural Resources, Environmental Pollution, Social Issues and Environment, Human Population and Environment |
| HS1003 | Communicative English | Core | 2 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Speaking Skills, Presentation Skills |
| MQ1002 | Computer Basics and Programming in Python | Core | 3 | Computer Fundamentals, Operating Systems, Introduction to Python, Data Types and Operators, Control Flow, Functions |
| MQ1003 | Physics Lab | Lab | 2 | Experiments on Optics, Basic Electronics, Error Analysis, Measurement Techniques |
| MQ1004 | Environmental Science Lab | Lab | 1 | Water Quality Analysis, Soil Analysis, Air Pollution Monitoring, Solid Waste Management Techniques |
| MQ1005 | Programming in Python Lab | Lab | 2 | Python Basic Programming, Conditional Statements, Loops and Functions, Data Structures in Python, File Handling |
| MQ1006 | Computer Workshop | Lab | 1 | Hardware Assembly, Software Installation, Networking Basics, Troubleshooting, Operating System Utilities |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA2001 | Calculus | Core | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Multivariable Calculus, Applications of Derivatives |
| MA2003 | Statistical Methods | Core | 4 | Descriptive Statistics, Probability Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression |
| HS2001 | Organizational Behaviour | Core | 3 | Introduction to OB, Individual Behaviour, Group Dynamics, Leadership, Organizational Culture, Motivation |
| CH2003 | General Chemistry | Core | 2 | Atomic Structure, Chemical Bonding, Thermodynamics, Chemical Kinetics, Electrochemistry |
| MQ2001 | Introduction to Data Science | Core | 3 | Data Science Lifecycle, Data Collection and Storage, Data Cleaning and Preprocessing, Exploratory Data Analysis, Introduction to Machine Learning |
| MQ2002 | Chemistry Lab | Lab | 1 | Volumetric Analysis, Gravimetric Analysis, pH Metry, Conductometry, Spectroscopy Experiments |
| MQ2003 | Statistical Computing Lab (Using R) | Lab | 2 | R Programming Basics, Data Structures in R, Descriptive Statistics in R, Hypothesis Testing in R, Regression Analysis in R |
| MQ2004 | Introduction to Data Science Lab | Lab | 2 | Data Import and Export, Data Cleaning Techniques, Data Visualization with Python, Feature Engineering, Basic Model Building |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA3001 | Linear Algebra | Core | 4 | Vector Spaces, Matrices and Linear Transformations, Eigenvalues and Eigenvectors, Inner Product Spaces, Matrix Decompositions |
| MA3003 | Probability Theory | Core | 4 | Random Variables, Probability Distributions, Joint and Conditional Probability, Expectation and Variance, Limit Theorems |
| MA3005 | Differential Equations | Core | 4 | First Order Differential Equations, Higher Order Linear Equations, Series Solutions, Laplace Transforms, Systems of Differential Equations |
| MQ3001 | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms, Hashing |
| MQ3002 | Principles of Economics | Core | 3 | Introduction to Economics, Demand and Supply, Market Structures, National Income, Inflation and Unemployment |
| HS3001 | Professional Communication | Core | 2 | Business Communication, Report Writing, Presentation Skills, Interview Techniques, Group Discussion |
| MQ3003 | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversals, Graph Algorithms, Sorting and Searching Implementations |
| MQ3004 | Professional Communication Lab | Lab | 1 | Public Speaking Practice, Resume Building, Mannerism and Etiquette, Negotiation Skills |
| MQ3005 | Principles of Economics Lab | Lab | 1 | Economic Data Analysis, Demand and Supply Estimation, Market Equilibrium Analysis, GDP Calculation Exercises |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA4001 | Numerical Methods | Core | 4 | Root Finding Methods, Interpolation, Numerical Integration, Numerical Differentiation, Solutions of Linear Systems |
| MA4003 | Optimization Techniques | Core | 4 | Linear Programming, Simplex Method, Duality Theory, Non-linear Programming, Game Theory Basics |
| MQ4001 | Database Management Systems | Core | 3 | Database Architecture, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| MQ4002 | Microeconomics | Core | 3 | Consumer Behavior, Producer Behavior, Market Structures, Factor Markets, Welfare Economics |
| MQ4003 | Introduction to Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression, Classification, Model Evaluation, Feature Selection |
| MQ4004 | Database Management Systems Lab | Lab | 2 | SQL Commands, Database Design, Advanced Queries, Stored Procedures, Database Administration |
| MQ4005 | Machine Learning Lab | Lab | 2 | Linear Regression Implementation, Logistic Regression, Decision Trees, Clustering Algorithms, Model Performance Metrics |
| MQ4006 | Microeconomics Lab | Lab | 1 | Demand-Supply Simulation, Cost-Benefit Analysis, Market Equilibrium Problems, Elasticity Calculations |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA5001 | Statistical Inference | Core | 4 | Point Estimation, Interval Estimation, Hypothesis Testing Theory, Non-parametric Tests, Bayesian Inference |
| MQ5001 | Design and Analysis of Experiments | Core | 3 | Basic Principles of DOE, Completely Randomized Design, Randomized Block Design, Factorial Experiments, Analysis of Variance (ANOVA) |
| MQ5002 | Big Data Technologies | Core | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce, HDFS, Spark, NoSQL Databases |
| MQ5003 | Macroeconomics | Core | 3 | Circular Flow of Income, National Income Determination, Consumption and Investment, Money and Banking, Fiscal and Monetary Policy |
| MQ5004 | Web Designing and Mining | Core | 3 | HTML, CSS, JavaScript, Web Architecture, Web Crawling, Web Content Mining, Web Usage Mining, Social Media Mining |
| MQ5005 | Big Data Technologies Lab | Lab | 2 | Hadoop Installation, MapReduce Programming, Spark Data Processing, Hive and Pig Scripting, MongoDB Operations |
| MQ5006 | Web Designing and Mining Lab | Lab | 2 | Website Development, Web Scraping with Python, Data Extraction Techniques, Text Preprocessing, Sentiment Analysis |
| MQ5007 | Macroeconomics Lab | Lab | 1 | GDP Growth Analysis, Inflation Data Interpretation, Fiscal Policy Impact, Monetary Policy Effects, Economic Model Simulations |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MQ6001 | Econometrics | Core | 3 | Classical Linear Regression Model, OLS Assumptions, Violations of OLS Assumptions, Panel Data Models, Time Series Econometrics |
| MQ6002 | Business Analytics | Core | 3 | Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, Decision Making, Business Intelligence Tools |
| MQ6003 | Time Series Analysis and Forecasting | Core | 3 | Components of Time Series, Stationarity, ARIMA Models, ARCH/GARCH Models, Forecasting Techniques |
| MQ6004 | Deep Learning | Core | 3 | Neural Network Fundamentals, Activation Functions, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models |
| MQ6005 | Econometrics Lab | Lab | 2 | Regression Analysis with Software, Hypothesis Testing in Econometrics, Heteroscedasticity Detection, Autocorrelation Correction |
| MQ6006 | Business Analytics Lab | Lab | 2 | Data Visualization for Business, Predictive Modeling for Marketing, Optimization for Operations, Reporting and Dashboarding |
| MQ6007 | Time Series Analysis and Forecasting Lab | Lab | 2 | ARIMA Model Implementation, Forecasting with Exponential Smoothing, Seasonality Analysis, Model Evaluation for Time Series |
| MQ6008 | Deep Learning Lab | Lab | 2 | TensorFlow/PyTorch Basics, Implementing CNNs for Image Classification, Building RNNs for Sequence Data, Hyperparameter Tuning |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MQ7001 | Data Visualization | Core | 3 | Principles of Data Visualization, Static and Interactive Visualization, Storytelling with Data, Tools: Tableau, Power BI, D3.js, Dashboard Design |
| MQ7002 | Financial Economics | Core | 3 | Financial Markets and Instruments, Portfolio Theory, Asset Pricing Models (CAPM), Derivatives, Risk Management |
| MQ7003 | Introduction to Game Theory | Core | 3 | Strategic Form Games, Extensive Form Games, Nash Equilibrium, Repeated Games, Evolutionary Game Theory |
| MQ7004 | Operations Research | Core | 3 | Linear Programming, Transportation and Assignment Problems, Network Models, Queuing Theory, Inventory Management |
| MQ7005 | Elective – I | Elective | 3 | Blockchain Technology & Cryptocurrency, Computer Vision, Digital Marketing & Analytics, Data Engineering with ETL, Financial Reporting & Analysis |
| MQ7006 | Data Visualization Lab | Lab | 2 | Creating Interactive Dashboards, Advanced Chart Types, Geospatial Data Visualization, Storyboarding Data |
| MQ7007 | Financial Economics Lab | Lab | 1 | Portfolio Optimization, Risk-Return Analysis, Option Pricing Models, Financial Data Analysis |
| MQ7008 | Project | Project | 2 | Problem Identification, Literature Review, Methodology Design, Data Collection and Analysis, Report Writing |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MQ8001 | Cloud Computing for Data Science | Core | 3 | Cloud Computing Paradigms, AWS/Azure/GCP for Data Science, Data Storage in Cloud, Cloud-based ML Platforms, Serverless Computing |
| MQ8002 | Natural Language Processing | Core | 3 | Text Preprocessing, Tokenization and Stemming, Word Embeddings, Sentiment Analysis, Named Entity Recognition, Language Models |
| MQ8003 | Elective – II | Elective | 3 | Marketing Analytics, Health Data Science, Geospatial Data Science, Social Network Analysis, Supply Chain Analytics |
| MQ8004 | Elective – III | Elective | 3 | Explainable AI, Reinforcement Learning, Ethical AI and Data Governance, Industrial IoT & Smart Manufacturing, Advanced Econometrics |
| MQ8005 | Cloud Computing for Data Science Lab | Lab | 2 | Deploying ML Models on Cloud, Cloud Data Warehousing, Working with Cloud APIs, Cloud Security Basics |
| MQ8006 | Natural Language Processing Lab | Lab | 2 | Text Classification, Chatbot Development, Topic Modeling, Machine Translation Basics |
| MQ8007 | Project | Project | 2 | Project Planning, Data Acquisition, Methodology Implementation, Results Interpretation, Technical Documentation |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MQ9001 | Quantitative Finance and Derivatives | Core | 3 | Stochastic Processes, Black-Scholes Model, Interest Rate Models, Risk-Neutral Pricing, Hedging Strategies |
| MQ9002 | Machine Learning Operations (MLOps) | Core | 3 | ML Model Deployment, Model Monitoring, Version Control for ML, CI/CD for ML, Scalability and Reliability |
| MQ9003 | Elective – IV | Elective | 3 | Algorithmic Trading, Predictive Analytics for Business, Quantum Computing for Data Science, Advanced Deep Learning, Image and Video Analytics |
| MQ9004 | Elective – V | Elective | 3 | Actuarial Science, Risk Management & Insurance, Behavioural Economics, Environmental Economics & Policy, Research Methodology |
| MQ9005 | Quantitative Finance and Derivatives Lab | Lab | 2 | Pricing Options with Python, Monte Carlo Simulation in Finance, Portfolio Optimization Strategies, Financial Data Visualization |
| MQ9006 | Machine Learning Operations (MLOps) Lab | Lab | 2 | Model Deployment Pipelines, Containerization (Docker), Orchestration (Kubernetes), Monitoring Tools for ML Models |
| MQ9007 | Project | Project | 2 | Advanced Data Modeling, Algorithm Selection, Performance Tuning, Result Validation, Presentation of Findings |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MQ10001 | Integrated M.Sc. Thesis Project | Project | 10 | Research Proposal Development, Extensive Literature Review, Independent Research Execution, Thesis Writing, Viva Voce |
| MQ10002 | MOOC / Summer Internship / Project (Self Study) | Elective/Internship/Project | 7 | Online Course Completion, Industry Internship Experience, Self-Directed Project, Advanced Skill Acquisition |
