Easwari School of Liberal Arts(ESLA)

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Easwari School of Liberal Arts Economics M.Sc. Economics

M.Sc. Economics

Department of Economics

M.Sc. Economics

Rebuilt with AI. Rooted in Theory.

"The main aim of the Programme is to equip the students with theories and tools of economic analysis in major areas of its application and focus on the emerging area of Economics"

The M.Sc. Economics programme at SRM University-AP is designed to equip students with a strong foundation in economic theory while integrating advanced computational methods for real-world applications. The curriculum blends data science, policy analysis, and economic modelling to prepare graduates for a rapidly evolving, technology-driven global economy.

Flyer

M.Sc. Economics Flyer

Fee Structure

Why Choose This Programme?

Industry-Relevant Curriculum

The programme integrates economics with emerging fields like Artificial Intelligence, Machine Learning, and Data Science, ensuring graduates stay ahead in the evolving job market.

Research-Led Learning

Students engage in advanced research through dissertations, policy analysis, and real-world case studies.

Strong Analytical Foundation

Focus on quantitative techniques, econometrics, and computational tools for data-driven decision-making.

Career-Ready Approach

Designed to prepare students for roles in finance, consulting, public policy, and analytics.

Global Opportunities

Exposure to international academic standards and opportunities for higher studies and research collaborations.

Program Overview

The M.Sc. Economics program at SRM University-AP offers advanced training in economic theory, quantitative methods, and applied analysis, structured around one central principle: machine learning and large language models are not replacing econometrics — they are extending it. Econometric Methods in Semester I maps what structural identification achieves and when AI or ML methods are the right next step. Machine Learning for Economics in Semester II, open to both tracks, takes this further into production-grade tools. Two specialization tracks — Finance & Data Science and Development & Policy — prepare graduates for the full range of research, policy, financial, and doctoral careers.

A note on AI and coding. Given the rapid progress in AI, coding is increasingly becoming a task AI tools handle well. Both AI and Computational Methods for Economists and Machine Learning for Economics have been designed with this reality in mind. The emphasis is not on syntax memorisation but on training students to understand what AI coding tools such as Codex are producing — to read, evaluate, and direct computational output rather than simply generate it. Students learn to code to the level where they can verify that the tool is doing what the economic problem requires.

Program Educational Objectives

At the end of the program, students will:

  • Achieve competence in advanced economic analysis, with a clear understanding of when structural econometric models, machine learning methods are each the appropriate tool — and the theoretical reasons why.
  • Develop specialised expertise in Finance & Data Science or Development & Policy, applying AI-augmented econometrics and, where appropriate to real problems — from financial risk modelling to causal impact evaluation.
  • Develop professional excellence in research, policy, or financial analysis — including the critical judgement to evaluate AI outputs for validity, recognise the limits of ML in causal settings, and communicate computational findings with intellectual honesty.
  • Develop capacity for lifelong learning, grounded in the understanding that tools change but the core questions of identification, inference, and economic mechanism do not.

Program Specific Outcomes

  • Enhanced employability across finance, consulting, data analytics, policy research, and development — with the ability to deploy ML, NLP, and other optimisation techniques on economic problems, grounded in the theoretical understanding of what each method can and cannot identify.
  • Graduate-level training in economic theory, microeconomics, macroeconomic modelling, and econometrics — redesigned so that Econometric Methods maps the boundary between structural models and ML/AI methods, giving students a principled framework rather than a disconnected toolbox.
  • Programming proficiency built in Semester I through AI and Computational Methods for Economists and extended in Semester II through Machine Learning for Economics — open to both tracks — giving all students hands-on experience with Python, ML pipelines, NLP tools, and the AI API on real Indian and international economic datasets.
  • Ph.D. readiness through rigorous quantitative training — including causal inference at scale, and other computational methods— positioning graduates at the frontier of computationally intensive economic research.

Track 1 — Finance & Data Science

This track prepares students for careers in financial analysis, risk management, fintech, and quantitative finance. Students develop expertise in programming, financial econometrics, derivatives, portfolio management, and data-driven decision making.

Track 2 — Development & Policy

This track equips students for careers in policy research, international development, public sector economics, and social sector work. Students gain expertise in causal inference, impact evaluation, institutional analysis, welfare economics, and sector-specific policy domains including health, agriculture, labor, and gender economics.

Highlights

  • Globally-oriented and Multi-dimensional approach
  • Proficiency in Quantitative methods
  • Internship Opportunities with Reputable Institutions
  • Tutelage of Highly-Qualified Faculty members
  • State-of-the-art Infrastructure

M.Sc. Economics Curriculum

Semester I — Common Foundation (22 Credits)

All students complete a common first semester. The fifth course is a track elective whose choice determines the Semester II specialization. AI and Computational Methods for Economists (2 credits) runs alongside all core courses, building the computational foundation for Semester II.

Code Course Title Credits
EC XXX Advanced Microeconomic Theory & Applications 4
EC XXX Dynamic Macroeconomic Modelling 4
EC XXX Advanced Mathematical Methods for Economics 4
EC XXX Econometric Methods 4
EC XXX Advanced Development Economics OR Advanced Financial Economics ★ 4
EC 5XX AI and Computational Methods for Economists 2

★  Choice of track elective determines the student’s Semester II specialization.

Students from both tracks may elect a third semester comprising a 12-credit dissertation under faculty supervision. Successful completion leads to the award of M.Sc. Economics with Research. The dissertation must engage with a substantive economic question using methods from the student’s chosen specialization track.

Credit Distribution Summary

One-Year M.Sc. Program
Component Credits
M.Sc. Semester I — Common Foundation 22
M.Sc. Semester II — Specialized Track 20
Total Credits 42
Two-Year M.Sc. — Year 1 Curriculum

Students with a three-year Bachelor’s degree complete Year 1 alongside B.Sc. Economics Honours students, building the analytical and quantitative foundation for M.Sc. studies.

Code Course Title Credits
ECO 4XX Optimization for Economics 4
ECO 4XX Statistical Methods for Economics 4
ECO 4XX Topics in Microeconomics (Social Choice & Welfare) 4
ECO 404 Theories of Economic Growth 4
ECO 4XX Computational Methods in Economics 4
Code Course Title Credits
ECO 420 Time Series Econometrics 4
ECO 405 Industrial Organization 4
CE Monetary Economics (Core Elective) 4
CE Political Economy of States & Markets (Core Elective) 4
CE 400 level (Core Elective) 4

Note: Students complete all three core elective courses in Semester VIII.

Semester II — Track 1: Finance & Data Science (20 Credits)

Three fixed courses; students choose two from the Track 1 Core Elective menu.

Code Course Title Credits
EC 5XX Machine Learning for Economics (also open to Track 2 students) 4
EC 5XX Portfolio Management 4
EC 5XX Financial Econometrics 4
EC 5XX Core Elective 1 (from Track 1 elective menu) 4
EC 5XX Core Elective 2 (from Track 1 elective menu) 4

Three fixed courses; students choose two from the Track 2 Core Elective menu.

Code Course Title Credits
EC 5XX Causal Methods and Impact Evaluation 4
EC 5XX Social Welfare & Policy 4
EC 5XX Institutional Economics 4
EC 5XX Core Elective 1 (from Track 2 elective menu) 4
EC 5XX Core Elective 2 (from Track 2 elective menu) 4

Students select two electives from their respective track menu. Machine Learning for Economics also appears in the Track 2 menu as an option.

Track 1 — Finance & Data Science Track 2 — Development & Policy
Derivatives and Risk Management Machine Learning for Economics
Big Data Analytics and Text Mining Labor Economics
Corporate Finance Agricultural Economics
Monetary Theory & Policy Health Economics

Note: Electives offered subject to availability of faculty and student interest.

Two-Year M.Sc. Program

Component Credits
Year 1 — Advanced Undergraduate (Semesters VII–VIII) 40
Semester VII 20
Semester VIII 20
Year 2 — M.Sc. Program (Semesters I–II) 40
M.Sc. Semester I — Common Foundation 22
M.Sc. Semester II — Specialised Track 20
Total Credits 82

Note: Students pursuing M.Sc. Economics with Research will have an additional 12 credits for the dissertation semester.

Eligibility

One-Year M.Sc. Two-Year M.Sc.
Degree BA / BSc Honours (4 years) BA / BSc / BCom / BBA / BCA / BTech
Disciplines Economics, Business Economics, Computer Science, Mathematics, Physics, Statistics Any discipline

Program Features

Flexible Entry, Shared Computational Foundation

Both pathways are structured so that all students arrive at the M.Sc. year with programming foundations already in place, acquired through AI and Computational Methods for Economists in Semester I. This ensures that AI tools, ML pipelines, and computational labs in subsequent courses are accessible to every student regardless of undergraduate background.

AI-Augmented Specialization Tracks

Both tracks are built around AI-augmented methods. Finance & Data Science students apply machine learning to financial econometrics, risk modelling, and algorithmic trading. Development & Policy students use causal ML and NLP for impact evaluation, policy text analysis, and welfare research. Machine Learning for Economics in Semester II is open to both tracks, ensuring no student is siloed from frontier computational tools.

Econometrics Redesigned for the AI Era

Econometric Methods in Semester I maps the boundary between structural models and machine learning. Students learn what traditional identification strategies achieve, when causal inference requires a structural approach, and precisely when and why ML methods — from Lasso regularization to double machine learning — are the appropriate next step. Theory first, computation in service of identification.

Frontier Research Tools, Grounded in Theory

Machine Learning for Economics gives students hands-on experience with double machine learning for causal inference, transformer-based NLP for text-as-data analysis, large language model APIs for automated literature review, and ML pipelines on Indian microdata from PLFS, NFHS-5, and RBI corpora. Every technique is taught in relation to what it can and cannot identify.

Specialisations Offered

  • Finance & Data Science
  • Development & Policy

Programme Structure

  • 1-Year Programme (for 4-year UG degree holders)
  • 2-Year Programme (for 3-year UG degree holders)

Specialisations Offered

  • Finance & Data Science
  • Development & Policy

Programme Structure

  • 1-Year Programme (for 4-year UG degree holders)
  • 2-Year Programme (for 3-year UG degree holders)

Career Opportunities

  • Economic & Policy Research
  • Banking & Financial Services
  • Data Analytics & Consulting
  • Government & International Organisations

Fee Structure (Per Year)

Category Tuition Fees (₹) Concession After Concession (₹)
A 1,00,000 100% 0
B 75,000 50% 37,500
C 50,000 30% 35,000
D 25,000 10% 22,500

Additional scholarships and financial aid options are available for eligible candidates.

Eligibility Criteria

For 2-Year M.Sc. Economics (3-Year UG Degree)

  • Eligible Degrees: BA / BSc / BCom / BBA / BTech
  • Must have studied Economics at UG level
  • Valid scores in:
  • CUET (PG) / CAT / GMAT
  • SRM AP Entrance Exam

For 1-Year M.Sc. Economics (4-Year UG Degree)

  • Eligible Degrees: BA / BSc
  • Preferred Background: Economics, Business Economics, Computer Science, Mathematics, Physics, Statistics
  • Valid scores in:
  • CUET (PG) / CAT / GMAT
  • SRM AP Entrance Exam

Admissions Open

Join a future-focused economics programme that combines theory, data, and technology to shape tomorrow’s leaders in policy, finance, and analytics.

Apply now to take the next step in your academic and professional journey.
Along with acquiring content knowledge, students in each course will practice critical thinking skills, communication skills, quantitative reasoning, and economic citizenry.
To prepare them to pursue higher studies and conduct research.
To train them and build their careers where they are likely to make a long-lasting contribution in either policy making or research career.
To solve real-life problems using economic theory and applications.
Analysis of data to solve complex economic problems.
Apply economic theories and concepts to contemporary social issues, as well as policy formulation and analysis.
Programme Outcomes (PO)
Scientific and Disciplinary Knowledge
Capable of demonstrating comprehensive knowledge and understanding of one or more disciplines that form a part of an undergraduate programme of study.
Ability to evaluate the reliability and relevance of evidence; Capacity to extrapolate from what one has learned and apply their competencies to solve different kinds of non-familiar problems, rather than replicate curriculum content knowledge; and apply one’s learning to real-life situations.
Capability to apply analytic thought to a body of knowledge; analyse and evaluate evidence, arguments, claims, and beliefs based on empirical evidence; identify relevant assumptions or implications; formulate coherent arguments; critically evaluate practices, policies and theories by following a scientific approach to knowledge development. Critical sensibility to lived experiences, with self-awareness and reflexivity of both self and society.
Ability to analyse, interpret and draw conclusions from quantitative/qualitative data; and evaluate ideas, evidence and experiences from an open-minded and reasoned perspective pertaining to incorporating into a system.
A sense of inquiry and capability for asking relevant/appropriate questions, problematising, synthesising, and articulating; Ability to recognise cause-and-effect relationships, define problems, formulate hypotheses, test hypotheses, analyse, interpret and draw conclusions from data, establish hypotheses, predict cause-and-effect relationships; ability to plan, execute and report the results of an experiment or investigation.
Capability to use ICT in a variety of learning situations; demonstrate the ability to access, evaluate, and use a variety of relevant information sources; and use appropriate software for analysis of data.
Understand the impact of scientific solutions in societal and environmental contexts, and demonstrate the knowledge of and need for sustainable development.
Possess knowledge of the values and beliefs of multiple cultures and a global perspective; and the capability to effectively engage in moral/ethical values in conducting one’s life, formulate a position/argument about an ethical issue from multiple perspectives, and use ethical practices in all work. Capable of demonstrating the ability to identify ethical issues related to one’s work. Avoid unethical behaviour such as fabrication, falsification or misrepresentation of data or committing plagiarism, not adhering to intellectual property rights; appreciating environmental and sustainability issues; and adopting objective, unbiased and truthful actions in all aspects of work.
Ability to work effectively and respectfully with diverse teams; facilitate cooperative or coordinated effort on the part of a group and act together as a group or a team in the interests of a common cause and work efficiently as a member of a team.
Ability to express thoughts and ideas effectively in writing and orally; Communicate with others using appropriate media to confidently share one’s views and express herself/himself; demonstrate the ability to listen carefully, read and write analytically, and present complex information clearly and concisely to different groups.
Capability for mapping out the tasks of a team or an organisation, setting direction, formulating an inspiring vision, building a team that can help achieve the vision, motivating and inspiring team members to engage with that vision, and using management skills to guide people to the right destination, in a smooth and efficient way.
Ability to work independently, identify appropriate resources required for a project, and manage a project through to completion; Ability to acquire knowledge and skills, including “learning how to learn”, that is necessary for participating in learning activities throughout life, through self-paced and self-directed learning aimed at personal development, meeting economic, social and cultural objectives, and adapting to changing trades and demands of the workplace through knowledge/skill development.

Curriculum

Admission India
International Admission

"The main aim of the Programme is to equip the students with theories and tools of economic analysis in major areas of its application and focus on the emerging area of Economics"