A BA Economics where the discipline itself does the heavy lifting — and six specializations amplify what graduates earn.
Financial Engineering & FinTech and Business Analytics feed roles paying ₹3.5–24 LPA at entry. Public Policy, Political Science and Spatial Economics pay ₹2.5–9 LPA at entry but reach strong ceilings when fused with analytics.
The biggest pay jump comes from analytics skill (SQL/Python/Power BI) + a credit-bearing internship + one global certification earned during the degree. That stack moves a graduate from the ₹3.5–5 LPA floor toward ₹8–15 LPA.
AI City, T-Hub 2.0, GCC clusters, a data-centre pipeline and APSAC/T-AIM create local demand across all six tracks. Embed regional internships and the NATS apprenticeship model to convert that demand into placements.
“Analytical thinking remains the most sought-after core skill… AI and big data top the list of fastest-growing skills.”
Implication for program design → every one of the six specializations must be wrapped around a common analytics spine.
Before any specialization, the degree itself has to earn its place. This is what BA Economics is worth on its own — the demand, the recruiters, the pay, the skills and the credentials of the economist track.
BA Economics is a quantitatively serious, widely-recruited degree. At bachelor’s level it opens the analyst funnel and the regulatory track; the titled economist path is unlocked by a Master’s. The numbers below set the floor everything else builds on.
The domains pulling for economics graduates — and the one shift that matters most for every track that follows.
Employer demand has shifted decisively toward “economist + data” hybrids — graduates who pair rigorous causal inference (econometrics) with the ability to actually handle data (Stata / R / Python). High-value postings increasingly list combinations like “Stata and R” or “Python and econometrics” together.
“Analytical thinking remains the most sought-after core skill… AI and big data top the list of fastest-growing skills.” — WEF Future of Jobs 2025
Causal inference fused with data engineering — the single strongest demand signal of 2024–26.
The core function of central banks, rating agencies, think tanks and KPOs.
RBI DEPR, bank economic-research desks, market strategy.
NIPFP, Finance Commission, state-finance analysis.
A notably rising area — NCAER runs a fast-growing climate-economics team; a distinct O*NET occupation.
NCAER, World Bank / ADB / UN India offices; impact and welfare research.
ICRIER’s core focus; trade policy, tariffs, global value chains.
Directorate of Economics & Statistics; ICRISAT / IWMI in Hyderabad.
Growing in academia and policy research; survey and experimental methods.
Pricing, market design and regulation for platforms, payments and the data economy — a fast-rising, high-pay frontier.
Material flows, EPR & recycling markets, resource efficiency — central to India’s net-zero and sustainability mandates.
The single most important truth for a BA Economics student to plan around: the bachelor’s opens the analyst funnel immediately, but the titled economist path runs through a Master’s. Two lanes, one decision.
Economic research analyst, credit analyst, market/industry research, KPO economic research, business economist. Entered directly after the BA — the broad, fast-growing funnel.
RBI DEPR, think-tank fellow, IMF / World Bank economist, IES, academia — almost universally require a Master’s minimum, a PhD at the senior research ceiling. The BA is the foundation, not the finish line.
Design implication → present the BA core honestly as a strong foundation and signalling credential that feeds both immediate analyst employment and competitive postgraduate study.
Distinct from the finance/analytics/consulting specialization roles that come later. India entry pay shown; ★ marks roles that effectively require a Master’s.
| Core role | India entry pay | Core skills screened |
|---|---|---|
| Economic research analyst / associate | ₹3.5–7 LPA | Macro/micro · data handling · economic writing · Excel |
| Credit analyst | ₹4–7 LPA (avg ~₹6.5 L) | Financial-statement analysis · risk · econometrics |
| Economic consultant | ₹6–10 LPA (Big 4) | Modelling · client communication · sector knowledge |
| Market / industry research analyst | ~₹4.4 LPA avg | Survey methods · data analysis · reporting |
| KPO economic-research analyst | ₹4–7 LPA | Research · data · English · domain reading |
| ESG / sustainability economics analyst | ₹5–9 LPA | Carbon accounting · climate data · economics |
| Economist (junior / assistant) ★ | ₹5–8 LPA | Econometrics · modelling · policy (MA normed) |
| Think-tank research associate ★ | ₹4–8 LPA | Stata/R · econometrics · academic writing (MA normed) |
| Government economic service (IES / RBI DEPR / DES) ★ | Pay-scale (see below) | Economics core · statistics · public finance (MA normed) |
O*NET (Economists 19-3011) confirms the core task set and screens for critical thinking, mathematics, reading comprehension and writing — with SPSS, SAS, MATLAB, SQL, Python and R as the hot technologies.
Employers that hire economics graduates for genuinely economics-led work — government, research, ratings, multilaterals — with the Hyderabad / AP–Telangana presence flagged.
Indian entry pay is strongly bimodal: a generalist cluster and a higher data-literate / regulatory cluster. Below it, the government scales and the global benchmarks.
Basic ₹78,450 (2026 revision); ~₹40–42 L CTC in metros. The DEPR stream recruits economists directly. Needs a Master’s in Economics.
Pay Matrix Level 10; gross ₹80k–1 L with allowances, rising to ~₹2.25 L at senior levels. Headed by the Chief Economic Adviser. Needs a Master’s.
ASO (mandal) up to Director on fixed state pay scales. Telangana DES alone employs ~921 statisticians and economists.
The non-negotiable core that travels across every specialization — and the add-ons that belong to the tracks, not the foundation.
The market signal is unambiguous: ICRIER, NCAER and the rating agencies all list Stata / R proficiency as a requirement. Econometrics + one package is the highest-ROI thing a BA student can master.
Only credentials employers actually recognise — industry-body (CFA Institute, GARP, INFORMS, SEBI/NISM) and OEM/vendor (Microsoft, Bloomberg) certifications. MOOCs and course-completion certificates are excluded: they are training, not credentials.
| Certification | Issuing body | Type | UG fit |
|---|---|---|---|
| CFA Program — Level I | CFA Institute | Industry body | Final-year UG · demanding |
| FRM — Part I | GARP | Industry body | Yr 3–4 · risk-focused |
| Bloomberg Market Concepts (BMC) | Bloomberg | OEM / vendor | Yr 1–2 · markets data |
| Power BI Data Analyst (PL-300) | Microsoft | OEM / vendor | Yr 2–3 · the recognised BI cert |
| Certified Analytics Professional (CAP) | INFORMS | Industry body | Yr 4 / PG · analytics |
| NISM Series V-A & XV (Research Analyst) | NISM · SEBI | Industry-regulatory | Yr 1–3 · Indian markets |
Not certifications — Indian government recruitment pathways: UGC-NET Economics (JRF), RBI Grade B (DEPR) and the Indian Economic Service are post-Master’s competitive exams that gate economist roles. They are recruitment gateways, valuable but distinct from the global industry/OEM certifications above.
The core sets the floor; specializations raise the ceiling. Each track below bolts a paid, in-demand capability onto the economics foundation — and the highest pay comes from the combination, never the label alone.
Local, regional, national and global needs mapped to the specialization that serves them. AI/analytics is the connective tissue: a specialization fused with analytics out-earns the same specialization in isolation.
| Thrust area | Principal drivers | Lead specialization(s) | Pay signal |
|---|---|---|---|
| AI & Big Data analytics | WEF #1 skill; India data-analytics market USD 2.6B (2024) → 27.0B (2033) @ 27.46% CAGR (IMARC) | Business Analytics · Financial Engineering & FinTech | Highest volume + fast hikes |
| FinTech & capital markets | RBI / SEBI, GIFT City, digital payments, bulge-bracket expansion | Financial Engineering & FinTech | Highest ceiling |
| Corporate strategy & transformation | GCC strategy units, Big 4 advisory, digital/AI transformation mandates | Management & Strategy Consulting · Business Analytics | High pay + broad exits |
| Public-policy data / governance | NITI Aayog, Digital India, MeitY data governance | Public Policy + Analytics | Moderate, rising |
| Digital & platform economy | Platform regulation (DPDP Act, Digital Competition Bill), the data economy, network markets, e-commerce & gig platforms | Business Analytics · Financial Engineering & FinTech | High & fast-growing |
| Spatial / location intelligence | National Geospatial Policy 2022, Operation Dronagiri (AP pilot), retail & logistics location analytics, Digital Twins | Spatial Economics & Location Intelligence | Lowest floor, strong growth |
| Circular economy & sustainability | Extended Producer Responsibility rules, resource-efficiency & recycling markets, carbon markets; WEF “environmental stewardship” top-10 skill | Public Policy · Financial Engineering & FinTech | ₹9 LPA+ (CRISIL ESG) |
| Political intelligence / public affairs | India’s electoral-consulting industry | Political Science | I-PAC ₹6.7–15.6 LPA |
| GCC / data-centre economy (regional) | AP IT/GCC Policy 4.0 (2024–29), Telangana AI City, Hyderabad data centres | Analytics · Finance | Strong regional demand |
Pattern → the highest pay flows to Financial Engineering & FinTech (ceiling) and Business Analytics (volume + velocity). AI/analytics is the connective tissue across every row.
Ranked for bachelor’s-level pay and employability. The order carries meaning — it is the recommended phasing priority if the program launches tracks in stages.
Velocity bars = relative pay-growth speed in early career. Financial Engineering & FinTech has the ceiling; Business Analytics has the fastest fresher-volume hikes; Management & Strategy Consulting offers the broadest exit options.
Every track shares an Economics core (econometrics, STATA/R/Python, Excel, data storytelling). Beyond it, each specialization opens a distinct role set with its own skill screen.
Nearly all six tracks are placeable from the region: Big 4, analytics firms, spatial-analytics majors, finance GCCs, political consulting and tech all recruit locally.
Floor-to-ceiling ranges, 2024–26. Bars read left (floor) to right (ceiling). Bangalore and Hyderabad typically pay 15–18% above the national analytics average.
US first-year IB analyst base ~$100,000–125,000 (NYC) · London ~£60,000. These define the global ceiling and justify certifications that travel.
Indicative pay trajectories from entry to senior across six role families. Tap a legend item to isolate a curve. Finance has the steepest ascent; analytics has the fastest early velocity.
NEP 2020 makes work experience part of the degree, not an extracurricular. The apprenticeship/internship-embedded model lets students bank employer-valued experience while earning credits and a government stipend.
Three mandatory experiential components: a 4-week NGO program (Yr 1), a 4-credit, 8-week Summer Internship (Yr 2), an industry internship (Yr 3) and a 4th-year Interdisciplinary Major Project. Hosts include Deloitte, PwC, Aditya Birla Capital, RBL Bank.
Credit-bearing internships (2–4 credits) counting toward CGPA; a 2-month summer internship at exit points; a 12-credit final-year research project.
The 4-year program totals 150 credits including 2–4 internship credits, fulfillable via summer research or global programs.
B.Sc. Economics (Hons / Hons with Research): a Yr-2 vocational diploma in Fintech + Taxation, plus a foreign-university exchange / dual degree (University of Bristol).
Ranked No. 1 for co-op (U.S. News 2024). 6-month full-time paid placements, a network of 3,800+ employers across 149 countries, and 93% of graduates employed or in further study nine months out.
GIS lab · policy lab · financial-modeling / Bloomberg simulation · analytics practicum · faculty & think-tank research assistantships · T-Hub / Startup India incubation · live consulting capstones · a mandatory 8–10 week summer internship (Yr 2) · semester-long Yr-4 immersion. Register cohorts under NATS for stipend-bearing apprenticeships.
Strictly industry-body and OEM/vendor certifications — the kind that clear hiring filters and lift pay. No MOOCs or course-completion certificates. Each is mapped to the track it serves and sequenced so signals compound year over year.
| Certification | Issuing body | Type | Track | UG fit |
|---|---|---|---|---|
| Bloomberg Market Concepts (BMC) | Bloomberg | OEM | Fin. Eng. & FinTech | Yr 1–2 · easy |
| FMVA — Financial Modeling & Valuation | Corporate Finance Institute | Industry | Fin. Eng. & FinTech | Yr 3 · moderate |
| CFA Program — Level I | CFA Institute | Industry body | Fin. Eng. & FinTech | Yr 3–4 · demanding |
| FRM — Part I | GARP | Industry body | Fin. Eng. & FinTech | Yr 3–4 · risk |
| NISM Series V-A / XV | NISM · SEBI | Industry-regulatory | Fin. Eng. & FinTech | Yr 1–3 · accessible |
| Power BI Data Analyst (PL-300) | Microsoft | OEM | Business Analytics | Yr 2–3 · recognised BI cert |
| Tableau Certified Data Analyst | Tableau (Salesforce) | OEM | Business Analytics | Yr 2–3 · moderate |
| Azure Data Scientist (DP-100) | Microsoft | OEM | Business Analytics | Yr 3–4 · advanced |
| Certified Data Engineer – Associate | Amazon Web Services | OEM | Business Analytics | Yr 4 / PG · advanced |
| Certified Analytics Professional (CAP) | INFORMS | Industry body | Business Analytics | Yr 4 / PG |
| Machine Learning – Specialty | Amazon Web Services | OEM | AI · cross-cutting | Yr 4 / PG · advanced |
| Azure AI Engineer (AI-102) | Microsoft | OEM | AI · cross-cutting | Yr 3–4 · moderate |
| Generative AI Engineer — Associate | Databricks | OEM | AI · cross-cutting | Yr 4 / PG |
| Esri ArcGIS Technical Certification | Esri | OEM | Spatial Economics | Yr 2–3 · moderate |
| GISP | GIS Certification Institute | Industry body | Spatial Economics | post-experience |
| Certificate in ESG Investing | CFA Institute | Industry body | ESG · cross-cutting | Yr 3–4 · moderate |
Also cross-cutting → Microsoft Office Specialist: Excel Expert (MO-201) in Year 1, GARP SCR (Sustainability & Climate Risk) for the ESG/climate path, and PMI CAPM for policy/program roles — all OEM or industry-body, none MOOCs.
Specialization × skill × certification × experience. These are the bachelor’s-level stacks that reach the highest offers.
Everything above, made concrete: a 34-course trimester architecture that sequences the core, the quant and technology spine, the chosen specialization, a two-stage capstone — and a per-trimester engine of team projects, expert sessions and placement CRT — into a single buildable 120-credit program, extendable to a 160-credit four-year Honours-with-Research degree.
Six course families across nine trimesters — T01–T07 carry four courses each, T08–T09 carry three. The open electives were removed and reinvested into a research-grade economics and methods spine. The cards below show course counts; the ribbon shows the credit weight that adds to the 120-credit three-year total.
The three-year program is 120 credits across 34 courses — economics core and methods at 4 credits each (48 + 28), the capstone at 4 (8), technology at 3 (15) and language at 2 (6), and the specialization at the BBA's own credits — its co-taught professional electives at 3 and the analytics BI anchor at 4 (15–16 cr). A fourth honours-with-research year (semester mode) adds 40 credits — a second specialization (15) plus a research dissertation or industry internship (25) — for a 160-credit four-year honours degree. Two mandatory summer internships sit between the years (see the map below).
Foundations first, methods and technology through the middle, the specialization running T06–T08, and the capstone closing the program. Colours map to the course families above; each card also shows that trimester’s out-of-class project and CRT focus (detailed later in Beyond the Classroom and the Project Playbook).
Exploratory and broad — students consolidate the Year-1 foundation in data, statistics and fieldwork inside a real organisation, and use the exposure to choose their specialization. Hosts: research institutes, think tanks, government statistics offices, NGOs, and market-research / analytics startups.
Audit · 0 cr · 2 CH/wkUniversal Human Values, Professional Ethics & Gender Inclusion — a non-credit seminar on values, ethical professional conduct and gender inclusion, recorded on the transcript.
Track-aligned and professional — a placement-oriented internship in the student’s chosen specialization, with pre-placement-offer (PPO) potential, feeding directly into the T07 agentic-AI project and the capstone. Hosts by track: fintech / banks (FE), analytics & data-science teams (BA), think tanks & policy orgs (PP), GIS / location-intelligence firms (SE), public-affairs / political-consulting (PS).
Audit · 0 cr · 2 CH/wkIndian Knowledge Systems for Economics — a non-credit seminar on India’s classical economic thought (Arthaśāstra, traditional accounting, guild & trade systems) and its bearing on modern economics, recorded on the transcript.
The optional fourth year runs in semester mode. Continuing students take a second specialization from the same six tracks (5 courses · 15 credits, BBA-synced) and a 25-credit research dissertation or industry internship (10 cr + 15 cr across the two semesters) — graduating with a 160-credit, four-year Bachelor’s (Honours with Research).
The specialization is scheduled to the BBA's timetable so the classes run together. All five courses now sit inside the BBA's specialization window on one identical schedule — two in T06, two in T07, one in T08 (2 + 2 + 1) — every course co-taught with the BBA cohort. The two cores the BBA taught outside that window were swapped for in-window courses — analytics BANA → BIDV (a BBA BI core in T06) and consulting STMG → AIPS (the BBA's AI-strategy elective in T06) — so every track now opens with two T06 courses, then two in T07 and one in T08. The §D·5 cards list every track's five courses with its co-taught trimester. The three economics-only tracks (Public Policy, Spatial Economics, Political Science) run on the same timetable within the BA programme.
Courses are only half the design. From T01 to T07 each trimester also carries a team-based flagship project, expert sessions, and placement-grade CRT — all synced to what students are learning that term, so theory compounds into an employer-facing portfolio and interview-ready skills.
Every project is a team of 3–4 and is cumulative — each one re-uses the economics it builds on, so concepts from T01 keep resurfacing through T07. CRT runs ≥6 hrs each trimester (≥42 hrs total) and is sequenced to the term’s courses, so quant, reasoning, verbal and technical-interview skills mature in lockstep with the economics. T08–T09 then convert all of it into the two-stage capstone and live placement drives.
For T01–T07, each trimester-wide team project is defined the way an employer scopes work: a precise brief, an expected outcome, concrete deliverables (including a team video log told as a story), a portfolio build, and a weighted rubric. All are team efforts of 3–4, run across the 11-week trimester (~4 hrs/week), and are cumulative — each re-uses the economics of the ones before it.
Every project ends as a portfolio asset. Across T01–T07 a student accumulates seven version-controlled repositories and seven polished portfolio entries — a documented arc from field data-collection (T01) through econometrics (T04), causal ML (T05), optimization (T06) and a shipped agentic-AI tool (T07). By graduation that becomes a GitHub portfolio, a personal project site and a LinkedIn trail evidencing exactly the skills high-paying employers screen for — what students carry into placement interviews instead of just a transcript. Every project also ships a team video log, so graduates leave with a seven-part video showreel that narrates their best work — a storytelling asset few candidates bring to an interview.
Each criterion below is scored on this 4-level scale and combined by its weight into a final mark. A shared rubric scale keeps grading consistent across all seven projects while the criteria stay project-specific.
Each team selects eight everyday goods or services traded in distinct local micro-markets around Vijayawada (e.g. a vegetable in the Rythu Bazaar vs a supermarket, auto-rickshaw fares on different routes, PG/hostel rents, second-hand textbooks, mobile-data plans, street-food items). They design a simple price-and-quantity observation protocol with consent, collect real data across locations and times, estimate the price elasticity of demand for each, identify substitutes and complements, and explain the anomalies they find — price dispersion, markups, segmentation — using consumer and producer theory. The analysis is built in a Python “price atlas”: a cleaned dataset plus demand/supply visualisations.
Students translate textbook micro (demand, supply, elasticity, market structure) into a real, messy local market, and prove they can collect, clean and visualise economic data in Python while writing a clear analytical narrative.
| Criterion | Weight |
|---|---|
| Economic reasoning — micro theory explains the findings | 25% |
| Data collection & cleaning — protocol, ethics, reproducibility | 20% |
| Python analysis & visualisation — correct elasticity, honest charts | 20% |
| Written communication — clarity and structure | 10% |
| Video log & storytelling — narrative arc, clarity, visual craft | 15% |
| Teamwork & process | 10% |
Teams build a refreshable dashboard tracking ten core macro indicators from RBI / MoSPI / CMIE (GDP growth, CPI inflation, IIP, repo rate, fiscal deficit, current-account balance, forex reserves, unemployment, exchange rate, bank-credit growth). They build a reproducible pipeline that pulls and updates the data, compute descriptive statistics (trend, volatility, YoY/MoM change) and simple probability-based risk flags (e.g. the probability inflation breaches the RBI 6% upper tolerance band given the recent distribution), and write a monthly “State of the Economy” report interpreting the signals for a non-economist reader.
Genuine macro literacy — what each indicator means and how they interact — fused with applied descriptive statistics and probability, packaged as a recruiter-impressive data product.
| Criterion | Weight |
|---|---|
| Macro literacy & interpretation — indicators and their interactions | 25% |
| Statistical rigour — descriptive stats + valid probability flags | 20% |
| Data pipeline & reproducibility | 20% |
| Dashboard & report communication — clarity for a lay reader | 10% |
| Video log & storytelling — narrative arc, clarity, visual craft | 15% |
| Teamwork & process | 10% |
Each team designs and runs a multi-round strategic game experiment with 30+ participants from the cohort. They pick and formalise a game (sealed-bid auction, Cournot/Bertrand oligopoly, public-goods, ultimatum, beauty contest), specify payoffs and the theoretical (Nash / subgame-perfect) prediction, run multiple rounds recording every decision, then use inferential statistics (hypothesis tests, confidence intervals) to test whether observed behaviour matches the equilibrium. They explain the deviations — bounded rationality, fairness, learning — and stage a live demonstration round.
Students move from game-theory theory to empirical test: designing an incentivised experiment, collecting behavioural data, and using inferential statistics to compare observed play against equilibrium — then communicating it persuasively.
| Criterion | Weight |
|---|---|
| Game-theoretic rigour — formalisation and equilibrium derivation | 25% |
| Experimental design — incentives, controls, ethics | 15% |
| Inferential statistics — valid tests, correct interpretation | 20% |
| Behavioural explanation of deviations | 15% |
| Video log & storytelling — narrative arc, clarity, visual craft | 15% |
| Teamwork & process | 10% |
Teams pose one causal/empirical question about the Indian economy answerable with public data — does monsoon rainfall drive rural wages? does the repo rate move auto sales? does female literacy affect district fertility? They frame a hypothesis grounded in theory, assemble a dataset from public sources (RBI, MoSPI, data.gov.in, PLFS, district handbooks), run their first real multiple regression interpreting coefficients, significance and fit, then discuss identification limits — omitted variables, reverse causality — honestly, and present a data-story with a clear takeaway.
Students produce their first genuine empirical economics paper: a theory-motivated regression on real Indian data, interpreted carefully with explicit attention to what can and cannot be claimed causally.
| Criterion | Weight |
|---|---|
| Question framing & economic motivation | 20% |
| Data assembly & quality | 15% |
| Econometric execution — correct model, diagnostics, interpretation | 30% |
| Honesty about identification & limitations | 10% |
| Video log & storytelling — the data-story told well | 15% |
| Teamwork & process | 10% |
Teams take a public development dataset (MGNREGA outcomes, school dropout, SECC poverty, child nutrition, health-facility access) and do two things that are often confused: predict an outcome with ML (random forest, gradient boosting) evaluated honestly (train/test split, appropriate metrics), and explain a driver using causal methods from Econometrics II (panel / difference-in-differences / IV, or a careful selection-on-observables design) plus interpretability tools (SHAP). They translate the result into a policy brief recommending an actionable intervention, and explicitly distinguish prediction from causation.
Students internalise the crucial distinction between prediction and causal inference, build and validate an ML model, and convert technical results into a development-policy recommendation.
| Criterion | Weight |
|---|---|
| ML execution & validation — no leakage, right metrics | 25% |
| Causal reasoning — identification and interpretation | 20% |
| Prediction-vs-causation distinction — explicit and correct | 15% |
| Policy translation — actionable, evidence-based brief | 15% |
| Video log & storytelling — narrative arc, clarity, visual craft | 15% |
| Communication & reproducibility — model card, repo | 10% |
Teams take a real allocation problem — a circular-economy subsidy (Extended Producer Responsibility / recycling incentives), a carbon tax, a welfare-transfer targeting scheme, or a district budget — and model it as a constrained optimization. They define the objective (welfare / efficiency / coverage) and constraints (budget, equity floors, behavioural responses), formalise and solve it with Advanced Optimization methods (Lagrangian / KKT, linear or convex programming), run sensitivity and scenario analysis on key parameters, interpret the trade-offs (efficiency vs equity, deadweight loss), and defend the welfare-maximising design before a panel.
Students apply advanced optimization to a genuine public-finance and circular-economy design problem, quantify the trade-offs, and defend a policy design with mathematical and economic rigour.
| Criterion | Weight |
|---|---|
| Problem formulation — objective and constraints correctly specified | 25% |
| Optimization execution — correct method and solution | 25% |
| Economic interpretation — trade-offs, welfare, incidence | 15% |
| Sensitivity & robustness | 10% |
| Video log & storytelling — narrative arc, clarity, visual craft | 15% |
| Defence & teamwork | 10% |
Teams build a working GenAI / agentic-AI tool that performs a real economics task end-to-end — a trade-policy analyzer (ingests tariff schedules + trade data, summarises exposure), a market-research agent (gathers and synthesises sector/company intelligence), a policy-brief generator, or a financial-statement / risk analyzer. They scope a concrete workflow and the user it serves, design an agentic pipeline (retrieval, tools, reasoning steps) fusing their specialization domain with trade/finance content, build and test it with real inputs, evaluate quality and failure modes honestly (hallucination, bias, guardrails), and demo it. This project directly seeds the T08–T09 capstone.
Students ship a contemporary, portfolio-defining AI tool grounded in real economics — proof they can combine domain economics, their specialization and agentic AI into something an employer can see working.
| Criterion | Weight |
|---|---|
| Economics grounding — a real, valuable workflow correctly modelled | 25% |
| Technical build — working agentic pipeline, sound design | 25% |
| Specialization + trade/finance integration | 15% |
| Evaluation & responsible-AI awareness — failure modes, guardrails | 10% |
| Video log & storytelling — the demo told as a story | 15% |
| Documentation & portfolio readiness | 10% |
T08–T09 do not add new out-of-class projects — the two-stage capstone becomes the flagship team project, building directly on the T07 agentic-AI tool, and runs in parallel with live placement drives.
The same six specializations ranked earlier, now mapped to their actual courses. Three are co-taught with the KL BBA (identical professional-elective courses); three are BA-exclusive economics tracks. All lean on the shared quant and technology spine.
Credits · LTPS · contact hours — identical to the BBA. Every specialization course carries the BBA's exact LTPS, contact hours and credits, with no BA adjustment — a BA student earns precisely what a BBA student earns for the same shared class. Each co-taught professional elective is 2-1-3-0 · CH 6 · 3 cr; BIDV is 2-1-6-0 · CH 9 · 4 cr. The specialization block is therefore 15 credits for the all-elective tracks (five 3-credit courses) and 16 for Business Analytics — its BIDV anchor being a 4-credit BBA core — bringing the three-year programme to 120 credits (121 for Business Analytics).
Credits follow the BBA rule exactly — L+T 3h = 2cr · P 3h = 1cr · S 6h = 1cr — under a 27-hour weekly contact ceiling, with one practice-led 2-1-6-0 course per trimester and 3-3-0-0 reserved for the five theory-and-tutorial economics courses. No course is pure lecture; the foreign language is synced to the BBA; internships and capstones are practical / skill.
| Code | Course | LTPS | CH | Cr |
|---|---|---|---|---|
| T01 · Year 1 | ||||
| ECO-101 | Microeconomics | 3-3-0-0 | 6 | 4 |
| QTM-101 | Calculus & Linear Algebra | 2-1-3-0 | 6 | 3 |
| TEC-101 | Programming (Python & R) | 2-1-6-0 | 9 | 4 |
| LNG-101 | Academic & Research Writing | 2-1-3-0 | 6 | 3 |
| T01 subtotal | 27 | 14 | ||
| T02 · Year 1 | ||||
| ECO-102 | Macroeconomics | 3-3-0-0 | 6 | 4 |
| QTM-102 | Optimization & Dynamics | 2-1-3-0 | 6 | 3 |
| QTM-201 | Statistics I | 2-1-6-0 | 9 | 4 |
| LNG-102 | Foreign Language Elective (German / Japanese / Korean) | 2-1-0-0 | 3 | 2 |
| T02 subtotal | 24 | 13 | ||
| T03 · Year 1 | ||||
| ECO-201 | Microeconomic Theory & Market Structures | 3-3-0-0 | 6 | 4 |
| ECO-203 | Game Theory | 2-1-3-0 | 6 | 3 |
| QTM-202 | Statistics II | 2-1-6-0 | 9 | 4 |
| LNG-201 | Professional Communication | 2-1-3-0 | 6 | 3 |
| T03 subtotal | 27 | 14 | ||
| T04 · Year 2 | ||||
| ECO-202 | Macroeconomic Theory & Monetary Economics | 3-3-0-0 | 6 | 4 |
| QTM-301 | Econometrics I | 2-1-6-0 | 9 | 4 |
| ECO-204 | Indian Economy | 2-1-3-0 | 6 | 3 |
| TEC-201 | Data Science & Visualization | 2-1-3-0 | 6 | 3 |
| T04 subtotal | 27 | 14 | ||
| T05 · Year 2 | ||||
| QTM-302 | Econometrics II | 2-1-6-0 | 9 | 4 |
| TEC-301 | Machine Learning | 2-1-3-0 | 6 | 3 |
| ECO-301 | Development Economics | 2-1-3-0 | 6 | 3 |
| ECO-304 | Behavioural & Experimental | 2-1-3-0 | 6 | 3 |
| T05 subtotal | 27 | 13 | ||
| T06 · Year 2 | ||||
| SPL·1 | Specialization 1 — co-taught PE | 2-1-3-0 | 6 | 3 |
| SPL·2 | Specialization 2 — co-taught PE | 2-1-3-0 | 6 | 3 |
| QTM-303 | Advanced Optimization | 2-1-6-0 | 9 | 4 |
| ECO-303 | Public Economics | 3-3-0-0 | 6 | 4 |
| T06 subtotal | 27 | 14 | ||
| T07 · Year 3 | ||||
| SPL·3 | Specialization 3 — co-taught PE | 2-1-3-0 | 6 | 3 |
| SPL·4 | Specialization 4 — co-taught PE | 2-1-3-0 | 6 | 3 |
| ECO-302 | International Trade & Finance | 2-1-3-0 | 6 | 3 |
| TEC-302 | Generative & Agentic AI | 2-1-6-0 | 9 | 4 |
| T07 subtotal | 27 | 13 | ||
| T08 · Year 3 | ||||
| SPL·5 | Specialization 5 — co-taught PE | 2-1-3-0 | 6 | 3 |
| ECO-305 | Environmental & Circular Economy | 2-1-6-0 | 9 | 4 |
| CAP-401 | Capstone I — Research Design | 0-0-6-6 | 12 | 3 |
| T08 subtotal | 27 | 10 | ||
| T09 · Year 3 | ||||
| ECO-306 | Digital & Data Economics | 2-1-6-0 | 9 | 4 |
| TEC-303 | Blockchain & DeFi | 2-1-3-0 | 6 | 3 |
| CAP-402 | Capstone II — Implementation | 0-0-12-0 | 12 | 4 |
| T09 subtotal | 27 | 11 | ||
| Summer modules (outside weekly cap) | ||||
| INT-1 | Summer Internship — Foundational | 0-0-0-12 | 12 | 2 |
| AUD-1 | Universal Human Values, Ethics & Gender Inclusion — audit | 1-1-0-0 | 2 | 0 |
| INT-2 | Summer Internship — Specialization | 0-0-0-12 | 12 | 2 |
| AUD-2 | Indian Knowledge Systems for Economics — audit | 1-1-0-0 | 2 | 0 |
| 3-year total (T01–T09 + 2 summer) | — | 120 | ||
| Year 4 · Honours with Research (optional, +40) | ||||
| SPL²·I–V | Second specialization — 5 co-taught PEs | 2-1-3-0 ×5 | 30 | 15 |
| RDI-701 | Research Dissertation / Industry Internship — Phase I | P / S block | FT | 10 |
| RDI-702 | Research Dissertation / Industry Internship — Phase II | P / S block | FT | 15 |
| 4-year honours total | — | 160 | ||
The arithmetic. Nine trimesters sum to 116 credits (14·13·14·14·13·14·13·10·11), plus 4 from the two summer internships → a 120-credit three-year programme. Every trimester sits at 24–27 contact hours, never above 27. The optional honours year adds a second specialization (15) and a 25-credit research/internship block (10 + 15) → a 160-credit four-year degree. The Business Analytics track is identical in total: its 4-credit BIDV anchor simply takes the T06 practice-led slot in place of QTM-303. Two non-credit audit seminars sit in the summers — Universal Human Values, Ethics & Gender Inclusion (Summer 1) and Indian Knowledge Systems for Economics (Summer 2), each 2 CH/week and transcript-recorded; SDGs and environmental economics are carried by ECO-305 and threaded across the core, so they need no separate slot.
The technology spine stays at the frontier: Generative AI & Agentic AI, Blockchain & DeFi, Machine Learning, Data Science and Programming — paired with contemporary core courses in Behavioural, Environmental, Climate & Circular Economy and Digital, Platform & Data Economics.
Seven sequenced methods courses — three mathematical-economics (calculus & linear algebra → optimization & dynamic systems → advanced optimization), two statistics and two econometrics — match the deepest peer programs course-for-course and lift the rigour ceiling of every theory and field course downstream.
Two English courses are economist-specific — research writing and data storytelling — and a foreign language (French / German / Mandarin) opens multilateral and global-trade roles at the IMF, World Bank and OECD.
The Business Analytics specialization is fully shareable across BA, B.Com and BBA, taught by a common faculty pool — the highest-ROI, most transferable track and the regional employment magnet, delivered at faculty efficiency.
The three open electives were retired and reinvested into economics: a standalone Game Theory course and a full three-course mathematical-economics sequence. Micro now runs Principles → Theory → Game Theory and macro Principles → Theory & Monetary — the depth a serious economics degree demands.
Capstone I (T08) scopes a real research or industry problem; Capstone II (T09) delivers the analysis and dissertation — converting three years of method and specialization into an employer-facing artefact.
Digital economics runs the length of the program: a dedicated Digital, Platform & Data Economics core course, the Financial Engineering & FinTech track (digital payments, DeFi, algorithmic finance), and the technology spine (Data Science, Machine Learning, Blockchain, Generative & Agentic AI) — with platform pricing and the data economy surfacing again in the T02 and T07 projects. Circular economy is anchored in the Environmental, Climate & Circular Economy core course and extended through Public Economics (EPR, carbon & recycling markets), Development and the T06 public-finance design sprint — so both themes are taught as named courses and reinforced as recurring threads across theory, technology and the experiential layer.
120 credits over three years across 34 courses — economics core & methods at 4 cr (48 + 28), the capstone at 4 (8), technology at 3 (15), language at 2 (6), and the specialization at the BBA's own credits (15–16 cr). Two mandatory summer internships (Foundational Data & Research after Year 1; Specialization Industry after Year 2). An optional fourth honours-with-research year (semester mode) adds 40 credits — a second specialization (15) + research dissertation or industry internship (25) — for 160 credits total. Economics + methods = 19 of 34 courses.
Build BA Economics (Honours) on the 120-credit three-year core (extendable to a 160-credit, four-year Honours-with-Research exit via a second specialization + dissertation/internship). Embed a common analytics spine (data tools, Yrs 1–3) into every track — the single largest pay lever — and anchor employability with two summer internships and the per-trimester project + CRT engine. Make Business Analytics a shared minor across BA/B.Com/BBA for faculty efficiency.
If phasing, launch Financial Engineering & FinTech and Business Analytics first — highest pay, highest employability, clearest certification ladders. Then Public Policy and Management & Strategy Consulting, then Spatial Economics and Political Science as differentiators. Spatial Economics needs lab investment (ArcGIS/QGIS) but has the strongest AP/Telangana institutional pull (APSAC, Cyient, Genesys, Operation Dronagiri).
Adopt the FLAME staggered model: community engagement (Yr 1), a mandatory 4-credit 8–10 week summer internship (Yr 2), industry/research immersion (Yr 3–4). Build a GIS lab, policy lab, financial-modeling / Bloomberg simulation and analytics practicum. Sign MoUs with regional recruiters (Big 4 Hyderabad, Fractal/Tiger, Cyient, CRISIL GCC, I-PAC) and register cohorts on NATS for stipend-bearing apprenticeships.
Yr 1: Bloomberg Market Concepts + Microsoft Excel Expert (MO-201) + NISM V-A. Yr 2: Power BI Data Analyst (PL-300) + Tableau Certified Data Analyst + Esri ArcGIS + NISM VIII. Yr 3: CFI FMVA + Azure Data Scientist (DP-100) + NISM XV (Research Analyst) + CFA ESG Investing. Final year: CFA Program Level I / FRM Part I / AWS ML – Specialty + INFORMS CAP.