NEP 2020 · Four-Year UG Honours · Strategic Dossier

Built on a rigorous core.
Engineered for earning power.

A BA Economics where the discipline itself does the heavy lifting — and six specializations amplify what graduates earn.

Regional lens: Andhra Pradesh & Telangana → Pan-India → Global · Figures 2024–26 · INR LPA distinguished from USD/GBP throughout.
The Earnings Engine ₹ LPA · log scale
₹1cr+
Senior pay ceiling
(Finance / IB → MD)
+78M
Net new jobs by 2030
(WEF Future of Jobs 2025)
27%
India analytics-market CAGR
USD 2.6B → 27.0B by 2033
160–176
FYUGP credit chassis
for the Honours degree
Explore the dossier

Three interactive views.

Infographic
The Program Stack
Every tool, framework, active-learning method and skill embedded in the degree — with live statistics from 73 courses and 434 mapped outcomes.
Open →
12 sample CVs
Graduate Resumes
A sample resume for each flagship role across all six specializations — core skills, tools, certifications, portfolio and target pay.
Open →
NBA-aligned
Outcomes Framework
Vision, Mission, the 4 PEOs, 12 POs and 3 PSOs, with the CO–PO articulation matrix that runs through every course.
Open →
00
The Core Argument

Three findings that shape every other decision.

Finding 01

Two tracks lead on pay

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.

Finding 02

The lever isn't the major — it's the stack

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.

Finding 03

Hyderabad is the launchpad

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.

Macro demand signal · WEF Future of Jobs Report 2025

The market is pulling hard toward analytical, data-literate graduates.

“Analytical thinking remains the most sought-after core skill… AI and big data top the list of fastest-growing skills.”
1,000+
leading employers surveyed across 22 industry clusters & 55 economies
170M
new roles created by 2030 — led by Big Data, FinTech, AI/ML specialists
7 in 10
companies call analytical thinking an essential 2025 skill
14M
workers represented in the survey base underpinning the projection

Implication for program design → every one of the six specializations must be wrapped around a common analytics spine.

I
Movement One · The Foundation

The economics core.

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.

C·0
The Core Value Proposition

A rigorous, respected foundation — honest about its ceiling.

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.

Core anchors · 2024–26 · BLS · AmbitionBox / Glassdoor / Indeed · RBI

What the economics degree is worth, before specialization.

₹3–6 LPA
India fresher band; ₹4–9 LPA at top employers & metros
$115,440
US economist median wage (BLS, May 2024); 10th–90th pct $62,340–$212,710
₹1.5 L/mo
RBI Grade B officer gross start (~₹40–42 L CTC) — the regulatory track
~1%
US economist-title growth 2024–34 (~900 openings/yr) — narrow at the title, broad at analyst level
C·1
Core · Demand Drivers

Where the discipline itself is in demand.

The domains pulling for economics graduates — and the one shift that matters most for every track that follows.

The dominant signal · 2026

The “one-tool” economist is becoming an endangered species.

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

Economist + data hybrids

Causal inference fused with data engineering — the single strongest demand signal of 2024–26.

Highest bachelor-accessible pay

Economic research & forecasting

The core function of central banks, rating agencies, think tanks and KPOs.

Moderate–strong

Monetary & financial economics

RBI DEPR, bank economic-research desks, market strategy.

Strong

Public finance & fiscal policy

NIPFP, Finance Commission, state-finance analysis.

Moderate–strong

Climate & environmental economics

A notably rising area — NCAER runs a fast-growing climate-economics team; a distinct O*NET occupation.

Rising

Development economics

NCAER, World Bank / ADB / UN India offices; impact and welfare research.

Stable

International trade economics

ICRIER’s core focus; trade policy, tariffs, global value chains.

Stable

Agricultural & resource economics

Directorate of Economics & Statistics; ICRISAT / IWMI in Hyderabad.

Stable · regional fit

Labour · health · behavioural

Growing in academia and policy research; survey and experimental methods.

Academic-tilted

Digital & platform economics

Pricing, market design and regulation for platforms, payments and the data economy — a fast-rising, high-pay frontier.

Strong & rising

Circular & resource economics

Material flows, EPR & recycling markets, resource efficiency — central to India’s net-zero and sustainability mandates.

Rising
C·2
Core · The Honest Structure

The postgraduate gate.

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.

Lane A · Open at bachelor’s

The analyst funnel

BA Economics Research / credit / KPO analyst ₹3.5–9 LPA

Economic research analyst, credit analyst, market/industry research, KPO economic research, business economist. Entered directly after the BA — the broad, fast-growing funnel.

Lane B · Gated

The economist title

BA Economics ⊟ MA / MSc (PhD for the top) Economist

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.

C·3
Core · Roles & Required Skills

The roles a BA Economics graduate is hired into — as an economist.

Distinct from the finance/analytics/consulting specialization roles that come later. India entry pay shown; ★ marks roles that effectively require a Master’s.

Core roleIndia entry payCore skills screened
Economic research analyst / associate₹3.5–7 LPAMacro/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 avgSurvey methods · data analysis · reporting
KPO economic-research analyst₹4–7 LPAResearch · data · English · domain reading
ESG / sustainability economics analyst₹5–9 LPACarbon accounting · climate data · economics
Economist (junior / assistant) ★₹5–8 LPAEconometrics · modelling · policy (MA normed)
Think-tank research associate ★₹4–8 LPAStata/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.

C·4
Core · Who Hires Economists

The economics-centric recruiters.

Employers that hire economics graduates for genuinely economics-led work — government, research, ratings, multilaterals — with the Hyderabad / AP–Telangana presence flagged.

AGovernment & Regulators

RBI (DEPR)NITI AayogMin. of Finance / DEAChief Economic AdviserMoSPI / NSSOAP & TS Dir. of Econ & Stats

BThink Tanks

NCAERNIPFPICRIERCESS HyderabadICRISAT / IWMI

CRatings & Research

CRISILCARE (CareEdge)ICRAIndia RatingsSBI Research

DEconomic Advisory / Consulting

DeloittePwCEYKPMGMcKinseyBCG

EMultilaterals & Data

World BankIMFADBUN IndiaNielsenIQKantar

FKPO / GCC Economic Research

EvalueserveWNSGenpactOxford EconomicsMoody’s AnalyticsEIU
Gold = active in Hyderabad / AP / Telangana / South India · the city hosts ICRISAT/IWMI, University of Hyderabad School of Economics, CESS and large GCC research centres
C·5
Core · Pay Packages & Growth

What economics pays — and why it pays two ways.

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.

India fresher pay is bimodal, not a single number
₹ LPA · entry-level distribution
illustrative shape
Two clusters: a generalist hump (₹3–6 LPA) and a smaller, higher data-literate / regulatory hump (₹6–9 LPA+). The gap between them is closed by data skills, a target employer, or the RBI/IES track.

The government track — fixed scales, high stability

RBI Grade B · DEPR

Reserve Bank of India

₹1.5 L /mo gross

Basic ₹78,450 (2026 revision); ~₹40–42 L CTC in metros. The DEPR stream recruits economists directly. Needs a Master’s in Economics.

UPSC · Group A

Indian Economic Service

₹56,100 /mo basic

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.

State · AP & Telangana

Directorate of Econ & Statistics

State scales

ASO (mandal) up to Director on fixed state pay scales. Telangana DES alone employs ~921 statisticians and economists.

The global benchmark

US · Economist (BLS median)
$115,440
10th–90th pct $62,340–$212,710
UK · Govt Economic Service
£35k→£68k+
Assistant → experienced economist
IMF · Economist (PhD entry)
$120k+
seniors past $200k
World Bank · YPP
~$115k
net; specialist bands $130k–$220k

The economist-track growth curve (India)

Entry · 0–2 yrs
₹3–6 L
Research / credit / KPO analyst, generalist start
3–5 yrs
₹8–16.5 L
With MA + skills; senior analyst / consultant
Mid-career
₹15–30 L
Consulting / rating-agency senior research
Senior · ceiling
₹50 L–1 cr+
Chief economist / economic adviser (PhD-normed)
C·6
Core · The Skills Spine

What every economics employer screens for.

The non-negotiable core that travels across every specialization — and the add-ons that belong to the tracks, not the foundation.

Must-have core · screened everywhere

The spine

Microeconomics & macroeconomicstheory
Econometricsmost valuable
Statistics & mathematical economicsquant
A statistical package — Stata / R / Pythontool
Economic modelling & forecastingapplied
Economic writing & researchcomms
Indian-economy & policy domain knowledgecontext
Specialization add-ons · NOT core

The branches

Financial modelling & valuation→ IB / FinTech
Power BI / Tableau / advanced ML→ Analytics
Case & hypothesis structuring→ Consulting
Impact evaluation / RCT methods→ Public Policy
GIS / spatial data science→ Spatial
Survey & field research design→ Political Science

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.

C·7
Core · Credentials

Economics-core certifications worth stacking.

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.

CertificationIssuing bodyTypeUG fit
CFA Program — Level ICFA InstituteIndustry bodyFinal-year UG · demanding
FRM — Part IGARPIndustry bodyYr 3–4 · risk-focused
Bloomberg Market Concepts (BMC)BloombergOEM / vendorYr 1–2 · markets data
Power BI Data Analyst (PL-300)MicrosoftOEM / vendorYr 2–3 · the recognised BI cert
Certified Analytics Professional (CAP)INFORMSIndustry bodyYr 4 / PG · analytics
NISM Series V-A & XV (Research Analyst)NISM · SEBIIndustry-regulatoryYr 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.

II
Movement Two · The Amplifiers

The six specializations.

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.

01
Part 01 · Specialization Thrust Areas

Where demand is forming — and which track captures the pay.

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 areaPrincipal driversLead specialization(s)Pay signal
AI & Big Data analyticsWEF #1 skill; India data-analytics market USD 2.6B (2024) → 27.0B (2033) @ 27.46% CAGR (IMARC)Business Analytics · Financial Engineering & FinTechHighest volume + fast hikes
FinTech & capital marketsRBI / SEBI, GIFT City, digital payments, bulge-bracket expansionFinancial Engineering & FinTechHighest ceiling
Corporate strategy & transformationGCC strategy units, Big 4 advisory, digital/AI transformation mandatesManagement & Strategy Consulting · Business AnalyticsHigh pay + broad exits
Public-policy data / governanceNITI Aayog, Digital India, MeitY data governancePublic Policy + AnalyticsModerate, rising
Digital & platform economyPlatform regulation (DPDP Act, Digital Competition Bill), the data economy, network markets, e-commerce & gig platformsBusiness Analytics · Financial Engineering & FinTechHigh & fast-growing
Spatial / location intelligenceNational Geospatial Policy 2022, Operation Dronagiri (AP pilot), retail & logistics location analytics, Digital TwinsSpatial Economics & Location IntelligenceLowest floor, strong growth
Circular economy & sustainabilityExtended Producer Responsibility rules, resource-efficiency & recycling markets, carbon markets; WEF “environmental stewardship” top-10 skillPublic Policy · Financial Engineering & FinTech₹9 LPA+ (CRISIL ESG)
Political intelligence / public affairsIndia’s electoral-consulting industryPolitical ScienceI-PAC ₹6.7–15.6 LPA
GCC / data-centre economy (regional)AP IT/GCC Policy 4.0 (2024–29), Telangana AI City, Hyderabad data centresAnalytics · FinanceStrong 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.

The Six Specializations · Ranked

A league table for earning power.

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.

01
Financial Engineering & FinTechHighest ceiling
Entry band
₹6–24 LPA
Ceiling
₹1 cr+ (MD)
Best-fit roles · certs
IB · equity research · risk · digital-payments / fintech product
CFA · FMVA · BMC · NISM
02
Business AnalyticsVolume + velocity
Entry band
₹3.5–6 LPA
Ceiling
₹25 LPA+ (sr)
Best-fit roles · certs
Data/BI analyst · decision scientist · ops
PL-300 · Tableau CDA · AWS
03
Management & Strategy ConsultingHigh pay · broad exits
Entry band
₹4–10 LPA
Ceiling
₹25–35 LPA+ (MBA)
Best-fit roles · certs
Associate consultant · business / advisory analyst · economic-consulting analyst
Case prep · CAPM · analytics · financial modeling
04
Public PolicyHigh ceiling, social capital
Entry band
₹3.5–9 LPA
Ceiling
₹28 LPA (MPP norm)
Best-fit roles · certs
Policy / M&E analyst · dev consultant
Power BI (PL-300) · INFORMS CAP · Stata/R
05
Political ScienceNiche, well-paid
Entry band
₹6–7 LPA
Ceiling
₹15.6 LPA+ (Lead)
Best-fit roles · certs
Political / campaign-data analyst · public affairs
Research · communication · regional language
06
Spatial Economics & Location IntelligenceStrongest regional pull
Entry band
₹2.5–4.5 LPA
Ceiling
₹18 LPA+ (GeoAI)
Best-fit roles · certs
Location / spatial-data analyst · site selection
Esri · GISP · Python for geospatial

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.

02
Part 02 · Industry Requirements

The roles industry hires freshers into — and the skills they screen for.

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.

Financial Engineering & FinTech

Investment-banking analyst · equity research associate · credit / risk analyst · fintech / digital-payments product analyst · ESG analyst
Skills screened
Financial modelingValuation (DCF/LBO)AccountingExcelBloombergPython · SQL

Business Analytics

Data analyst · BI analyst · business analyst · market-research / ops analyst · decision scientist
Skills screened
SQL (baseline)ExcelPower BI / TableauPythonStatisticsStakeholder comms

Public Policy

Policy analyst · research associate · M&E / impact-evaluation analyst · development consultant · public-affairs associate
Skills screened
EconometricsRCT / impact methodsSTATA / RPolicy writing

Political Science

Political / intelligence analyst · campaign data analyst · public-affairs & corporate-affairs associate
Skills screened
Primary & secondary researchData analysisLogical reasoningRegional-language fluencyCommunication

Spatial Economics & Location Intelligence

Location / spatial-data analyst · geospatial analyst · site-selection analyst · remote-sensing analyst
Skills screened
ArcGIS / QGISSpatial data sciencePython for geospatialRemote sensingSpatial statistics

Management & Strategy Consulting

Associate consultant · business / advisory analyst · strategy analyst · economic-consulting analyst · operations consultant
Skills screened
Case solvingStructured problem-solvingMarket sizingExcel · PowerPointFinancial modelingAnalytics

Economics Core (any track)

Economic research analyst · data analyst · business analyst · research associate · consulting analyst
Skills screened
EconometricsSTATA / R / PythonExcelData storytelling
03
Part 03 · Who Hires Freshers

Named recruiters — with the Hyderabad / AP–Telangana advantage flagged.

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.

AConsulting / Advisory

DeloittePwCEYKPMGAccentureMcKinsey*BCG*Bain*

BAnalytics Firms

FractalTiger AnalyticsMu SigmaLatentViewZS AssociatesGenpactEXLTheMathCompany

CFinance & Banks

JP MorganGoldman SachsMorgan StanleyCitiKotakICICI SecuritiesCRISILS&PMoody’sFitch

DThink Tanks / Policy

NITI AayogCPRORFICRIERNIPFPVidhiJ-PALIDinsight

ESpatial Economics

CyientEsri IndiaRMSIGenesys Intl.SatSureAPSAC

FPolitical / Public Affairs & Tech

I-PACVarahe AnalyticsPoliticalEDGEAmazonMicrosoftGoogleUber
Gold = active recruiter in Hyderabad / AP / Telangana  ·  * MBB roles typically MBA-gated
04
Part 04 · Entry Pay Anchors

What freshers actually earn — India, by role.

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.

Investment Banking Analystbulge / boutique
6–24
ESG AnalystCRISIL
~9
Policy AnalystVidhi · think tanks
6–9
Political ConsultingI-PAC analyst → lead
6.7–15.6
Big 4 Advisory Analystgraduate intake
4–7
Data / Business Analystnatl. avg ₹6.87 LPA
3.5–8
Equity Research AssociateCRISIL etc.
4–8
Spatial / Location Analystlowest floor of six
2.5–4.5
06121824 LPA
Global ceiling · US BLS medians (May 2024)

What the same careers pay abroad.

US · Economists
$115,440
median annual wage
US · Financial & Investment Analysts
$101,350
median annual wage
US · Management Analysts
$101,190
median annual wage
US · Financial Risk Specialists
$106,000
median annual wage

US first-year IB analyst base ~$100,000–125,000 (NYC) · London ~£60,000. These define the global ceiling and justify certifications that travel.

04
Part 04 · Pay Growth With Experience

Careers don’t pay in lines — they pay in curves.

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.

The full earnings curve, by role family
₹ LPA · log scale
Entry → 3–5 yrs → Mid → Senior
Curves are indicative midpoints synthesized from 2024–26 salary bands — directional, not guaranteed. Finance senior = ₹1 cr+ (charted at the ceiling).
05
Part 05 · Experience, Built Into the Curriculum

Graduates can earn the experience that earns the offer — for credit.

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.

The regulatory chassis · UGC FYUGP + AEDP

Up to 20% of total credits can be apprenticeship or internship.

20%
of total credits eligible for apprenticeship/internship under AEDP, delivered via the NATS portal with a government stipend
1 cr = 45 hrs
UGC norm; exit-point internships are 4 credits (~8–10 weeks), evaluated jointly by industry (30–40%) and a faculty mentor
95% · 58%
Northeastern co-op benchmark: 95% of students complete a co-op; 58% receive an offer from a prior co-op employer

FLAME University

Pune · staggered

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.

Christ University

Bengaluru · CGPA-linked

Credit-bearing internships (2–4 credits) counting toward CGPA; a 2-month summer internship at exit points; a 12-credit final-year research project.

Ashoka University

Sonipat · research

The 4-year program totals 150 credits including 2–4 internship credits, fulfillable via summer research or global programs.

Symbiosis School of Economics

Pune · vocational diploma

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).

Northeastern University

Boston · co-op gold standard

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.

Recommended architecture

This program

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.

06
Part 06 · Global Certifications, Mapped

Stackable credentials that travel — earned across the degree.

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.

CertificationIssuing bodyTypeTrackUG fit
Bloomberg Market Concepts (BMC)BloombergOEMFin. Eng. & FinTechYr 1–2 · easy
FMVA — Financial Modeling & ValuationCorporate Finance InstituteIndustryFin. Eng. & FinTechYr 3 · moderate
CFA Program — Level ICFA InstituteIndustry bodyFin. Eng. & FinTechYr 3–4 · demanding
FRM — Part IGARPIndustry bodyFin. Eng. & FinTechYr 3–4 · risk
NISM Series V-A / XVNISM · SEBIIndustry-regulatoryFin. Eng. & FinTechYr 1–3 · accessible
Power BI Data Analyst (PL-300)MicrosoftOEMBusiness AnalyticsYr 2–3 · recognised BI cert
Tableau Certified Data AnalystTableau (Salesforce)OEMBusiness AnalyticsYr 2–3 · moderate
Azure Data Scientist (DP-100)MicrosoftOEMBusiness AnalyticsYr 3–4 · advanced
Certified Data Engineer – AssociateAmazon Web ServicesOEMBusiness AnalyticsYr 4 / PG · advanced
Certified Analytics Professional (CAP)INFORMSIndustry bodyBusiness AnalyticsYr 4 / PG
Machine Learning – SpecialtyAmazon Web ServicesOEMAI · cross-cuttingYr 4 / PG · advanced
Azure AI Engineer (AI-102)MicrosoftOEMAI · cross-cuttingYr 3–4 · moderate
Generative AI Engineer — AssociateDatabricksOEMAI · cross-cuttingYr 4 / PG
Esri ArcGIS Technical CertificationEsriOEMSpatial EconomicsYr 2–3 · moderate
GISPGIS Certification InstituteIndustry bodySpatial Economicspost-experience
Certificate in ESG InvestingCFA InstituteIndustry bodyESG · cross-cuttingYr 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.

Year 01

Foundations & vendor signals

Bloomberg Market ConceptsBloomberg · Finance
Office Specialist: Excel ExpertMicrosoft · cross-cutting
NISM Series V-ASEBI · easiest market entry
Tableau Desktop SpecialistTableau · Analytics
Year 02

Tooling & vendor depth

Power BI Data Analyst (PL-300)Microsoft · Analytics
Tableau Certified Data AnalystTableau · Analytics
Esri ArcGIS Technical Cert.Esri · Spatial Economics
NISM Series VIIISEBI · Finance
Year 03

Professional credentials

CFI FMVACorporate Finance Institute
Azure Data Scientist (DP-100)Microsoft · Analytics / AI
NISM Series XV — Research AnalystSEBI-mandated for research
Certificate in ESG InvestingCFA Institute · ESG
Final / Honours yr

Industry-body credentials

CFA Program — Level ICFA Institute · Finance
FRM — Part IGARP · Risk
AWS ML – Specialty / Databricks Gen AIOEM · AI / Analytics
Certified Analytics ProfessionalINFORMS · Analytics
07
Part 07 · The Pay-Maximizing Combinations

It’s never one thing — it’s the combination.

Specialization × skill × certification × experience. These are the bachelor’s-level stacks that reach the highest offers.

Financial Engineering & FinTech + financial modeling + FMVA/BMC + IB / equity-research internship → bulge-bracket or boutique IB, or fintech product
₹8–24 LPA entry
₹1 cr+ ceiling
Business Analytics + Python/SQL/Power BI + PL-300/Tableau CDA + analytics-firm internship → product-analytics / decision-science
₹6–10 LPA entry
fastest hikes
Management & Strategy Consulting + analytics + Big 4 / MBB internship → associate consultant / business analyst, strong exit options
₹4–10 → ₹25–35
LPA with MBA
Public Policy + econometrics / impact methods + think-tank / J-PAL internship → policy / development analyst
₹3.5–9 LPA
strong ceiling
Spatial Economics + Python / remote sensing + Esri cert + Cyient / APSAC internship → location-intelligence / spatial-data analyst
rising floor
strong AP fit
III
Movement Three · The Delivery

The curriculum.

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.

D·1
Curriculum · Course Allocation

How 120 credits are budgeted.

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.

12
Economics core
7
Maths · Stats · Econometrics
5
Technology
3
Language & communication
5
Specialization
2
Capstone projects
48 crEconomics core
28 crMaths · Stats · Econometrics
15 crSpecialization
15 crTechnology
8 crCapstone
6 crLanguage
Economics core Maths, Statistics & Econometrics Technology Language & Communication Specialization Capstone

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).

D·2
Curriculum · The Trimester Map

Nine trimesters, sequenced for compounding skill.

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).

HANDBOOKSCourses marked with a are clickable — open any highlighted course for its full handbook: outcomes, six-module syllabus, case studies, labs, careers and onward path. Every core handbook across Trimesters 1–9 is live — twenty-nine courses in full; the specialization electives are being folded in stream by stream.
Year 1Foundations & the methods spine · T01–T03
T0114 cr · 27 CH
ECO-101Principles of Microeconomics3-3-0-0 · CH6 · 4cr
QTM-101Mathematical Economics I — Calculus & Linear Algebra2-1-3-0 · CH6 · 3cr
TEC-101Programming for Economists (Python & R)2-1-6-0 · CH9 · 4cr
LNG-101Academic & Research Writing for Economists2-1-3-0 · CH6 · 3cr
ProjectBazaar Economics — The Local Price Lab
CRT · 6hQuantitative Aptitude I — numbers, ratios, speed maths
T0213 cr · 24 CH
ECO-102Principles of Macroeconomics3-3-0-0 · CH6 · 4cr
QTM-102Mathematical Economics II — Optimization & Dynamic Systems2-1-3-0 · CH6 · 3cr
QTM-201Statistics I — Descriptive Statistics & Probability2-1-6-0 · CH9 · 4cr
LNG-102Foreign Language Elective · German / Japanese / Korean2-1-0-0 · CH3 · 2cr
ProjectMacro Pulse — India’s Vital-Signs Monitor
CRT · 6hQuantitative Aptitude II — data interpretation & probability
T0314 cr · 27 CH
ECO-201Microeconomic Theory & Market Structures3-3-0-0 · CH6 · 4cr
ECO-203Game Theory & Strategic Decisions2-1-3-0 · CH6 · 3cr
QTM-202Statistics II — Inferential Statistics & Distributions2-1-6-0 · CH9 · 4cr
LNG-201Professional Communication & Data Storytelling2-1-3-0 · CH6 · 3cr
ProjectThe Strategy Lab — Game-Theory Tournaments
CRT · 6hLogical Reasoning + Verbal I · GD practice
Summer 1
Y1 → Y2

Foundational Data & Research Internship INT-1 · 2 cr

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.

6–8 weeks · 2 cr
+ 1 audit
transcript-recorded
Year 2Core theory, econometrics & specialization PE-1 begins · T04–T06
T0414 cr · 27 CH
ECO-202Macroeconomic Theory & Monetary Economics3-3-0-0 · CH6 · 4cr
QTM-301Econometrics I — Regression & Cross-Sectional Methods2-1-6-0 · CH9 · 4cr
ECO-204Indian Economy: Policy & Contemporary Issues2-1-3-0 · CH6 · 3cr
TEC-201Data Science & Visualization for Economics2-1-3-0 · CH6 · 3cr
ProjectWhat Moves India? — Econometric Investigation
CRT · 6hData Interpretation & Case Math (consulting-style)
T0513 cr · 27 CH
QTM-302Econometrics II — Time-Series, Panel & Causal Inference2-1-6-0 · CH9 · 4cr
TEC-301Machine Learning for Economics2-1-3-0 · CH6 · 3cr
ECO-301Development Economics2-1-3-0 · CH6 · 3cr
ECO-304Behavioural & Experimental Economics2-1-3-0 · CH6 · 3cr
ProjectML for Bharat — Predict & Explain Development
CRT · 6hTechnical Interview Prep I — SQL · ML · Python
T0614 cr · 27 CH
SPL · 1Specialization course 1 — co-taught w/ BBA (T6)2-1-3-0 · CH6 · 3cr
SPL · 2Specialization course 2 — co-taught w/ BBA (T6)2-1-3-0 · CH6 · 3cr
QTM-303Advanced Optimization & Dynamic Methods for Economics2-1-6-0 · CH9 · 4cr
ECO-303Public Economics & Fiscal Policy3-3-0-0 · CH6 · 4cr
ProjectThe Optimizer’s Budget — Public-Finance Sprint
CRT · 6hDomain & Guesstimates · case interviews
Summer 2
Y2 → Y3

Specialization Industry Internship INT-2 · 2 cr

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.

8–10 weeks · 2 cr
+ 1 audit
PPO potential
Year 3Specialization completes (T08) · capstone · contemporary frontier · T07–T09
T0713 cr · 27 CH
SPL · 3Specialization course 3 — co-taught w/ BBA (T7)2-1-3-0 · CH6 · 3cr
SPL · 4Specialization course 4 — co-taught w/ BBA (T7)2-1-3-0 · CH6 · 3cr
ECO-302International Trade & Finance2-1-3-0 · CH6 · 3cr
TEC-302Generative AI & Agentic AI for Economic Analysis2-1-6-0 · CH9 · 4cr
ProjectThe AI Economist — Build an Agentic Analyst
CRT · 6hAdvanced Placement — mock interviews & negotiation
T0810 cr · 27 CH
SPL · 5Specialization course 5 — co-taught w/ BBA (T8)2-1-3-0 · CH6 · 3cr
CAP-401 · CapstoneCapstone Project I — Research Design & Proposal0-0-6-6 · CH12 · 3cr
ECO-305Environmental, Climate & Circular Economy2-1-6-0 · CH9 · 4cr
BeyondCapstone I (flagship project) · placement drives begin
T0911 cr · 27 CH
CAP-402 · CapstoneCapstone Project II — Implementation & Dissertation0-0-12-0 · CH12 · 4cr
ECO-306Digital, Platform & Data Economics2-1-6-0 · CH9 · 4cr
TEC-303Blockchain, Digital Assets & Decentralized Finance2-1-3-0 · CH6 · 3cr
BeyondCapstone II dissertation · live placement drives
Year 4Honours with Research · semester mode · second specialization + dissertation / internship · +40 credits
Semester 719 credits
SPL²-I · 2nd specSecond-specialization Course 12-1-3-0 · CH6 · 3cr
SPL²-II · 2nd specSecond-specialization Course 22-1-3-0 · CH6 · 3cr
SPL²-III · 2nd specSecond-specialization Course 32-1-3-0 · CH6 · 3cr
RDI-701 · 10 crResearch Dissertation / Industry Internship — Phase IP / S block · 10cr
Semester 821 credits
SPL²-IV · 2nd specSecond-specialization Course 42-1-3-0 · CH6 · 3cr
SPL²-V · 2nd specSecond-specialization Course 52-1-3-0 · CH6 · 3cr
RDI-702 · 15 crResearch Dissertation / Industry Internship — Phase IIP / S block · 15cr

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.

D·3
Curriculum · Beyond the Classroom

The experiential & placement engine — every trimester.

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.

7
trimester-wide team projects (teams of 3–4)
10+
economist seminars & industry workshops
42+ hrs
CRT, ≥6 hrs every trimester (T01–T07)
T08–T09
transition to capstone + live placement drives
T01Year 1 · foundations
Project
Bazaar Economics — The Local Price Lab
Teams map supply, demand & elasticity across 8 real Vijayawada micro-markets and build a Python “price atlas” explaining the anomalies. Micro · Python · writing
Sessions
1-day seminar — “Thinking Like an Economist”
A popular economist / senior alumnus, paired with a guided local-market field visit.
CRT · 6h
Quantitative Aptitude I
Numbers, ratios, percentages, speed maths · career-pathways map · resume v1.
T02Year 1 · foundations
Project
Macro Pulse — India’s Vital-Signs Monitor
Build a live dashboard of 10 macro indicators (RBI / MoSPI) with probability-based risk flags and a monthly “economy health report.” Macro · Statistics I · data
Sessions
1-day seminar by an RBI / policy economist
Monetary policy in practice, followed by an RBI MPC mock-committee exercise.
CRT · 6h
Quantitative Aptitude II
Data interpretation, probability & permutations (synced to Statistics I) · LinkedIn / profile build.
T03Year 1 · foundations
Project
The Strategy Lab — Game-Theory Tournaments
Design and run multi-round games (auctions, oligopoly, public goods) on 30+ players, then test observed vs Nash outcomes with inferential statistics. Game theory · Statistics II · communication
Sessions
2-day workshop by industry experts
Strategic thinking & market design, led by consulting / pricing practitioners.
CRT · 6h
Logical Reasoning + Verbal I
Critical reasoning, reading comprehension · structured communication & GD practice (synced to Communication).
T04Year 2 · methods
Project
What Moves India? — An Econometric Investigation
Pose a real causal question (monsoon → rural wages, repo rate → auto sales), gather public data, run the first real regression, and present a data-story. Econometrics I · Indian economy · data science
Sessions
1-day seminar by a senior econometrician / data scientist
Causal inference in industry, paired with a Union / State Budget deep-dive.
CRT · 6h
Data Interpretation & Case Math
Consulting-style DI and case maths · aptitude mock tests · internship application strategy.
T05Year 2 · specialization begins
Project
ML for Bharat — Predict & Explain Development
Use ML + causal methods on a public dataset (MGNREGA, school dropout, poverty) to both predict an outcome and isolate its driver, then write a policy brief. Econometrics II · ML · development
Sessions
2-day workshop by ML / data-science industry experts
From a fintech / analytics firm, capped with a live Kaggle-style sprint.
CRT · 6h
Technical Interview Prep I
SQL, statistics / ML interview questions, Python coding rounds (synced to ML & Econometrics II).
T06Year 2 · specialization
Project
The Optimizer’s Budget — A Public-Finance Design Sprint
Model a real allocation problem — a circular-economy subsidy (EPR / recycling incentives), a carbon tax, or a district budget — as a constrained optimization, find the welfare-maximizing solution, and defend it. Advanced Optimization · public economics · specialization
Sessions
1-day seminar by a public-finance expert
From NIPFP / a Finance Ministry track, with a live PPP / policy case study.
CRT · 6h
Domain & Guesstimates
Economics / finance domain interviews, market sizing, consulting case interviews (synced to public econ & specialization).
T07Year 3 · specialization completes · capstone springboard
Project
The AI Economist — Build an Agentic Analyst
Teams build a GenAI / agentic-AI tool for a real economics task — a trade-policy analyzer, a market-research agent, or a policy-brief generator — fusing their specialization with trade economics and agentic AI. Directly seeds the T08–T09 capstone. Specialization · trade & finance · GenAI / agentic AI
Sessions
2-day workshop by GenAI / quant-finance industry experts
AI in economics & finance — production tools, agentic workflows, and where the high-pay roles are heading.
CRT · 6h
Advanced Placement
Technical + HR mock interviews, salary negotiation, quant brainteasers (synced to specialization completion & capstone prep).

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.

D·4
Curriculum · The Project Playbook

Every out-of-class project, specified in full.

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.

Portfolio by design — seven projects become one graduation portfolio

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.

Excellent · 4
Exceeds the standard — rigorous, original, fully reproducible, employer-ready.
Proficient · 3
Meets the standard — correct and complete, with only minor gaps.
Developing · 2
Partially meets — notable gaps in rigour, execution or communication.
Beginning · 1
Below standard — incomplete or incorrect; not yet at the bar.

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.

T01

Bazaar Economics — The Local Price Lab

Draws on: Principles of Microeconomics · Programming (Python) · Academic Writing · team of 3–4 · 11 weeks
The Brief — what students do

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.

Expected outcome

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.

Deliverables
  • Cleaned dataset (CSV) of price–quantity observations + documented collection protocol
  • Python notebook: elasticity estimates and ≥3 decision-ready charts (demand curves, dispersion, markup map)
  • 1,500-word analytical brief explaining the economics behind the anomalies
  • 5-minute team presentation of the price atlas
  • Team video log (3–5 min) — the project told as a story: problem → approach → finding → takeaway, with visuals
Portfolio build
  • Publish notebook + dataset to a personal GitHub repo with a clear README
  • Add a “Local Price Lab” entry (one hero chart + paragraph) to a portfolio page
  • Post the price-atlas visual on LinkedIn with a 3-line economic insight
  • Publish the video log to YouTube / LinkedIn — a flagship storytelling asset
Evaluation rubric
CriterionWeight
Economic reasoning — micro theory explains the findings25%
Data collection & cleaning — protocol, ethics, reproducibility20%
Python analysis & visualisation — correct elasticity, honest charts20%
Written communication — clarity and structure10%
Video log & storytelling — narrative arc, clarity, visual craft15%
Teamwork & process10%
T02

Macro Pulse — India’s Vital-Signs Monitor

Draws on: Principles of Macroeconomics · Statistics I · Data · team of 3–4 · 11 weeks
The Brief — what students do

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.

Expected outcome

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.

Deliverables
  • Reproducible data pipeline (notebook/script) pulling all ten indicators
  • Interactive dashboard (Streamlit / Dash / Power BI / Plotly)
  • One-page monthly health report with probability-based risk flags
  • Data dictionary documenting each indicator, source and transformation
  • Team video log (3–5 min) — the project told as a story: problem → approach → finding → takeaway, with visuals
Portfolio build
  • Deploy the dashboard (Streamlit Cloud / GitHub Pages) with a public link
  • Repo with README + a sample report; add “India Macro Monitor” to the portfolio site
  • LinkedIn post with the live link and one macro insight
  • Publish the video log to YouTube / LinkedIn — a flagship storytelling asset
Evaluation rubric
CriterionWeight
Macro literacy & interpretation — indicators and their interactions25%
Statistical rigour — descriptive stats + valid probability flags20%
Data pipeline & reproducibility20%
Dashboard & report communication — clarity for a lay reader10%
Video log & storytelling — narrative arc, clarity, visual craft15%
Teamwork & process10%
T03

The Strategy Lab — Game-Theory Tournaments

Draws on: Microeconomic Theory · Game Theory · Statistics II · Communication · team of 3–4 · 11 weeks
The Brief — what students do

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.

Expected outcome

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.

Deliverables
  • Game design document — rules, payoffs, equilibrium derivation, hypotheses
  • Experimental dataset (all rounds) + analysis notebook with hypothesis tests
  • Findings report — observed vs predicted, with statistical evidence and behavioural explanation
  • Live tournament demonstration + 8-minute presentation
  • Team video log (3–5 min) — the project told as a story: problem → approach → finding → takeaway, with visuals
Portfolio build
  • Repo with game protocol, data and analysis
  • A short “research note” PDF (observed vs Nash) for the portfolio
  • LinkedIn / blog write-up of the most surprising deviation
  • Publish the video log to YouTube / LinkedIn — a flagship storytelling asset
Evaluation rubric
CriterionWeight
Game-theoretic rigour — formalisation and equilibrium derivation25%
Experimental design — incentives, controls, ethics15%
Inferential statistics — valid tests, correct interpretation20%
Behavioural explanation of deviations15%
Video log & storytelling — narrative arc, clarity, visual craft15%
Teamwork & process10%
T04

What Moves India? — An Econometric Investigation

Draws on: Econometrics I · Indian Economy · Data Science · team of 3–4 · 11 weeks
The Brief — what students do

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.

Expected outcome

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.

Deliverables
  • Question + hypothesis memo grounded in economic theory
  • Reproducible dataset + regression notebook (R/Python) with diagnostics
  • 2,500-word empirical report — intro, data, results, limitations
  • Data-story presentation with one headline chart
  • Team video log (3–5 min) — the project told as a story: problem → approach → finding → takeaway, with visuals
Portfolio build
  • Repo with data, code and report
  • A polished 2-page empirical brief for the portfolio
  • LinkedIn post of the headline finding with a causality caveat — signals methodological maturity
  • Publish the video log to YouTube / LinkedIn — a flagship storytelling asset
Evaluation rubric
CriterionWeight
Question framing & economic motivation20%
Data assembly & quality15%
Econometric execution — correct model, diagnostics, interpretation30%
Honesty about identification & limitations10%
Video log & storytelling — the data-story told well15%
Teamwork & process10%
T05

ML for Bharat — Predict & Explain Development

Draws on: Econometrics II (causal) · Machine Learning · Development Economics · team of 3–4 · 11 weeks
The Brief — what students do

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.

Expected outcome

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.

Deliverables
  • Reproducible ML pipeline — feature engineering, model, evaluation, SHAP
  • Causal analysis notebook — identification strategy and estimates
  • 3-page policy brief — problem, evidence, recommendation, caveats
  • Model card documenting data, metrics, limitations and fairness
  • Team video log (3–5 min) — the project told as a story: problem → approach → finding → takeaway, with visuals
Portfolio build
  • Repo with ML + causal notebooks and the model card
  • The policy brief as a standalone portfolio piece
  • A LinkedIn / Kaggle-style write-up contrasting “what predicts” vs “what causes”
  • Publish the video log to YouTube / LinkedIn — a flagship storytelling asset
Evaluation rubric
CriterionWeight
ML execution & validation — no leakage, right metrics25%
Causal reasoning — identification and interpretation20%
Prediction-vs-causation distinction — explicit and correct15%
Policy translation — actionable, evidence-based brief15%
Video log & storytelling — narrative arc, clarity, visual craft15%
Communication & reproducibility — model card, repo10%
T06

The Optimizer’s Budget — A Public-Finance Design Sprint

Draws on: Advanced Optimization · Public Economics · Specialization · Circular Economy · team of 3–4 · 11 weeks
The Brief — what students do

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.

Expected outcome

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.

Deliverables
  • Optimization model — formulation + code (SciPy / CVXPY / Excel-Solver) with documented constraints
  • Sensitivity / scenario analysis — how the optimum shifts with parameters
  • 3-page design memo — recommended allocation and trade-off discussion
  • Panel defence — 10-minute presentation + Q&A
  • Team video log (3–5 min) — the project told as a story: problem → approach → finding → takeaway, with visuals
Portfolio build
  • Repo with the model and scenario notebooks
  • The design memo as a portfolio piece
  • A one-slide “policy design” visual for LinkedIn
  • Publish the video log to YouTube / LinkedIn — a flagship storytelling asset
Evaluation rubric
CriterionWeight
Problem formulation — objective and constraints correctly specified25%
Optimization execution — correct method and solution25%
Economic interpretation — trade-offs, welfare, incidence15%
Sensitivity & robustness10%
Video log & storytelling — narrative arc, clarity, visual craft15%
Defence & teamwork10%
T07

The AI Economist — Build an Agentic Analyst

Draws on: Specialization · International Trade & Finance · Generative & Agentic AI · capstone springboard · team of 3–4 · 11 weeks
The Brief — what students do

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.

Expected outcome

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.

Deliverables
  • Working agentic tool — repo + runnable demo with documented architecture
  • Evaluation report — strengths, failure modes, guardrails
  • Economics rationale doc — the workflow it automates and the value created
  • Live demo + 10-minute presentation (capstone-pitch dry run)
  • Team video log (3–5 min) — the project told as a story: problem → approach → finding → takeaway, with visuals
Portfolio build
  • Deployed demo (or recorded walkthrough) + repo with README and architecture diagram
  • A flagship portfolio entry and LinkedIn launch post
  • Carries forward as the centrepiece project and capstone springboard
  • Publish the video log to YouTube / LinkedIn — a flagship storytelling asset
Evaluation rubric
CriterionWeight
Economics grounding — a real, valuable workflow correctly modelled25%
Technical build — working agentic pipeline, sound design25%
Specialization + trade/finance integration15%
Evaluation & responsible-AI awareness — failure modes, guardrails10%
Video log & storytelling — the demo told as a story15%
Documentation & portfolio readiness10%

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.

D·5
Curriculum · The Six Tracks, Course by Course

Five courses each — one chosen track, co-taught across T6–T8.

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.

Financial Engineering & FinTech

FE · shared with BBA
maps to BBA → Investment Banking & Global Capital Markets + FinTech
FE-1FMODFinancial Modelling, Valuation & Pitchbook CraftT6
FE-2PAYSPayments Infrastructure, UPI & India Stack ArchitectureT6
in-house altThe Economics of Digital Payments & FinTech — Field Studio. Activity-based: network-effect & two-sided-market economics of UPI, embedded finance and digital lending, built as a live model + financial-inclusion field project. Runs in-house, no classwork, if the BBA FinTech elective isn’t offered that term.
FE-3MALBMergers, Acquisitions & Leveraged BuyoutsT7
FE-4EQREEquity Research, Industry Analysis & Initiating CoverageT7
FE-5PEVCPrivate Equity, Venture Capital & Growth CapitalT8
Advanced electives · optional depth, beyond the core 5 — Algorithmic Trading & Quant Finance
FE·A1Market Microstructure, Order Books & Trading Mechanicsadv
FE·A2Quantitative Strategies, Factor Models & Statistical Arbitrageadv
FE·A3Algorithmic Execution, Backtesting & Risk Managementadv
FE·A4Crypto, Derivatives & Cross-Asset Trading Systemsadv
⇄ co-taught with BBA

Business Analytics

BA · shared with BBA
maps to BBA → Decision Intelligence & Applied AI
BA-1BIDVBusiness Intelligence, Visualization & Executive DashboardsT6 · core
BA-2CAUSCausal Inference, Experimentation & Quasi-Experimental MethodsT6
BA-3FCASForecasting, Time-Series & Demand PlanningT7
BA-4OPTIOptimization, Decision Modelling & Operations ResearchT7
BA-5DTLGDecision Storytelling, Stakeholder Briefings & Executive DashboardsT8
⇄ co-taught with BBA

Public Policy

PP · high ceiling
policy · evaluation · governance
PP-1Foundations of Public Policy & Policy Analysis
PP-2Programme Evaluation & Impact Assessment (Causal Methods)
PP-3Welfare, Health & Education Economics
PP-4Public Finance, Budgeting & Regulatory Economics
PP-5Data for Public Policy & Governance Analytics
BA-exclusive · economics track

Spatial Economics & Location Intelligence

SE · location intelligence
GIS · GeoAI · location analytics
SE-1Economic Geography & Spatial Economics
SE-2Geographic Information Systems (GIS) & Cartography
SE-3Remote Sensing, Geospatial Data Science & GeoAI
SE-4Urban, Regional & Transport Economics
SE-5Location Intelligence for Business & Spatial Analytics
BA-exclusive · economics track

Management & Strategy Consulting

MC · shared with BBA
maps to BBA → Strategy Consulting & Corporate Advisory
MC-1STFRStrategy Frameworks, Hypothesis-Driven Problem Solving & MECET6
MC-2AIPSAI Product Strategy, LLM Capabilities & Use-Case MappingT6
in-house altAI, Automation & the Economics of Strategy — Use-Case Studio. Activity-based: map AI use-cases to economic value using productivity, cost-structure and labour economics, delivered as a consulting-style strategy brief. Runs in-house, no classwork, if the BBA AI-Product elective isn’t offered that term.
MC-3INDMIndustry Deep Dives, Market Entry & Growth StrategyT7
MC-4OPTROperations Transformation, Cost-Out & Performance ImprovementT7
MC-5DASTDigital & AI Strategy, Org Design & Change ManagementT8
⇄ co-taught with BBA

Political Science

PS · public affairs
political economy · IR · electoral analytics
PS-1Political Theory & Comparative Government
PS-2Political Economy & Institutions
PS-3Indian Politics, Constitution & Governance
PS-4International Relations & Geopolitics
PS-5Political Data Analytics & Public Opinion Research
BA-exclusive · economics track

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).

D·6
Curriculum · Finalised Credit Framework

LTPS · contact hours · credits — every course.

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.

CodeCourseLTPSCHCr
T01 · Year 1
ECO-101Microeconomics3-3-0-064
QTM-101Calculus & Linear Algebra2-1-3-063
TEC-101Programming (Python & R)2-1-6-094
LNG-101Academic & Research Writing2-1-3-063
T01 subtotal2714
T02 · Year 1
ECO-102Macroeconomics3-3-0-064
QTM-102Optimization & Dynamics2-1-3-063
QTM-201Statistics I2-1-6-094
LNG-102Foreign Language Elective (German / Japanese / Korean)2-1-0-032
T02 subtotal2413
T03 · Year 1
ECO-201Microeconomic Theory & Market Structures3-3-0-064
ECO-203Game Theory2-1-3-063
QTM-202Statistics II2-1-6-094
LNG-201Professional Communication2-1-3-063
T03 subtotal2714
T04 · Year 2
ECO-202Macroeconomic Theory & Monetary Economics3-3-0-064
QTM-301Econometrics I2-1-6-094
ECO-204Indian Economy2-1-3-063
TEC-201Data Science & Visualization2-1-3-063
T04 subtotal2714
T05 · Year 2
QTM-302Econometrics II2-1-6-094
TEC-301Machine Learning2-1-3-063
ECO-301Development Economics2-1-3-063
ECO-304Behavioural & Experimental2-1-3-063
T05 subtotal2713
T06 · Year 2
SPL·1Specialization 1 — co-taught PE2-1-3-063
SPL·2Specialization 2 — co-taught PE2-1-3-063
QTM-303Advanced Optimization2-1-6-094
ECO-303Public Economics3-3-0-064
T06 subtotal2714
T07 · Year 3
SPL·3Specialization 3 — co-taught PE2-1-3-063
SPL·4Specialization 4 — co-taught PE2-1-3-063
ECO-302International Trade & Finance2-1-3-063
TEC-302Generative & Agentic AI2-1-6-094
T07 subtotal2713
T08 · Year 3
SPL·5Specialization 5 — co-taught PE2-1-3-063
ECO-305Environmental & Circular Economy2-1-6-094
CAP-401Capstone I — Research Design0-0-6-6123
T08 subtotal2710
T09 · Year 3
ECO-306Digital & Data Economics2-1-6-094
TEC-303Blockchain & DeFi2-1-3-063
CAP-402Capstone II — Implementation0-0-12-0124
T09 subtotal2711
Summer modules (outside weekly cap)
INT-1Summer Internship — Foundational0-0-0-12122
AUD-1Universal Human Values, Ethics & Gender Inclusion — audit1-1-0-020
INT-2Summer Internship — Specialization0-0-0-12122
AUD-2Indian Knowledge Systems for Economics — audit1-1-0-020
3-year total (T01–T09 + 2 summer)120
Year 4 · Honours with Research (optional, +40)
SPL²·I–VSecond specialization — 5 co-taught PEs2-1-3-0 ×53015
RDI-701Research Dissertation / Industry Internship — Phase IP / S blockFT10
RDI-702Research Dissertation / Industry Internship — Phase IIP / S blockFT15
4-year honours total160

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.

D·7
Curriculum · Design Notes

Why the structure is shaped this way.

Contemporary by design

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.

A research-grade methods spine

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.

Language tuned to the profession

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.

Business Analytics as a shared minor

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.

Economics deepened, not diluted

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 as proof of work

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.

Two contemporary throughlines — Digital & Circular economics

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.

Recommendations · Staged

What to build, in what order — with the thresholds that change the plan.

Stage01

Curriculum architecture — design now

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.

Threshold to change courseIf finance/analytics entry offers stay below ₹4 LPA for two consecutive cohorts, reweight toward data science and stronger internship pipelines.
Stage02

Prioritize tracks by resource

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).

ThresholdKeep Spatial Economics as an interdisciplinary minor rather than a full track if lab utilization or placements are weak.
Stage03

Experiential architecture

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.

Core KPIsCertification-completion rate · internship-to-PPO conversion.
Stage04

Certification roadmap — stacked across four years

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.

Reposition ruleIf a finance/IB pipeline doesn’t form, steer the finance track toward FP&A / credit / ESG / fintech-ops — more accessible, still strong pay.
Read With Care

Caveats & data provenance

  • Salary figures are 2024–26 market estimates from aggregators (Glassdoor, AmbitionBox, Levels.fyi, Indeed, PayScale) and industry blogs; ranges vary by college tier, city and individual skill — treat bands as indicative, not guaranteed.
  • Several roles (economist, senior policy analyst, MBB consultant) are postgraduate-gated; this blueprint maximizes bachelor’s outcomes but flags PG study (MPP/MBA/Master’s/CFA) as the norm for senior tiers.
  • WEF and BLS projections are forecasts, not certainties; AI may compress some entry-level analyst roles even as it grows specialist roles.
  • Spatial-economy and India analytics-market figures (₹63,000 cr by 2025; 10 lakh jobs; IMARC market sizing) are projections, not realized outcomes.
  • The NISM Series XV exam is being revised effective January 2026 — verify the current syllabus and fee before mapping.
  • Northeastern co-op is paid but non-credit; the Indian NEP equivalent is credit-bearing — the models are analogous, not identical.