Draft:Meritshot Zetta Edutech

• Comment: This article has no references. in order to prove notability, an article needs to references 2-3 sources which are reliable, independent, secondary sources, which talk about the subject of the Wikipedia article at length.
  In addition, this feels a LOT like an LLM written article, which is absolutely forbidden on Wikipedia. It needs to be rewritten in your own words Absurdum4242 (talk) 15:25, 12 August 2026 (UTC)

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Meritshot

Type	Private limited company
Industry	Educational technology
Founded	2 August 2023
Founders	Roshan Sharma; Maryam
Area served	India; international learners via online delivery
Products	Online certification programmes
Services	Live, mentor-led online education
Website	https://www.meritshot.com

Meritshot is an Indian educational technology company that delivers long-form, cohort-based online certification programmes in professional domains. The company operates under the legal entity Meritshot Zetta Edutech Private Limited, was founded on 2 August 2023, and is headquartered in Noida, Uttar Pradesh. It operates from a single location and delivers all instruction online, which allows it to run national cohorts without regional campuses. Its seven programmes cover Investment Banking, Business Analytics, Data Analytics, Data Science AI/ML with Agentic AI, Cyber Security, Backend Development Engineering, and Data Engineering, with durations ranging from six to nine months.

Meritshot's target learner is a working professional rather than a full-time student. Programmes are delivered through live weekend and evening sessions in batches of 25 to 30, are supported by one-to-one mentorship from industry practitioners, and are assessed through module assignments, tests and project submissions. Every programme is built so that the learner finishes holding a portfolio of project artefacts: financial models and pitch books in the finance track, penetration test reports and incident response playbooks in the security track, deployed pipelines and dashboards in the data tracks.

History and background

Founding

Meritshot was founded on 2 August 2023 and incorporated as Meritshot Zetta Edutech Private Limited, a private limited company registered in Gautam Buddha Nagar district, Uttar Pradesh. The founding team comprised Roshan Sharma, Chief Executive Officer, whose background is in software engineering across multiple industries, and Maryam, Chief Marketing Officer.

The company was established during a period of structural correction in Indian online education. The pandemic-era expansion of consumer education technology in India had been oriented largely towards school-age test preparation and self-paced video libraries; by 2023 that segment had entered consolidation, and scrutiny of completion and outcome claims across the category had intensified. Meritshot's founding thesis positioned the company deliberately away from that model and towards the adult professional market, and towards live instruction rather than recorded catalogues.

Operating footprint

Meritshot operates from a single office in Sector 59, Noida. It maintains no branch campuses. All teaching, mentorship, laboratory work, assessment and placement activity is conducted online through the company's learner portal at learn.meritshot.com, which hosts recorded lectures, laboratory guides, assignment submission, tests and curriculum materials. This structure allows the company to recruit both learners and mentors nationally , .

Programme portfolio development

The portfolio grew to seven programmes across five domain clusters:

Cluster	Programmes
Finance	Investment Banking
Management and analytics	Business Analytics; Data Analytics
Data and AI	Data Science AI/ML with Agentic AI; Data Engineering with Generative and Agentic AI
Engineering	Backend Development Engineering
Security	Cyber Security

Rather than a broad catalogue of short courses, the company offers long-form programmes with programme-specific projects and assessments.

Accreditation and certification partners

Certification varies by programme and is issued in multiple parallel credentials rather than a single completion certificate.

Credential	Issuer	Programmes
Meritshot Programme Completion Certificate	Meritshot	All
Meritshot Post Graduate Certificate	Meritshot	Investment Banking
Cisco Cyber Security Certificate	Cisco	Cyber Security
Microsoft accreditation	Microsoft	Backend Development Engineering; Data Science; Data Engineering †
NSDC-Aligned Completion Certificate	Medhavi Skills University	All
Certification preparation tracks	CompTIA Security+ (SY0-701); CEH v13 (AI)	Cyber Security

Certificates carry a unique digital verification identifier and are described by the company as globally verifiable by recruiters.

Educational methodology and pedagogy

Live cohort delivery

All programmes run as live, instructor-led online cohorts. Sessions are scheduled at weekends and in the evenings so that learners can remain in full-time employment, and every session is recorded with lifetime access. Live attendance is treated as the primary mode and recordings as revision support rather than as a substitute, on the reasoning that real-time participation permits questions, demonstrations, tool practice and immediate doubt resolution that a recording cannot reproduce.

==== Small batch sizes ==== The stated purpose is individual attention, deeper laboratory time per learner, and the practicability of reviewing submitted work individually.

Learning formats

Format	Description
Live classes	Interactive weekend and evening sessions led by practising professionals
Hands-on labs	Attack-defence ranges, modelling workshops, pipeline builds and cyber ranges depending on programme
1:1 mentorship	A dedicated mentor assigned to each learner
Recorded lectures	Lifetime access to the full session library
Doubt resolution	Daily weekday doubt-clearing sessions
Masterclasses and guest lectures	Sessions with senior practitioners from established firms
Case studies	Structured analysis of real transactions, breaches and business problems

Specialisation and elective structure

Programmes are not monolithic. Each carries specialisation paths or elective tracks that allow a learner to weight the second half of the programme towards a target role. The Investment Banking programme offers Financial Modelling, Valuation and IB Operations. The Cyber Security programme offers Cloud Security, AI in Cyber Security, DevSecOps and IoT Security. The Data Science programme offers Machine Learning Engineering, Agentic AI, and applied Deep Learning and NLP.

Assessment architecture: assignments, tests and submissions

Every programme uses the same three-part assessment structure, applied per module. The house standard is eight core modules and eight assignments in total, one attached to each module, with the Investment Banking programme running nine modules across its nine-month structure.†

Component	Description
Assignments	One assignment per module, eight in total. Each is tied to that module's content and must be attempted for programme completion. Assignments are graded and feed the skills assessment score printed on the certificate.
Tests	A module test at the close of each module, structured as multiple-correct questions with four lettered statements per question and no fixed number of correct options. Marks are awarded only for the complete correct set, which prevents partial-credit guessing. Questions are wrapped in a scenario built around a fictional organisation, typically framed as inheriting an incomplete analysis under a compressed deadline. Difficulty escalates across the module sequence.
Project submissions	Portfolio deliverables submitted, reviewed and returned with mentor feedback. These are the artefacts that constitute the learner's portfolio and are linked from the certificate.

Certification is conditional on all three. Company terms state that the learner must participate in all assignments and activities, attempt all tests, and complete all project submissions.

Assessment papers are additionally issued in role-specific variants in the programmes where roles diverge sharply, so that a learner is examined against the specialisation being pursued rather than against a single generic standard. The Cyber Security programme issues papers across three tracks (Security Operations; Ethical Hacking; Governance, Risk and Compliance), producing 24 papers across eight modules. The Business Analytics programme issues papers across three tracks (Analyst; Product and Consulting; Risk and Financial Analyst), producing 18 papers across six modules. Within a module, the same underlying statement bank is redeployed across tracks with option ordering varied per track.

Admissions, eligibility and enrolment

Who the programmes are built for

Meritshot's admissions position is that background matters less than commitment, and the published learner outcomes bear this out: the alumni record includes accountants, network administrators, IT support engineers, compliance associates, audit associates, junior developers and finance executives moving into roles that would conventionally be filled from a narrower pipeline. The company does not require a prior degree in the destination discipline.

Programme	Prior technical requirement	Typical entrant
Investment Banking	None. No coding background required	Commerce and accountancy graduates, chartered accountants, IT services professionals, MBA candidates, finance executives
Business Analytics	None. No coding background required	Operations and process professionals, marketing analysts, MBA candidates, consultants
Data Analytics	None. Basic spreadsheet familiarity assumed	Reporting and MIS professionals, operations analysts, support engineers, commerce graduates
Data Science AI/ML with Agentic AI	Basic programming familiarity	Software engineers, analysts, statisticians, engineering graduates
Cyber Security	Basic computer and networking familiarity	Network and system administrators, IT support engineers, developers, compliance staff
Backend Development Engineering	Basic programming familiarity	Frontend developers, QA engineers, support engineers, computer science graduates
Data Engineering	SQL and basic programming familiarity	Data analysts, backend developers, ETL and warehouse staff

Enrolment sequence

1. Enquiry and counselling. A free counselling session establishes the learner's current position, target role and realistic timeline. The company positions this as a screen in both directions: a learner whose target is not reachable through the programme is told so. 2. Programme and track selection. The learner selects the programme and the career track within it, which determines specialisation weighting in the second half. 3. Cohort allocation. Learners are placed in a batch of 25 to 30 with a fixed start date. 4. Payment or financing. Payment in full or through available financing arrangements. 5. Onboarding. Learner portal access at learn.meritshot.com, mentor assignment, laboratory environment provisioning and orientation. 6. Seven-day window. A full refund is available within seven days of programme commencement.

Time commitment

The stated expectation is ten to fifteen hours per week: live weekend and evening sessions, laboratory and project work, assignments, and module tests. Programmes run six to nine months, so total learner investment ranges from roughly 260 to 540 hours depending on the track.

The learning week

A representative week in an active cohort, which the company uses to set expectation at counselling rather than leaving learners to discover the load.

Element	Typical cadence	Purpose
Live sessions	Weekend and selected weekday evenings	Core instruction, demonstration and unscripted question handling
Hands-on lab	Alongside each technical session	Tool practice in a provisioned environment
Doubt-clearing session	Weekday	Resolution of blockers before they compound across a module
1:1 mentor time	Scheduled per learner	Track-specific guidance, project review, career direction
Assignment work	Per module	Graded practice tied to that module's content
Project work	Ongoing	Portfolio deliverables submitted for mentor review
Recorded review	Learner-scheduled	Revision using the lifetime session library
Masterclass or guest lecture	Periodic	Senior practitioner sessions outside the core syllabus

Mentorship model

Mentorship at Meritshot is structured rather than advisory. Each learner is assigned a dedicated mentor for the duration of the programme, and the assignment is made against the learner's declared career track rather than at random.

Function	What the mentor does
Project review	Reads submitted work line by line and returns written feedback for revision, rather than issuing a grade
Track guidance	Directs the learner's elective and project choices towards the declared destination role
Technical unblocking	Resolves problems that a group session cannot address at the required depth
Interview preparation	Conducts mock interviews in the format the target employer uses
Reality checking	Tells the learner where they currently stand against the bar for the target role

Instruction and mentorship are delivered by practitioners in current or recent industry roles rather than by full-time academic staff. The published roster spans Cisco, Microsoft, Amazon, Cognizant, PwC, EY, Deloitte, KPMG, Bank of America, Goldman Sachs and Transacta Capital, alongside independent consultants and two university faculty members.

Certification in depth

Certification is issued as a set of parallel credentials rather than a single completion certificate, and is conditional on completing all three assessment components.

Credential	Issuer	Applies to	What it evidences
Programme Completion Certificate	Meritshot	All programmes	Completion of all modules, assignments, tests and project submissions
Post Graduate Certificate	Meritshot	Investment Banking	Completion of the nine-module post graduate structure
Cyber Security Certificate	Cisco	Cyber Security	Partner-accredited security credential
Microsoft accreditation	Microsoft	Backend Development, Data Science, Data Engineering †	Partner accreditation on the technology tracks
NSDC-Aligned Completion Certificate	Medhavi Skills University	All programmes	National skills framework alignment
Internship Certificate	JP Morgan, via Forage	Investment Banking	Completion of the associated virtual programme
Certification preparation	CompTIA Security+ (SY0-701); CEH v13 (AI)	Cyber Security	Preparation mapped against external certification syllabi

Conditions. Company terms require the learner to participate in all assignments and activities, attempt all tests, and complete all project submissions. Certificates carry a unique digital verification identifier for employer validation, and are accompanied by a project portfolio link, a skills assessment score derived from assignment and test performance, and a mentor endorsement.

Programme policies

The company publishes a set of policy documents governing the learner relationship. Each is a separate published page.

Policy	What it governs
Certification policy	Conditions for certificate issue, verification, reissue and revocation
Examination policy	Test conduct, attempt requirements, retake provisions and integrity rules
Assignments policy	Submission requirements, deadlines, feedback cycle and resubmission
Attendance policy	Live session attendance expectations and the treatment of recorded catch-up
Placement policy	The scope of placement assistance, what is and is not undertaken, and learner obligations within the process
Refund policy	The seven-day full refund window and the treatment of withdrawals beyond it
Privacy policy	Handling of learner data
Terms and conditions	The general contractual framework
Code of conduct	Expected learner behaviour in live sessions, laboratories and the alumni community
Escalation policy	Route for raising and resolving learner complaints
Intellectual property policy	Ownership and permitted use of curriculum, laboratory and project material
Disclaimer	Limits on outcome representations

The placement policy describes placement assistance and learner obligations. The examination policy sets out the assessment requirements for certification.

Programmes

#	Programme	Duration	Modules	Assignments	Career tracks	Accreditation
1	Investment Banking	9 months	9	9	6	Meritshot PG Certificate; NSDC-aligned
2	Cyber Security	8 months	8	8	6	Cisco; NSDC-aligned
3	Data Science AI/ML with Agentic AI	9 months	8	8	5	Microsoft; NSDC-aligned
4	Backend Development Engineering	9 months	8	8	5	Microsoft; NSDC-aligned
5	Data Engineering with Generative and Agentic AI	7 months	8	8	5	Microsoft; NSDC-aligned
6	Business Analytics	6 months	8	8	6	NSDC-aligned
7	Data Analytics	6 months	8	8	6	NSDC-aligned

How each programme is set out below. First the curriculum. Then the career tracks, and each career track carries its own projects, its own assignments and its own outcomes inside it, so that a learner reading one track sees everything that track requires without cross-referencing. After the tracks, two reference views map the same work back to the module sequence: projects by module and assignments by module. Then tests, then submissions, then tools.

1. Investment Banking

Post Graduate Programme in Investment Banking. Nine months, nine modules, 35 weeks.

1.1 Curriculum

Core competencies

Competency	Coverage
Advanced Excel and Financial Modelling	VBA, macros, three-statement models
Business Valuation	DCF, comparable company analysis, precedent transactions
IPO and Stock Markets	IPO process, equity markets, listing mechanics
Risk Mitigation	Credit risk, market risk, VaR models
Compliance and Regulatory Acts	SEBI, RBI, Basel norms, FEMA
Financial Markets and Derivatives	Futures, options, swaps, hedging strategies

Module sequence

Module	Title	Weeks
01	Financial Systems and Global Markets	1–4
02	Excel and Advanced Excel for Investment Banking	5–8
03	Valuation Techniques	9–12
04	Financial Markets: Equity and Derivatives	13–16
05	Corporate Transactions: M&A and Leveraged Buyouts	17–20
06	Startup Ecosystem, Venture Capital and Due Diligence	21–23
07	Due Diligence and Compliance in Investment Banking	24–26
08	Regulatory Framework and Advanced Topics	27–29
09	Capstone Projects and Career Readiness	30–35

Module 01: Financial Systems and Global Markets (Weeks 1–4)

Builds the foundation in how global financial systems operate and where investment banks sit within them: how money moves through banking structures, forex markets and risk frameworks, in preparation for valuation, modelling and deal structuring.

What the learner covers

• Global financial system architecture: central banks, commercial banks and investment banks
• Forex markets: exchange rate mechanisms, hedging basics, cross-border transaction risk
• Risk management fundamentals: market, credit, operational and liquidity risk
• Key players in investment banking: buy-side against sell-side, brokers, clearing houses
• Practical application: how global events transmit into capital markets and IB deal cycles

Module projects: M&A Pitch Book Financial Section Build; Multi-Bank Financial Health Scorecard; Institutional Three-Statement Financial Model.

Outcomes: understands how global capital markets and banking systems interconnect; identifies the key players, instruments and risk frameworks used in investment banking; produces a first portfolio-ready financial analysis deliverable; holds the foundation required for valuation, modelling, M&A and deal advisory.

Module 02: Excel and Advanced Excel for Investment Banking (Weeks 5–8)

Establishes the production environment the analyst actually works in. Excel is treated as a professional instrument with conventions, not as a spreadsheet: layout discipline, auditability and error-trapping are graded alongside output correctness, because a model nobody else can check is a model nobody will use.

What the learner covers

• Model architecture: input, calculation and output separation; colour conventions; sheet ordering; named ranges
• Lookup, index-match, array and dynamic array functions; nested logic and error handling
• Scenario managers, data tables and goal seek for one and two-variable sensitivity
• VBA and macro automation for repetitive model tasks and formatting standardisation
• Three-statement integration: linking income statement, balance sheet and cash flow so the model balances in every period
• Circularity and iterative calculation: interest on average debt, the circular reference it creates, and how to control it
• Audit and error trapping: check rows, balance checks, flag cells, and reviewing a model built by someone else

Module project: model build and automation exercise; VBA-driven scenario engine.

Outcomes: builds a model another analyst can pick up and audit without explanation; automates repetitive tasks rather than repeating them; traps errors so a broken link surfaces immediately rather than at the pitch; can review and correct an inherited model, which is the majority of first-year work.

Module 03: Valuation Techniques (Weeks 9–12)

Establishes what a business is worth on both intrinsic and relative bases, and where the two diverge.

What the learner covers

• Unlevered free cash flow construction from the operating model
• WACC build from observable market inputs: risk-free rate, equity risk premium, levered and unlevered beta, cost of debt, target capital structure
• Terminal value under both perpetuity growth and exit multiple methods, and reconciling the implied growth rate between them
• Trading comparables: peer set construction, calendarisation, adjustment for non-recurring items, multiple selection by industry
• Precedent transactions: deal sourcing, control premia, synergy expectations embedded in announced multiples
• Football field reconciliation of intrinsic and relative ranges, and sum-of-the-parts for multi-segment businesses
• Sensitivity design: identifying the two assumptions that actually move the valuation

Module projects: DCF valuation with sensitivity analysis; comparable company and precedent transaction set. Case study: Amazon profitability analysis.

Outcomes: derives an intrinsic value and states the range rather than a point; builds a peer set and defends every inclusion and exclusion; explains why the DCF and the comparables disagree; walks an interviewer through a DCF without notes.

Module 04: Financial Markets: Equity and Derivatives (Weeks 13–16)

Covers the instruments and the market mechanics through which capital is actually raised and risk actually transferred.

What the learner covers

• Equity market structure: primary and secondary markets, order types, market microstructure, index construction
• The IPO process end to end: eligibility, DRHP, due diligence, book building, price band determination, allotment, listing and stabilisation
• Derivatives: futures, forwards, options and swaps; payoff structures; margining and settlement
• Option pricing intuition: the variables that move a premium and why, without treating Black-Scholes as a black box
• Hedging strategy construction: matching instrument and tenor to a defined exposure, and stating what the hedge does not cover
• Portfolio theory: efficient frontier, diversification, correlation; VaR, beta and the Sharpe ratio and the limitations of each

Module projects: IPO Valuation and Investor Pitch; Hedge Fund Portfolio and Risk Management. Case studies: Airbnb IPO; Bridgewater Associates strategy.

Outcomes: explains how a company gets listed and who does what in the process; prices and structures a hedge for a stated exposure; interprets rather than only calculates risk metrics; understands how the buy-side consumes sell-side work.

Module 05: Corporate Transactions: M&A and Leveraged Buyouts (Weeks 17–20)

The two transaction archetypes that define investment banking work, taught as models rather than as concepts.

What the learner covers

• Strategic rationale: horizontal, vertical and conglomerate logic; the tests a board applies
• Deal structuring: asset against share purchase; consideration mix; earn-outs; conditions precedent
• Synergy quantification and phasing: cost and revenue synergies, integration cost, realistic timing
• Purchase price allocation, goodwill creation and the accounting consequences that follow
• Accretion and dilution analysis across cash, stock and mixed consideration, and the breakeven exchange ratio
• LBO model construction: sources and uses, debt tranching, amortisation and cash sweep, covenant headroom, PIK and mezzanine treatment
• Returns attribution: decomposing IRR into deleveraging, multiple expansion and operating improvement, and identifying which the thesis depends on

Module projects: M&A Deal Analysis; Leveraged Buyout Model. Case studies: Tesla and SolarCity; KKR and RJR Nabisco.

Outcomes: states whether a deal creates or destroys value and supports it; builds a full debt schedule with a working cash sweep; answers the paper-LBO question standard in private equity interviews; identifies the assumption on which a deal thesis actually rests.

Module 06: Startup Ecosystem, Venture Capital and Due Diligence (Weeks 21–23)

Private markets, where valuation cannot rely on cash flows and structure carries as much value as price.

What the learner covers

• Funding stages from pre-seed to growth, and what changes at each in investor expectation and instrument
• Cap table construction and maintenance across rounds, including option pools and their timing
• Dilution and anti-dilution mechanics: full ratchet against broad and narrow-based weighted average
• Convertible instruments: SAFEs, convertible notes, caps, discounts and their conversion arithmetic
• Startup valuation methods where cash flows are absent: comparables, scorecard, venture capital method, milestone-based
• Term sheet economics and control: liquidation preference, participation, board composition, protective provisions, drag and tag rights
• Commercial due diligence: market sizing, unit economics interrogation, cohort quality, customer concentration

Module project: Startup Valuation and Fundraising Strategy. Case study: WeWork valuation collapse.

Outcomes: builds and maintains a cap table through several rounds; models founder and investor dilution under alternative structures; reads a term sheet for control as well as economics; values a company with no meaningful cash flows and states the confidence attached.

Module 07: Due Diligence and Compliance in Investment Banking (Weeks 24–26)

The control layer. Most transaction risk is discovered here rather than in the model.

What the learner covers

• Financial due diligence: quality of earnings, normalisation adjustments, working capital and net debt determination
• Legal and commercial workstreams: contract review priorities, change of control provisions, litigation exposure
• Data room construction and management; request lists; issue tracking and escalation
• Red flag identification: revenue recognition aggressiveness, related party transactions, customer concentration, unexplained margin movement
• KYC and AML process: customer identification, beneficial ownership, source of funds and wealth, ongoing monitoring, suspicious activity escalation
• Client lifecycle management: onboarding, periodic review, offboarding
• Conflict checks, information barriers and restricted lists

Module projects: due diligence workstream exercise; KYC and AML client file; red flag memorandum.

Outcomes: runs a diligence workstream against a request list rather than reading documents at random; identifies the findings that would change or kill a deal; completes a client onboarding file to a standard a compliance officer would accept; understands why the information barrier exists and what breaches it.

Module 08: Regulatory Framework and Advanced Topics (Weeks 27–29)

The rules the transaction operates inside, taught as constraints on structure rather than as background reading.

What the learner covers

• SEBI regulations: LODR disclosure obligations, ICDR for issues, SAST and the open offer trigger, insider trading and UPSI handling
• RBI framework: FDI routes and sectoral caps, ECB norms, banking regulation touchpoints
• Basel norms: capital adequacy, risk weighting, liquidity coverage, and how they constrain a bank counterparty
• FEMA and cross-border considerations: repatriation, pricing guidelines, round-tripping concerns
• Disclosure obligations and market abuse controls; the timing of announcements
• Restructuring and distressed situations: IBC process, creditor classes, resolution plans, distressed valuation

Module projects: regulatory compliance assessment; restructuring scenario.

Outcomes: identifies the regulatory triggers a proposed structure will hit before it is proposed; explains what changes when a shareholding crosses an open offer threshold; understands the constraint Basel places on a lending counterparty; can approach a distressed situation with the right valuation lens.

Module 09: Capstone Projects and Career Readiness (Weeks 30–35)

Integration and hiring. The longest module, and the one that produces the portfolio the learner interviews on.

What the learner covers

• End-to-end mandate execution: a single company carried through model, valuation, transaction analysis and recommendation
• Pitch construction and live defence to a mentor acting as client
• Technical interview preparation: valuation, accounting, modelling and market question banks, with drilling to fluency
• Behavioural and fit preparation: deal walkthroughs, motivation, market view
• Profile finalisation: curriculum vitae, LinkedIn, portfolio presentation of the models built
• Internship preparation for the industry placement

Module projects: Profit and Loss and Business Decision Analysis; integrated capstone mandate and live pitch defence.

Outcomes: holds a complete, defensible body of transaction work; answers technical questions at conversational speed rather than by recall; can walk through any model in the portfolio line by line; enters the internship able to contribute rather than observe.

Case studies

Case	Type	Module	What the learner does
Tesla and SolarCity	M&A deal analysis	05	Evaluates strategic rationale, synergy assumptions, valuation gaps and shareholder impact of a vertical integration transaction
KKR and RJR Nabisco	Leveraged buyout	05	Reconstructs the LBO, builds the model, analyses the debt structure, evaluates returns against the sponsor's thesis
Airbnb IPO	IPO valuation	04	Performs a full IPO valuation using DCF and comparable company analysis, then assesses pricing and first-day performance
WeWork	Startup risk analysis	06	Investigates the collapse from a $47bn valuation to under $10bn, analysing governance failures and unrealistic projections
Bridgewater Associates	Portfolio strategy	04	Studies the All Weather portfolio and risk parity strategy, and how macro-driven approaches generate consistent returns
Amazon	Financial statement analysis	03	Examines how a low-margin business generates large free cash flow and funds aggressive expansion

Foundation work common to every track

Three deliverables are produced in Module 01 and are common to all six career tracks: the M&A Pitch Book Financial Section Build, the Multi-Bank Financial Health Scorecard, and the Institutional Three-Statement Financial Model. They give the learner a portfolio-ready artefact inside the first four weeks and the modelling base layer that all later track work is built on. Assignments 01 and 02 are likewise common to every track.

1.2 Career tracks Track 1: Investment Banking Analyst

Role description. financial modelling, pitch books, M&A deal execution and valuations at investment banks.. Core skills: financial modelling; valuation (DCF, comparables); M&A analysis; Excel and PowerPoint..

Projects in this track

Project	Objective	Deliverables
M&A Deal Analysis	Analyse a real transaction end to end, from strategic rationale through valuation and synergy estimation to accretion and dilution	Sources and uses; pro forma combined financials; purchase price allocation with goodwill; synergy phasing; accretion and dilution bridge across cash, stock and mixed consideration
IPO Valuation and Investor Pitch	Value a pre-IPO company across methodologies and produce the listing pitch	Multi-method valuation range; book-building assumptions; formatted investor deck; live pitch defence
Leveraged Buyout Model	Determine the maximum price a sponsor can pay while meeting a target return	Full debt schedule with cash sweep; covenant headroom; exit assumptions; IRR and MOIC returns attribution
Capstone Mandate	Execute a complete sell-side mandate	Integrated model, valuation package, merger analysis, pitch deck and defence, assessed as one body of work

Track 2: Equity Research Analyst

Role description. company analysis, research reports and investment recommendations for institutional investors.. Core skills: equity valuation; financial statement analysis; industry research; report writing..

Projects in this track

Project	Objective	Deliverables
IPO Valuation and Investor Pitch	Establish a defensible value for a company approaching listing	DCF and comparable company analysis; pricing strategy assessment; first-day performance evaluation
Comparable Company and Precedent Transaction Analysis	Build a peer set and derive a relative valuation range	Trading comparables with calendarised and adjusted multiples; precedent transactions with control premia; football field reconciliation
Hedge Fund Portfolio and Risk Management	Understand how the buy-side consumes research	Efficient portfolio construction; VaR, beta and Sharpe; derivative hedge selection and sizing
Initiation Coverage Note	Convert analysis into the format the role actually produces	Written research note with thesis, valuation, catalysts, risks and a rated recommendation

Track 3: Private Equity Analyst

Role description. investment evaluation, LBO modelling, due diligence, and portfolio company monitoring.. Core skills: LBO modelling; deal structuring; due diligence; financial forecasting..

Projects in this track

Project	Objective	Deliverables
Leveraged Buyout Model	Build a complete LBO for a sponsor acquisition	Sources and uses; full debt schedule with cash sweep and covenants; sensitivity testing; returns attribution across deleveraging, multiple expansion and operating improvement
Startup Valuation and Fundraising Strategy	Value and structure a private growth investment	Cap table through multiple rounds; dilution and anti-dilution modelling; convertible instrument treatment; fundraising strategy for a Series B raise
Due Diligence Workstream	Run the diligence that precedes a private investment	Financial, legal and commercial diligence checklists; quality of earnings assessment; red flag memorandum
Capstone Mandate	Execute a buy-side investment recommendation	Investment thesis, model, diligence findings and committee-format recommendation

1.3 Projects by module (reference view)

Module	Project work attached
01	M&A Pitch Book Financial Section Build; Multi-Bank Financial Health Scorecard; Institutional Three-Statement Financial Model
02	Model build and automation exercise; VBA-driven scenario engine
03	DCF valuation with sensitivity analysis; comparable company and precedent transaction set. Case: Amazon
04	IPO Valuation and Investor Pitch; Hedge Fund Portfolio and Risk Management. Cases: Airbnb; Bridgewater
05	M&A Deal Analysis; Leveraged Buyout Model. Cases: Tesla and SolarCity; KKR and RJR Nabisco
06	Startup Valuation and Fundraising Strategy. Case: WeWork
07	Due diligence workstream; KYC and AML client file; red flag memorandum
08	Regulatory compliance assessment; restructuring scenario
09	Profit and Loss and Business Decision Analysis; integrated capstone mandate and live pitch defence

1.4 Assignments by module (reference view)

Nine assignments, one per module. All nine are required for completion regardless of track; the track sections above identify which carry most weight for each career path.

Module	Assignment focus
01	Financial system mapping and risk framework identification
02	Excel model build to specification, with audit trail and error trapping
03	Valuation input derivation and multiple calculation
04	Instrument pricing, hedge construction and portfolio metrics
05	Accretion and dilution calculation; debt schedule construction
06	Cap table and dilution modelling
07	Due diligence checklist execution and red flag write-up
08	Regulatory scenario analysis against SEBI, RBI, Basel and FEMA provisions
09	Capstone components and interview technical bank

Assignment performance contributes to the skills assessment score recorded on the certificate.

1.5 Tests

A test closes each module. Tests use the house multiple-correct format: four lettered statements per question, no fixed number of correct options, credit awarded only for the complete correct set. Questions sit inside a scenario built around a fictional firm. Difficulty escalates across the sequence, from foundational at Module 01 to expert across the transaction and regulatory modules. All tests must be attempted for certification.

1.6 Submissions

Project submissions are the assessed portfolio layer. Each is submitted through the learner portal, reviewed by a mentor and returned with written feedback for revision. Completion of all project submissions is a certification condition and a condition of the Forage-issued internship certificate.

1.7 Tools

Tool	Category
Bloomberg Terminal	Financial data platform: real-time pricing across equities, bonds, FX and commodities; DLIB and PORT portfolio analytics; Bloomberg Intelligence research synthesis; BQL Excel add-in; IB Chat
S&P Capital IQ	Financial intelligence: screening, financial extraction, comparable sets, Excel integration
PitchBook	Private market intelligence: private company, fund and transaction data
AlphaSense	Market intelligence: search across filings, transcripts and broker research
FinanceGPT	AI finance assistant: document interrogation, drafting, research summarisation
Fenergo	Client lifecycle management: onboarding, KYC and AML workflow
Hyperproof	Compliance management: control frameworks, evidence collection, audit readiness
Mixpanel	Product analytics: funnel, cohort and retention analysis for diligence
Advanced Excel, VBA and macros	Core production environment
PowerPoint	Pitch book and deck production
DCF models and LBO templates	Institutional model templates

1.8 Instructors

Instructor	Role	Firm	Experience
Abhishek Garg	Manager	PwC	5+ years, financial advisory, M&A due diligence, transaction services
Hunny Arya	Manager	EY	7+ years, financial services advisory and transaction structuring
Rahul Jain	M&A Advisor	Transacta Capital	7+ years, deal origination, structuring, financial due diligence
Dhruv Bajaj	Deputy Manager	Deloitte	8+ years, transaction advisory, due diligence, LBO modelling
Jinay Jain	Chartered Accountant	QuintEdge	5+ years, investment analysis, financial modelling, equity research
Anupam Dixit	Senior Finance Mentor	Independent	12+ years; M&A transactions exceeding $500m; 200+ bankers trained
Upama Das	Senior Investment Analyst	Independent	9+ years across PE, VC and public markets; 100+ deals evaluated
Anjana Srinivasan	Client Service Associate	Independent	6+ years, wealth management and equity research
Himanshu	Professor, Business Finance	LPU	8+ years academic and industry
Ravi Das	Professor, Business Finance	Amity University	10+ years

2. Cyber Security

Professional Cyber Security Programme with Gen AI. Eight months, eight core modules, 34 weeks. Because adversaries have incorporated generative AI into reconnaissance, social engineering and payload development, AI tooling is treated as part of the defensive toolchain rather than as an optional module.

2.1 Curriculum

Six core domains

Domain	Coverage
Cryptography	Encryption, hashing, PKI and digital certificates
Identity and Access Management	MFA, SSO, IAM policies, Zero Trust
Network Security	Firewalls, IDS/IPS, VPNs, network monitoring
DNS and Web Attacks	DNS poisoning, DDoS, MITM, OWASP Top 10
Database and Cloud Security	SQL injection prevention, AWS/Azure/GCP security and IAM
Ethical Hacking and Penetration Testing	Vulnerability assessment, exploit development, red teaming

Module sequence

Delivered as two stacked paths. The Beginner Path (Foundation Track) is 16 weeks and 4 modules. The Advanced Path is 34 weeks and 8 modules in total, comprising the four foundation modules plus four advanced modules.

Module	Title	Weeks	Path
01	Cyber Security Foundations	1–4	Beginner
02	Ethical Hacking and Penetration Testing	5–9	Beginner
03	Enterprise Defence and Cloud Security	10–13	Beginner
04	Career Readiness and Capstone	14–16	Beginner
05	Advanced Security and Specialised Domains	17–21	Advanced
06	Red Team vs Blue Team Cyber Range	22–26	Advanced
07	Cyber Security Leadership and Compliance	27–30	Advanced
08	AI-Powered Capstones and Career Launchpad	31–34	Advanced

Module 01: Cyber Security Foundations (Weeks 1–4)

Builds the foundation in computer networks, security principles and Python scripting: how networks are structured, how threats propagate, and how to automate security tasks.

What the learner covers

• Computer and network fundamentals: OSI model, TCP/IP, firewalls and VPNs
• Core security principles: CIA triad, authentication, authorisation, non-repudiation
• Python for security: scripting, automation, secure coding practice
• Network scanning and automation: Nmap, Shodan, reconnaissance technique
• Phishing and malware basics: attack vectors, social engineering, threat classification

Module project: Automated Network Scanner and Security Audit Tool.

Outcomes: understands how networks and security systems are architected; writes Python scripts that automate security tasks and scanning; identifies common attack vectors and threat categories; performs basic network reconnaissance using industry tools.

Module 02: Ethical Hacking and Penetration Testing (Weeks 5–9)

The offensive foundation. The longest of the beginner modules, because exploitation cannot be taught faster than the learner can practise it safely.

What the learner covers

• Reconnaissance: passive and active information gathering, OSINT, subdomain and asset discovery
• Enumeration: service and version identification, banner grabbing, share and user enumeration
• Vulnerability identification and, critically, validation, so that a scanner finding is confirmed rather than reported
• Exploitation technique and payload selection; Metasploit workflow; manual exploitation where tooling fails
• Web application attack classes across the OWASP Top 10: injection, broken authentication, misconfiguration, XSS, insecure deserialisation, SSRF
• Privilege escalation on Linux and Windows: misconfigurations, credential reuse, kernel and service exploits
• Professional reporting: executive summary, technical findings with reproduction steps, risk rating, remediation guidance

Module projects: Web Application Penetration Test; Web and Malware Security Lab (offensive half). Case study: Colonial Pipeline ransomware.

Outcomes: executes an engagement from scoping through reporting; validates findings rather than forwarding scanner output; chains low-severity findings into a high-severity path; writes the report, which is the deliverable clients pay for and .

Module 03: Enterprise Defence and Cloud Security (Weeks 10–13)

The blue team counterpart. The learner moves from causing the alert to receiving it.

What the learner covers

• SOC operating model: tiers, escalation paths, shift handover, playbooks, metrics
• SIEM ingestion and normalisation: log sources, parsing, field mapping, retention design
• Detection engineering: writing rules, tuning against false positives, mapping coverage to adversary technique
• Log analysis and incident triage: what to look at first, what to close, what to escalate
• Cloud security posture across AWS, Azure and GCP: configuration baselines, logging and monitoring services, common misconfiguration classes
• Cloud IAM: roles, policies, least privilege, cross-account access, key and secret management
• The shared responsibility model and where teams routinely misjudge it
• System hardening and secure configuration: baselines, patching cadence, service reduction

Module projects: Network Security Architecture; Identity and Access Security Lab; SOC Monitoring and Detection Engineering. Case study: Equifax breach.

Outcomes: onboards and normalises a log source rather than querying one already configured; writes a detection rule and states its expected false positive behaviour; audits a cloud estate against its own IAM policy; explains the shared responsibility boundary to engineers who assume the provider covers more than it does.

Module 04: Career Readiness and Capstone (Weeks 14–16)

Closes the Foundation Track. Consolidates the first three modules into an employable package and produces the decision point for continuing into the Advanced Track.

What the learner covers

• Foundation capstone integrating network, offensive and defensive work
• Portfolio assembly: laboratory write-ups, scripts, detection rules and reports organised for a recruiter
• Curriculum vitae, LinkedIn and GitHub profile construction for security roles specifically
• Mock interviews with practising security professionals, technical and scenario-based
• Certification path preparation: CompTIA Security+ (SY0-701) and CEH v13 (AI) mapping against what has been covered
• Phishing and social engineering defence: simulation design, result interpretation, awareness programme construction

Module projects: foundation capstone; Phishing and Social Engineering Defence. Case study: WannaCry global ransomware.

Outcomes: holds an assembled portfolio rather than scattered laboratory files; can discuss their own work in an interview at depth; understands which certification to pursue and when; can design and run a phishing simulation and act on what it shows.

Module 05: Advanced Security and Specialised Domains (Weeks 17–21)

Opens the Advanced Track. Depth replaces breadth, and the learner commits to an elective.

What the learner covers

• Advanced network security: segmentation strategy, east-west traffic inspection, encrypted traffic analysis, deception
• Advanced application security: business logic flaws, API security, authentication bypass chains, race conditions
• Vulnerability management as a programme rather than a scan: asset criticality, exploitability weighting, remediation sequencing, exception handling
• Secure development and the software supply chain: dependency risk, container image provenance, infrastructure as code scanning, secrets in repository history
• Elective depth in one of Cloud Security, AI in Cyber Security, DevSecOps or IoT Security

Module projects: elective deep-dive project; Vulnerability Assessment; Secure Development and Secrets Assignment. Case study: Log4Shell zero-day response.

Outcomes: runs vulnerability management as a risk-reduction programme rather than a scan report; assesses supply chain exposure across dependencies, containers and infrastructure code; holds demonstrable depth in one specialisation rather than uniform shallow coverage.

Module 06: Red Team vs Blue Team Cyber Range (Weeks 22–26)

A live adversarial range where each learner operates on both sides.

What the learner covers

• Objective-based attack planning: defining the crown jewel and working backwards to initial access
• Initial access, execution, persistence, defence evasion and credential access techniques
• Lateral movement and privilege escalation across a simulated enterprise
• Live defence: detection under attack, tuning without breaking coverage, containment decisions with incomplete information
• Threat hunting: hypothesis formation, data source selection, query construction, converting confirmed technique into permanent detection
• Incident response lifecycle: triage, scoping, containment, evidence preservation, timeline reconstruction, eradication, recovery, post-incident review
• Incident reporting under time pressure for both technical and executive audiences

Module projects: Red Team vs Blue Team Capstone; Red Team Simulation; Incident Response Exercise; Threat Hunting Hypothesis. Case study: Capital One cloud breach.

Outcomes: has attacked and defended the same environment, and therefore knows which of their own actions are detectable; scopes an incident rather than chasing the first alert; preserves evidence in a form that survives scrutiny; writes the post-incident report that determines what an organisation changes afterwards.

Module 07: Cyber Security Leadership and Compliance (Weeks 27–30)

The governance layer, and the module that separates a practitioner from a candidate for architect and CISO-track roles.

What the learner covers

• Risk assessment methodology: asset identification, threat modelling, likelihood and impact scoring, inherent against residual risk
• Risk register construction, treatment decisions, owner assignment and review cadence
• Control frameworks: ISO 27001 Annex A, NIST CSF and 800-53, PCI DSS, GDPR obligations, and mapping controls across them without duplicating effort
• Policy lifecycle: authoring, approval, communication, exception management, review
• Audit preparation and evidence: what an auditor asks for, how to produce it continuously rather than at audit time
• Business impact analysis: criticality, recovery time objective, recovery point objective
• Business continuity and disaster recovery planning and testing
• Security strategy and board communication: expressing residual risk in business terms

Module project: GRC deliverable comprising risk register, control mapping, business impact analysis, continuity and disaster recovery plan, and audit evidence pack.

Outcomes: builds a risk register that drives decisions rather than sitting in a folder; maps controls to a recognised framework and produces audit-ready evidence; sets recovery objectives that reflect business reality; presents residual risk to a board in the language the board uses.

Module 08: AI-Powered Capstones and Career Launchpad (Weeks 31–34)

Closes the programme. The AI layer is applied across everything already learned, and placement activates.

What the learner covers

• AI-assisted penetration testing workflows: attack path suggestion, payload generation, findings documentation, and where the assistant must not be trusted
• AI in detection and triage: alert enrichment, summarisation, anomaly explanation, and the human gate that must remain
• Security automation: building workflows that enrich, triage and respond to a defined alert class without analyst intervention, with documented escalation conditions
• Adversarial use of AI: how attackers use generative tooling in reconnaissance, phishing and payload development, and the detection implications
• Final capstone execution and portfolio consolidation
• Placement activation: hiring partner introduction, referral, interview scheduling and negotiation support

Module projects: AI-powered capstone; Security Automation workflow; final portfolio consolidation.

Outcomes: uses AI tooling to compress engagement and triage time while retaining judgement over the output; automates a response workflow end to end; understands the adversarial side of the same tooling; enters placement with a complete portfolio and an activated referral network.

Specialised electives

Cloud Security; AI in Cyber Security; DevSecOps; IoT Security.

Case studies

Case	Type	Module	What the learner does
SolarWinds (SUNBURST)	Supply chain attack	01	Traces how APT29 embedded malware in a software update, compromised 18,000+ organisations and evaded detection for months; builds detection rules and IR playbooks
Colonial Pipeline (DarkSide)	Ransomware response	02	Reconstructs the kill chain, analyses the VPN credential compromise, evaluates the $4.4m ransom decision, designs a zero-trust architecture
Equifax	Vulnerability exploitation	03	Dissects the breach exposing 147m records, analyses the unpatched Apache Struts vulnerability, maps lateral movement, builds a patch management and compliance framework
WannaCry	Malware analysis	04	Reverse-engineers the ransomware, analyses the EternalBlue SMB exploit and worm propagation, maps the kill switch discovery, builds segmentation defences
Log4Shell (CVE-2021-44228)	Zero-day response	05	Exploits the JNDI injection in a controlled lab, builds WAF rules, scans enterprise environments, creates an emergency patching strategy
Capital One	Cloud security audit	06	Audits the AWS breach, analyses the misconfigured WAF and SSRF exploit, reviews IAM policy failures, designs a hardened cloud architecture

Foundation work common to every track

The Module 01 Automated Network Scanner and Security Audit Tool and Assignment 01 are common to all six tracks, as is the Module 06 Red Team vs Blue Team Capstone, which every learner completes on both sides of the engagement.

Track 1: SOC Analyst

Role description. monitors and responds to security incidents in a security operations centre; analyses logs, detects threats, manages SIEM tooling and triages alerts.. Core skills: SIEM tools; log analysis; threat detection; incident triage..

Projects in this track

Project	Objective	Deliverables
SOC Monitoring and Detection Engineering	Move from raw telemetry to actionable alerting on an estate containing both benign activity and an intrusion	Log source onboarding and normalisation; search queries isolating the anomalous activity; detection rules with documented true and false positive expectations; alert triage record
Web and Malware Security Lab	Detect and analyse the attack classes a SOC sees daily	SQLi and XSS identification; sandbox malware analysis; SIEM detection rules built from observed behaviour
Phishing and Social Engineering Defence	Handle the attack class behind most successful breaches	Phishing simulation and result interpretation; MITM detection; user awareness training programme
Red Team vs Blue Team Capstone	Defend live against a real attack chain	Live defence, detection tuning under attack, and a comprehensive incident report

Track 2: Penetration Tester

Role description. conducts ethical hacking and identifies system vulnerabilities; web application testing, network pentesting and security audits.. Core skills: Kali Linux; web application testing; network pentesting; exploit development..

Projects in this track

Project	Objective	Deliverables
Web Application Penetration Test	Exploit and document application-layer weaknesses in an intentionally vulnerable application	Documented reconnaissance and mapping; exploitation of OWASP Top 10 class weaknesses with evidence; proof of concept per finding; professional report written for both technical and executive audiences
Web and Malware Security Lab	Both exploit and remediate	SQLi and XSS exploitation and patching; sandbox malware analysis
Red Team Simulation	Chain individual weaknesses into an objective-based attack path	Attack narrative from initial access through privilege escalation and lateral movement to objective completion, mapped to an adversary technique framework, with the detection opportunities missed at each stage
Red Team vs Blue Team Capstone	Operate a live engagement end to end	Attack execution, defensive counterpart, and comprehensive incident report

Track 3: Security Engineer

Role description. builds and maintains enterprise security infrastructure; designs secure networks, implements firewalls, hardens systems.. Core skills: vulnerability assessment; network security; threat detection; hardening..

Projects in this track

Project	Objective	Deliverables
Network Security Architecture	Design a secure business network for a simulated enterprise	Segmentation design with rationale; firewall and VPN configuration; authored and tuned IDS/IPS rules; monitoring design
Identity and Access Security Lab	Build the authentication and trust layer	MFA implementation; secure login and token-based access; PKI infrastructure with issuance, trust chains and revocation
Security Automation with Python	Remove repetitive manual security work	Password strength checking; secure file transfer; custom enterprise security scripts
Vulnerability Assessment	Enumerate and prioritise exposure across a target environment	Authenticated against unauthenticated scan comparison; false positive validation; risk-ranked finding register combining severity, asset criticality and exploitability; remediation plan sequenced by risk reduction per unit of effort

2.3 Projects by module (reference view)

Module	Project work attached
01	Automated Network Scanner and Security Audit Tool; Security Automation with Python. Case: SolarWinds
02	Web Application Penetration Test; Web and Malware Security Lab (offensive half). Case: Colonial Pipeline
03	Network Security Architecture; Identity and Access Security Lab; SOC Monitoring and Detection Engineering. Case: Equifax
04	Foundation capstone; Phishing and Social Engineering Defence. Case: WannaCry
05	Elective deep-dive project (Cloud Security, AI in Cyber Security, DevSecOps or IoT Security); Vulnerability Assessment; Secure Development and Secrets Assignment. Case: Log4Shell
06	Red Team vs Blue Team Capstone; Red Team Simulation; Incident Response Exercise; Threat Hunting Hypothesis. Case: Capital One
07	GRC deliverable: risk register, control mapping, BIA, BCP and DR plan, audit evidence pack
08	AI-powered capstone; Security Automation workflow; final portfolio consolidation

2.4 Assignments by module (reference view)

Eight assignments, one per module. All eight are required for completion regardless of track.

Module	Assignment focus
01	Network mapping, CIA triad application, Python security scripting
02	Reconnaissance and enumeration exercise; vulnerability validation write-up
03	SIEM query authoring and detection rule construction; cloud IAM policy review
04	Portfolio and profile deliverables; certification path preparation
05	Elective-specific technical assignment
06	Attack chain documentation mapped to an adversary technique framework
07	Risk register construction and control framework mapping
08	AI-assisted security workflow assignment

2.5 Tests

Twenty-four assessment papers: eight modules across three tracks (Security Operations; Ethical Hacking; Governance, Risk and Compliance). Each paper carries 30 multiple-correct questions in scenario blocks, four lettered statements per question, no fixed number of correct options, credit only for the complete correct set. Each scenario is set in its own distinct fictional organisation, and answer-set sizes vary across a paper so that pattern-matching does not substitute for knowledge. Difficulty escalates from beginner at Module 01 to expert across Modules 05 to 08.

2.6 Submissions

All project submissions must be completed for certification, including the Red Team vs Blue Team capstone report and the AI-powered capstone. Submissions are reviewed by mentors and returned with feedback. The public FAQ carries a dedicated Submissions section of eight questions.

2.7 Tools

25+ industry tools across six domains, plus a dedicated AI tool layer.

Domain	Tools
Languages	Python, Bash, SQL
Security tools	Kali Linux, Metasploit, Nmap, Burp Suite, Wireshark, Shodan
SIEM and monitoring	Splunk, ELK Stack, OSSEC, Nagios
Cloud platforms	AWS, Azure, GCP
Infrastructure	Docker, Kubernetes, CI/CD pipelines
Compliance	ISO 27001, GDPR, NIST, PCI DSS

AI security tool layer

Tool	Category	Function
HackerAI (PentestGPT)	AI pentesting assistant	Suggests attack paths and payloads in real time; automated payload generation; findings documentation; context-aware remediation guidance; methodology aligned to OWASP and PTES; integration context for Burp Suite, Metasploit and Nmap. Reported to reduce engagement time by up to 40%
GitGuardian	Secret detection and remediation	Repository and pipeline scanning for exposed credentials, keys and tokens
Tines	SOC automation and SOAR	No-code automation of alert enrichment, triage and response
Splunk	SIEM and threat detection	Log ingestion, search processing language, correlation searches, dashboards
Linux auditd	System monitoring and forensics	Kernel-level audit rules, event interrogation, host forensic evidence
Tenable Nessus	Vulnerability assessment	Credentialed and uncredentialed scanning, plugin detection, risk-ranked reporting
Snyk	Developer security	SAST, software composition analysis, container and IaC scanning
Eramba	GRC and risk management	Risk register, control and framework mapping, policy lifecycle, audit tracking

2.8 Mentors

Mentor	Role	Firm	Experience
Julien Joseph	Chief Information Security Officer	Cisco	7+ years; enterprise security strategy, incident response, compliance
Anushka Dixit	Senior Threat Intelligence Specialist	Amazon	4+ years; threat intelligence, vulnerability analysis, incident response
Abhishek Rana	Cyber Security Engineer	Cognizant	5+ years; network security, vulnerability assessment, incident response
Maulik Lakhani	Cyber Security Professional	Independent	7+ years; product security, AppSec, pentesting, secure SDLC, threat modelling
Kunal Bharadwaj	Cyber Security Evangelist	Independent	Industry mentor; awareness, best practice, secure digital transformation

3. Data Science AI/ML with Agentic AI

Nine months, eight core modules, eight assignments. Microsoft-accredited. The defining curriculum decision is that agentic artificial intelligence, meaning autonomous systems that plan, call tools and act across multiple steps, is taught as a first-class subject on a machine learning foundation rather than appended as a final module. The framing is that model training remains necessary but is no longer sufficient, because much applied AI work is now orchestrating foundation models, retrieval systems and tool-calling agents into production, and evaluating systems whose outputs are non-deterministic.

3.1 Curriculum

Module	Title	Principal content
01	Programming and Data Foundations	Python, NumPy, Pandas, data structures, SQL for analysis, version control, environment management
02	Mathematics and Statistics for ML	Linear algebra, calculus for optimisation, probability, distributions, hypothesis testing, experiment design
03	Exploratory Analysis and Feature Engineering	Data cleaning, missingness strategy, outlier treatment, encoding, scaling, feature selection, leakage prevention
04	Classical Machine Learning	Regression, classification, tree ensembles including random forests and gradient boosting, clustering, dimensionality reduction, cross-validation, metric selection
05	Deep Learning	Neural network fundamentals, backpropagation, convolutional networks, recurrent and sequence architectures, transfer learning, regularisation
06	NLP and Transformers	Text representation, embeddings, attention, transformer architecture, fine-tuning, evaluation of generative output
07	Generative AI and Retrieval-Augmented Generation	Prompt design, context management, embedding models, vector databases, chunking and retrieval strategy, RAG evaluation, hallucination mitigation
08	Agentic AI, MLOps and Capstone	Agent architectures, tool calling, planning and reflection loops, multi-agent orchestration, state and memory, guardrails, model packaging and serving, containerisation, experiment tracking, monitoring and drift detection

Case work is grounded in Indian consumer internet and fintech businesses, including the recommendation, logistics and fraud detection problems characteristic of firms such as Swiggy and PhonePe. The Module 08 integrated capstone is common to every track.

Track 1: Data Scientist

Role description. frames business problems statistically, builds and validates models, communicates findings to decision-makers.. Core skills: statistics; classical ML; experiment design; communication..

Projects in this track

Project	Objective	Deliverables
End-to-End Supervised Learning Problem	Deliver a model that is defensible rather than merely accurate, from a raw and imperfect dataset with a real business decision attached	Exploratory analysis; documented feature pipeline; baseline plus two tuned candidate models; evaluation against a metric justified by the business cost of each error type; error analysis identifying where the model fails and for whom
Experiment Design and A/B Analysis	Establish causation rather than correlation	Hypothesis and power calculation; randomisation design; result analysis with confidence intervals; decision recommendation
Business Case Analysis	Connect a model to a decision	Problem framing, success metric definition, expected value of the model against the status quo
Integrated Capstone	Ship the full arc of the role in one artefact	Pipeline, model, evaluation, deployment and written narrative

Track 2: Machine Learning Engineer

Role description. takes models to production and keeps them there; serving, scaling, monitoring and retraining.. Core skills: MLOps; containerisation; serving; monitoring..

Projects in this track

Project	Objective	Deliverables
Deployment and MLOps	Move a model from notebook to service	Containerised inference service behind an API; experiment tracking record linking the deployed artefact to its training run; monitoring specification for latency, throughput and input drift; documented retraining trigger
Model Optimisation and Serving	Make inference viable at production cost and latency	Batching and caching strategy; quantisation or distillation where applicable; measured latency and cost per request before and after
Multi-Agent System	Operate systems whose behaviour is non-deterministic	Agent graph with roles and handoffs; tool definitions; guardrails and termination conditions; trace logs; failure analysis
Integrated Capstone	Deliver a production-shaped system	Pipeline, model, deployment, observability and evaluation harness

Track 3: AI Engineer (Agentic Systems)

Role description. builds LLM-backed applications, retrieval systems and autonomous agents.. Core skills: agent frameworks; RAG; evaluation; guardrails..

Projects in this track

Project	Objective	Deliverables
Retrieval-Augmented Generation System	Build question answering grounded in a private corpus that a general model cannot answer without retrieval	Ingestion and chunking pipeline; embedding and vector store; retrieval with reranking; generation with citation of sources; evaluation harness measuring retrieval precision and answer faithfulness
Multi-Agent System	Build an autonomous system completing a multi-step task requiring external tools	Agent graph with defined roles and handoffs; tool definitions and calling logic; memory and state across steps; guardrails and termination conditions; execution trace; failure analysis when a tool call fails or the plan is invalid
Evaluation Harness	Judge non-deterministic output systematically	Golden dataset; automated scoring; regression detection across prompt and model changes
Integrated Capstone	Ship an agentic application	Full system with retrieval, agents, deployment and evaluation

3.3 Projects by module (reference view)

Module	Project work attached
01	Data acquisition and preparation exercise
02	Experiment Design and A/B Analysis; statistical inference exercise
03	Feature engineering pipeline with leakage audit
04	End-to-End Supervised Learning Problem; Business Case Analysis
05	Deep Learning Application; Model Optimisation and Serving
06	Transformer Fine-Tuning
07	Retrieval-Augmented Generation System; Evaluation Harness
08	Multi-Agent System; Deployment and MLOps; Integrated Capstone

3.4 Assignments by module (reference view)

Module	Assignment focus
01	Python and SQL foundations
02	Statistical inference and experiment design
03	Feature engineering with leakage audit
04	Model selection and evaluation
05	Neural architecture training
06	Embedding and fine-tuning
07	Retrieval strategy and RAG evaluation
08	Agent design and MLOps instrumentation

3.5 Tests

A module test at the close of each module in the house multiple-correct scenario format, escalating from foundational to expert. All eight must be attempted for certification.

3.6 Submissions

Project submissions plus module deliverables, reviewed by mentors and returned with written feedback. Completion of all submissions is a certification condition.

3.7 Tools

Layer	Tools
Core	Python, NumPy, Pandas, scikit-learn
Deep learning	PyTorch, TensorFlow
Agentic and LLM frameworks	LangChain, LangGraph, AutoGen
Retrieval	Vector databases, embedding models
Serving and operations	FastAPI, Streamlit, Docker, MLflow

4. Backend Development Engineering

Nine months, eight core modules, eight assignments. Microsoft-accredited. Positioned around AI-first backend engineering: services now embed model inference, retrieval and agentic behaviour as ordinary components, and the engineer designs, secures and operates systems whose behaviour is partly probabilistic.

4.1 Curriculum

Module	Title	Principal content
01	Language, Foundations and Version Control	Backend language proficiency, data structures and algorithms, asynchronous programming, error handling, testing discipline, Git workflows
02	API Design and Implementation	REST semantics and resource modelling, versioning, pagination, idempotency, contract design and documentation, authentication and authorisation
03	Relational Databases	Schema design and normalisation, indexing and query planning, transactions and isolation levels, connection pooling, migration strategy
04	NoSQL, Caching and Data Modelling	Document and wide-column modelling, access-pattern-driven design, cache strategy and invalidation, consistency trade-offs
05	Asynchronous and Event-Driven Architecture	Queues and workers, message semantics, idempotent handling, retries and dead letter treatment, event-driven design
06	System Design and Scale	Load balancing, horizontal scaling, consistency models, rate limiting, failure isolation, capacity reasoning, trade-off articulation
07	AI Integration	Model API integration, embedding and vector retrieval inside application services, streaming responses, prompt and context management, cost and latency control, output evaluation, fallback design
08	Operations, Security and Capstone	Containerisation, deployment pipelines, observability (logging, metrics, tracing), incident handling, performance profiling, secrets handling, dependency and supply chain risk, capstone

The Module 08 capstone, a complete backend system incorporating API, persistence, asynchronous processing, an AI-driven capability and production tooling, is common to every track.

Track 1: Backend Engineer

Role description. builds and operates the services behind a product.. Core skills: API design; databases; testing; operations..

Projects in this track

Project	Objective	Deliverables
Production-Grade REST API	Build a service that survives review at a professional engineering organisation	Consistent resource modelling and error semantics; authentication and role-based authorisation; request validation; structured logging; unit and integration test suite; OpenAPI specification exercised through a request collection
Relational Data Layer and Query Optimisation	Fix a schema and query set that degrade under realistic data volume	Normalised schema with justified denormalisation; indexing strategy supported by query plan evidence; before and after performance comparison; written justification of the isolation level chosen
Asynchronous and Event-Driven Service	Separate request handling from long-running work	Queue-backed worker architecture; idempotent message handling; retry and dead letter treatment; demonstrated behaviour under consumer failure
Capstone	Ship a complete system	API, persistence, async processing, AI capability, operational tooling

Track 2: Software Development Engineer

Role description. general product engineering at a technology firm.. Core skills: data structures and algorithms; system design; code quality..

Projects in this track

Project	Objective	Deliverables
System Design Exercise	Reason about scale before writing code, against a capacity target and functional requirements	Written design covering data model, service decomposition, storage and caching choices, failure modes and mitigations, and an explicit statement of accepted trade-offs
Production-Grade REST API	Demonstrate engineering craft, not just working code	Tested, documented, reviewable service
Algorithms and Testing Portfolio	Meet the interview bar	Problem set with complexity analysis and full test coverage
Capstone	Demonstrate end-to-end ownership	Complete system with design document

Track 3: AI Application Engineer

Role description. builds product features backed by model inference and retrieval.. Core skills: LLM integration; RAG; evaluation; cost control..

Projects in this track

Project	Objective	Deliverables
AI-Integrated Feature	Embed model inference into an existing service responsibly	Integration with streaming response handling; retrieval layer where grounding is required; prompt and context management; latency and cost instrumentation; evaluation approach for output quality; defined fallback when the model call fails or returns unusable output
Retrieval Layer Build	Ground a feature in the product's own data	Embedding pipeline, vector store, retrieval and reranking inside the application service
Production-Grade REST API	Expose AI capability as a stable product surface	Streaming endpoints, timeout and retry semantics, error contracts for probabilistic failures
Capstone	Ship an AI-backed product feature	Complete system with evaluation and cost envelope

4.3 Projects by module (reference view)

Module	Project work attached
01	Algorithms and Testing Portfolio; repository and workflow setup
02	Production-Grade REST API
03	Relational Data Layer and Query Optimisation
04	NoSQL modelling and caching exercise
05	Asynchronous and Event-Driven Service
06	System Design Exercise
07	AI-Integrated Feature; Retrieval Layer Build
08	Deployment and Observability; Security Hardening; Capstone

4.4 Assignments by module (reference view)

Module	Assignment focus
01	Algorithmic problem set with tests
02	API contract design
03	Schema and index design
04	Access-pattern modelling
05	Queue and worker implementation
06	System design write-up
07	AI integration with cost instrumentation
08	Pipeline, observability and security hardening

4.5 Tests

A module test at the close of each module in the house multiple-correct scenario format. All eight must be attempted for certification.

4.6 Submissions

Project submissions plus module deliverables, reviewed by mentors with written feedback. The capstone is a complete backend system incorporating API, persistence, asynchronous processing, an AI-driven capability and production operational tooling.

4.7 Tools

Tool	Category	Function taught
Cursor	AI code editor	Codebase-aware generation, multi-file refactoring, agentic editing
Claude	AI assistant (Anthropic)	Code generation and review, refactoring, test authoring, documentation; also available as an agentic command-line coding tool
Windsurf	AI development environment	Agentic development workflows and codebase-aware assistance
Google Antigravity	Agent-first development platform	Agent-driven development across editor, terminal and browser surfaces
Amazon Q Developer	AI development assistant	Code generation, code transformation and modernisation, AWS-aware support
v0.dev	Generative UI	Prompt-driven React and Tailwind component generation
Uizard	AI design tool	Wireframe and mockup generation from prompts or sketches
Supabase	Backend platform	Managed PostgreSQL with authentication, storage, realtime and edge functions
Postman	API platform	Collections, environments, contract testing, mock servers, pipeline-integrated test runs
Virtuoso QA	AI test automation	Natural-language authored end-to-end tests and self-healing regression suites

5. Data Engineering with Generative and Agentic AI

Seven months, eight core modules, eight assignments. Microsoft-accredited. Positioned as the fusion of conventional pipeline engineering with generative and agentic AI, treated both as workloads pipelines must now serve and as tooling that automates parts of pipeline construction. Framing: data engineers build the invisible foundation everything else runs on.

5.1 Curriculum

Module	Title	Principal content
01	Python and Advanced SQL	Python for data engineering, Pandas, NumPy, advanced SQL including window functions, CTEs and query optimisation
02	NoSQL and Data Modelling	MongoDB and Cassandra modelling, access-pattern design, dimensional modelling, star schemas, slowly changing dimensions
03	ETL and Orchestration	Apache Airflow DAG design, dependency management, retries and alerting, Apache NiFi ingestion, dbt transformation with testing and documentation, AWS Glue
04	Data Warehousing	BigQuery, Redshift, Snowflake, partitioning and clustering, cost control, lineage and governance
05	Big Data Frameworks	Hadoop ecosystem, PySpark, partitioning and shuffle behaviour, skew handling, distributed computing principles
06	Streaming and Event Processing	Kafka, Flink, Spark Structured Streaming, windowed aggregation, late and out-of-order events, delivery semantics
07	Cloud Ecosystems	AWS (S3, EMR, Glue, Redshift), Azure (Data Factory, Databricks, Synapse), GCP (BigQuery, Pub/Sub), infrastructure as code, access control
08	Generative and Agentic AI, DSA and System Design	LLMs inside pipelines, AI-driven ETL automation, embedding and vector infrastructure, multi-agent orchestration for data workflows, prompt engineering, arrays, trees, graphs, dynamic programming, event-driven platform design, capstone

The Module 08 capstone, an end-to-end platform combining batch and streaming ingestion, warehouse modelling, orchestration, quality monitoring and an AI-serving layer, is common to every track.

5.2 Career tracks

Data Engineering with Generative and Agentic AI — three tracks. Each carries its own projects, assignments and outcomes; every project states the outcome the learner can demonstrate on completion.

Track 1: Data Engineer

Progression: Senior Data Engineer in 3 to 4 years. What the role does: builds and operates the pipelines that move and shape an organisation's data. Core skills: SQL; Python; orchestration; warehousing.

Projects in this track

Project	Objective	Deliverables
Batch ETL Pipeline with Orchestration	Build a scheduled, dependency-aware pipeline that fails safely, ingesting from multiple heterogeneous sources daily	Orchestrated DAG with dependencies, retries and alerting; idempotent load logic; data quality assertions at each stage; backfill capability
Dimensional Warehouse Model	Design the analytical layer	Star schema with conformed dimensions; slowly changing dimension handling; version-controlled transformation logic with tests; documented lineage from source to reporting table
Streaming Pipeline	Process data in motion	Producer and consumer implementation; windowed aggregation; late and out-of-order event handling; justified delivery semantics; consumer lag demonstration under load
Capstone	Ship a working platform	Batch and streaming ingestion, warehouse, orchestration, quality monitoring, AI-serving layer

Assignments in this track: Module 01 (advanced SQL problem set); Module 02 (dimensional and NoSQL modelling); Module 03 (DAG authoring with quality assertions); Module 06 (streaming semantics exercise).

Outcomes. The learner writes idempotent loads so a rerun does not duplicate data; embeds quality assertions so a bad load is caught rather than discovered by an analyst; supports backfill, which is a common production requirement and every production pipeline needs; can be handed a broken DAG and diagnose it.

Track 2: Analytics Engineer

Progression: Analytics Engineering Lead in 3 to 4 years. What the role does: owns the transformation layer between raw data and analytics. Core skills: dbt; dimensional modelling; testing; documentation.

Projects in this track

Project	Objective	Deliverables
Dimensional Warehouse Model	Build the layer analysts actually query	Star schema, conformed dimensions, SCD handling, dbt models with tests and documentation
Data Quality and Testing Framework	Make correctness enforceable rather than hoped for	Assertion suite, freshness and volume tests, alerting on breach, documented ownership
Batch ETL Pipeline with Orchestration	Understand what feeds the transformation layer	Orchestrated pipeline with dependency-aware scheduling
Warehouse Cost and Performance Optimisation	Keep the layer affordable	Partitioning and clustering strategy; query cost analysis; before and after comparison

Assignments in this track: Module 01 (advanced SQL problem set); Module 02 (dimensional and NoSQL modelling); Module 03 (DAG authoring with quality assertions); Module 04 (warehouse tuning and cost analysis).

Outcomes. The learner can model a warehouse an analyst can navigate without asking what a column means; keeps transformation logic in version control with tests, ; can trace any reported number back to its source; can defend a modelling decision against a request that would break conformance.

Track 3: Cloud Data Engineer

Progression: Cloud Data Architect in 4 to 6 years. What the role does: builds data platforms on managed cloud services. Core skills: AWS, Azure and GCP data services; infrastructure as code; cost control.

Projects in this track

Project	Objective	Deliverables
Cloud Data Platform Build	Assemble ingestion, storage, catalogue, transformation and serving on managed services	Deployed stack on a chosen cloud; infrastructure defined as code; access control applied; documented cost controls
Warehouse Cost and Performance Optimisation	Control the constraint that dominates real cloud data work	Partitioning, clustering, slot or credit analysis, measured cost reduction
Batch ETL Pipeline with Orchestration	Operate the platform	Managed orchestration with alerting and retries
Capstone	Deliver a production-shaped platform	Full stack with IaC, governance and cost envelope

Assignments in this track: Module 03 (DAG authoring); Module 04 (warehouse tuning and cost analysis); Module 07 (cloud platform deployment with IaC); Module 01 (advanced SQL).

Outcomes. The learner defines infrastructure as code rather than clicking through consoles, which is ; applies access control at platform level; can state the monthly cost of the platform built and where it would grow.

5.3 Projects by module (reference view)

Module	Project work attached
01	SQL optimisation and Python data processing exercise
02	Dimensional Warehouse Model; NoSQL access-pattern exercise
03	Batch ETL Pipeline with Orchestration; Data Quality and Testing Framework
04	Warehouse Cost and Performance Optimisation
05	Distributed Processing at Scale
06	Streaming Pipeline
07	Cloud Data Platform Build; Platform Reliability and Governance
08	AI-Integrated Pipeline; Agentic Data Workflow; Capstone

5.4 Assignments by module (reference view)

Module	Assignment focus
01	Advanced SQL problem set
02	Dimensional and NoSQL modelling
03	DAG authoring with quality assertions
04	Warehouse tuning and cost analysis
05	PySpark optimisation
06	Streaming semantics exercise
07	Cloud platform deployment with infrastructure as code
08	Agentic workflow and vector pipeline

5.5 Tests

A module test at the close of each module in the house multiple-correct scenario format. All eight must be attempted for certification.

5.6 Submissions

Project submissions plus module deliverables, reviewed by mentors with written feedback.

5.7 Tools

Layer	Tools
Languages and stores	Python, SQL, MongoDB, Apache Cassandra
Orchestration and transformation	Apache Airflow, Apache NiFi, dbt, AWS Glue
Warehousing	Google BigQuery, Amazon Redshift, Snowflake
Distributed processing	Apache Hadoop, PySpark
Streaming	Apache Kafka, Apache Flink, Spark Structured Streaming
Cloud	AWS (S3, EMR, Glue, Redshift), Azure (Data Factory, Databricks, Synapse), GCP (BigQuery, Pub/Sub)
Generative and agentic AI	LLM APIs, vector databases, agent frameworks

6. Business Analytics

Six months, eight core modules, eight assignments. For professionals who will occupy the interface between business stakeholders and technical delivery. Theme: the AI-augmented analyst who converts data into decisions and uses AI tooling to compress the mechanical parts of that work. No prior coding background required.

Distinct from Data Analytics: Business Analytics is weighted towards requirements, process, stakeholder management and decision frameworks; Data Analytics towards the analytical and statistical production of insight.

6.1 Curriculum

Six skill pillars

Pillar	Coverage
Data Foundations	Business models and value chains, KPI definition and hierarchy, metric trees, first-principles decomposition
Excel and BI Analytics	Advanced Excel modelling, Power Query, DAX, Power BI and Tableau dashboards, executive reporting
Statistics and Predictive Models	Descriptive and inferential statistics, hypothesis testing, regression, forecasting, experiment design
Industry Use Cases	Applied problems across BFSI, e-commerce, healthcare, supply chain and SaaS
Data Visualisation	Chart selection, visual encoding, dashboard information architecture, narrative structure
Stakeholder Communication	Requirements elicitation, BRD and FRD authoring, BPMN process mapping, user stories, Agile ceremonies, stakeholder alignment

Module sequence

Module	Title
01	Business Foundations, KPIs and Metric Architecture
02	Advanced Excel and Data Preparation
03	SQL and Data Querying for Analysts
04	Business Intelligence: Power BI and Tableau
05	Statistics, Hypothesis Testing and Predictive Modelling
06	Requirements, Process Mapping and Agile Delivery
07	Industry Use Cases and Decision Frameworks
08	Stakeholder Communication, Capstone and Career Readiness

Long-form case studies on Indian and global companies run throughout, each accompanied by a comprehension paper testing extraction and application of the case's analytical logic rather than recall of its narrative. The Module 08 capstone is common to every track.

6.2 Career tracks

Business Analytics — three tracks. Each carries its own projects, assignments and outcomes; every project states the outcome the learner can demonstrate on completion.

Track 1: Business Analyst

Progression: Senior or Lead BA in 3 to 4 years. What the role does: translates business need into implementable specification and validates delivery. Core skills: requirements; BPMN; user stories; stakeholder management.

Projects in this track

Project	Objective	Deliverables
Requirements and Process Documentation Pack	Turn an ambiguous brief with conflicting commercial, operations and technology objectives into an implementable specification	BRD; FRD; BPMN 2.0 as-is and to-be process map with swimlanes; prioritised user story backlog with acceptance criteria; stakeholder register with engagement plan
Root Cause Investigation	Diagnose a KPI that has declined over two quarters	MECE issue tree; quantified contribution analysis across segments; Ishikawa and five whys documentation; recommendation memorandum
KPI Architecture and Executive Dashboard	Define what the business should measure	Metric tree; definitions and calculation logic; Power BI dashboard with drill-through
Capstone	Run a full analysis cycle	Requirements, analysis, recommendation and stakeholder presentation

Assignments in this track: Module 01 (KPI definition); Module 03 (SQL extraction); Module 06 (requirements authoring); Module 08 (stakeholder communication).

Outcomes. The learner can run a requirements workshop and leave with a specification rather than a list of wishes; can map an as-is process and defend a to-be design; can write acceptance criteria a developer can build against and a tester can verify; can distinguish a cause from a correlate under time pressure.

Track 2: Data-Driven Product Manager

Progression: Senior PM in 3 to 5 years. What the role does: decides what to build using evidence rather than opinion. Core skills: metric trees; experiment design; prioritisation.

Projects in this track

Project	Objective	Deliverables
Funnel, Cohort and Growth Analysis	Quantify acquisition through retention	Full funnel with stage conversion; cohort retention curves; unit economics including LTV to CAC and payback period
KPI Architecture and Executive Dashboard	Define the product's success metrics	Metric tree linking outcome to input drivers; dashboard with drill-through
Experiment Design and Prioritisation	Decide what to build next on evidence	Hypothesis, success metric, experiment design, prioritised roadmap with rationale
Capstone	Own a product decision end to end	Analysis, recommendation, roadmap and executive presentation

Assignments in this track: Module 01 (KPI definition); Module 04 (dashboard construction); Module 05 (statistical testing); Module 07 (decision framework application).

Outcomes. The learner can build a metric tree that connects a headline number to the levers that move it; can state whether growth is sustainable rather than only whether it is fast; can design an experiment that will answer the question rather than confirm the preference; can say no to a feature with evidence.

Track 3: BI Developer

Progression: BI Lead or Analytics Manager in 3 to 4 years. What the role does: builds and owns the reporting layer an organisation runs on. Core skills: SQL; DAX; Power BI; Tableau; data modelling.

Projects in this track

Project	Objective	Deliverables
KPI Architecture and Executive Dashboard	Build the standing management reporting layer	Governed data model; metric definitions and calculation logic; Power BI dashboard with drill-through from summary to transaction detail
Multi-Source Data Model	Make disparate sources queryable together	Relationship model, calculated measures in DAX, refresh and performance design
Tableau Executive Reporting	Build for an audience that will not ask questions	Dashboard information architecture, chart selection, narrative sequencing
Capstone	Deliver a reporting suite	End-to-end model, dashboards and documentation

Assignments in this track: Module 02 (Excel preparation and modelling); Module 03 (SQL extraction); Module 04 (dashboard construction); Module 08 (stakeholder communication).

Outcomes. The learner can build reporting a management committee will actually run on; can answer why a number moved rather than only reporting that it did; can design for performance so a dashboard does not time out at month end; can document a model so it survives their departure.

6.3 Projects by module (reference view)

Module	Project work attached
01	Metric tree and KPI definition exercise
02	Advanced Excel preparation and modelling; Financial Model and Scenario Build
03	SQL querying and extraction; Multi-Source Data Model
04	KPI Architecture and Executive Dashboard; Tableau Executive Reporting; Operational KPI Dashboard
05	Risk and Sensitivity Analysis; A/B Test Design and Analysis
06	Requirements and Process Documentation Pack; Process Mapping and Redesign
07	Root Cause Investigation; Funnel, Cohort and Growth Analysis; Campaign Attribution and ROI Analysis
08	Company case studies with comprehension papers; capstone and stakeholder presentation

6.4 Assignments by module (reference view)

Module	Assignment focus
01	KPI definition and metric tree construction
02	Excel data preparation and modelling
03	SQL extraction and joining
04	Dashboard construction in Power BI and Tableau
05	Statistical testing and interpretation
06	Requirements authoring and BPMN process mapping
07	Decision framework application to an industry case
08	Stakeholder communication and presentation

6.5 Tests

Eighteen assessment papers across six assessed modules and three role tracks (Analyst; Product and Consulting; Risk and Financial Analyst). Each paper carries 30 multiple-correct questions inside a scenario wrapper built around a fictional company, typically framed as inheriting a departed analyst's folder against a 72-hour board deadline. Four lettered statements per question, no fixed number of correct options, credit only for the complete correct set. The same statement bank is reused across tracks within a module with option order shuffled per track. A consolidated answer key documents, per question, the correct set and why each remaining option fails.

6.6 Submissions

Project submissions plus case study comprehension papers and module deliverables, reviewed by mentors with written feedback.

6.7 Tools

Tool	Category	Function taught
ThoughtSpot	Search and AI analytics	Natural-language querying of governed datasets, self-service Liveboards
KNIME	Visual data science and ETL	Node-based preparation, blending and analytical workflows without code
Microsoft Fabric	Unified analytics platform	Lakehouse storage, pipelines, semantic modelling, Power BI integration
MonkeyLearn	Text analytics	No-code classification, sentiment analysis, keyword extraction ‡
Notion AI	Documentation and knowledge work	Requirement drafting, meeting synthesis, workspace question answering
Lucidchart	Diagramming	BPMN maps, swimlanes, ERDs, system context diagrams
Kissflow	Low-code workflow automation	Process digitisation, approval workflow design
PolymerSearch	Automated data exploration	Rapid conversion of spreadsheets into interactive exploratory dashboards
Core stack	Analyst tooling	Excel, SQL, Power BI, Tableau

‡ MonkeyLearn was acquired by Medallia in 2022 and its standalone platform has since been discontinued. Retention should be reviewed at the next refresh.

7. Data Analytics

Six months, eight core modules, eight assignments, 18 hands-on projects. Deliberately business-first rather than tool-first: statistical and technical method is introduced in service of a commercial question rather than as a subject in itself. The differentiating claim is portfolio proof, on the basis that the learner finishes holding a set of real analyst deliverables rather than a completion certificate alone.

7.1 Curriculum

Module	Title	Principal content
01	Business Foundations and Economic Theory	Excel foundations and what-if analysis, Excel for finance including TVM, NPV and IRR, ratio analysis, supply chain foundations including the bullwhip effect and lead time, workforce analytics and OEE, macro and microeconomics, economic indicators, Python foundations with NumPy, Pandas and Seaborn
02	Statistical Concepts and Business Intelligence	Descriptive and inferential statistics, central limit theorem, confidence intervals, hypothesis testing and p-values, correlation against causation, statistical process control and control charts, Tableau dashboarding, Power BI with Power Query and DAX
03	SQL, Data Modelling and BI Integration	SQL fundamentals, joins and relational algebra, ER modelling, normalisation to third normal form, connecting SQL sources to Tableau and Power BI
04	Financial Analysis and Reporting	Financial statement analysis, vertical and horizontal analysis, cash flow forecasting, DuPont decomposition, drill-through dashboards, time series, ARIMA, SARIMA and GARCH, seasonality and volatility, the Pyramid Principle
05	Financial and Marketing Theorems	Customer journey mapping and funnel quantification, Modigliani-Miller, CAPM, arbitrage pricing theory, Black-Scholes, efficient market hypothesis, pricing strategy and the 7Ps, dynamic, penetration and skimming pricing, MECE, Ishikawa and five whys, BPMN 2.0 and swimlanes, segmentation, targeting and positioning
06	Risk Analysis and Product Management	Risk identification frameworks, quantitative risk and sensitivity analysis, Monte Carlo simulation, EOQ and safety stock, supply chain single point of failure analysis, decision trees and expected value, Six Sigma and Lean DMAIC, waste reduction, feature adoption and retention analysis, product lifecycle and roadmap
07	Growth and Strategy Modelling	AARRR pirate metrics, unit economics including LTV, CAC and contribution margin, BRD against FRD, Agile and Scrum, process mapping, business mathematics, cohort analysis, market basket analysis, game theory and Nash equilibrium, virality and K-factor
08	Strategic Synthesis, Capstone and Career Readiness	Integrated analysis, stakeholder presentation, portfolio consolidation, interview preparation

Project distribution

The 18 projects are distributed across four industry domains and five tool configurations.

Dimension	Count		Dimension	Count
BFSI	5		Excel-based	4
Supply chain	5		Power BI-based	4
HR and operations	4		SQL-based	4
Marketing and advertising	4		Tableau-based	3
			Combined BI and SQL	3

The Module 08 capstone is common to every track.

7.2 Career tracks

Data Analytics — three tracks. Each carries its own projects, assignments and outcomes; every project states the outcome the learner can demonstrate on completion.

Track 1: Data Analyst

Progression: Senior or Lead Analyst in 3 to 4 years. What the role does: turns raw data into decisions for an operating business. Core skills: SQL; Excel; Python; visualisation.

Projects in this track

Project	Objective	Deliverables
Business KPI Dashboard	Build the standing management reporting layer	Governed data model; defined metric logic; dashboard with drill-through from executive summary to transaction detail
ARIMA and SARIMA Forecast	Produce a defensible forward forecast with quantified uncertainty	Stationarity testing and differencing; model order selection; residual diagnostics; forecast with confidence intervals; written statement of what the model can and cannot be relied upon to predict
Financial Statement Analysis	Read a business from its accounts	Vertical and horizontal analysis; DuPont decomposition; cash flow forecast
Capstone	Run a full analysis cycle	Data acquisition, modelling, visualisation and stakeholder presentation

Assignments in this track: Module 01 (Excel and Python foundations); Module 02 (statistical inference); Module 03 (SQL extraction and ER modelling); Module 04 (time series and financial analysis).

Outcomes. The learner can go from a raw extract to a defended recommendation without help; can quantify uncertainty rather than presenting a single number; can explain a forecast's limits to someone who will act on it; leaves with a portfolio of real deliverables rather than a certificate.

Track 2: BI Developer

Progression: BI Lead or Analytics Manager in 3 to 4 years. What the role does: builds and owns the organisation's reporting layer. Core skills: DAX; LOD expressions; data modelling; dashboards.

Projects in this track

Project	Objective	Deliverables
Business KPI Dashboard	Build reporting the business runs on	Data model, metric logic, Power BI dashboard with drill-through
Drill-Through Financial Reporting Suite	Serve finance and operations from one model	Layered dashboards from board summary to line item; DAX measures; performance tuning
SQL-to-BI Integration Project	Connect governed sources to the reporting layer	ER model; SQL views; live connections into Tableau and Power BI
Capstone	Deliver a reporting suite	Model, dashboards and documentation

Assignments in this track: Module 02 (BI dashboarding); Module 03 (SQL extraction and ER modelling); Module 04 (drill-through reporting); Module 08 (portfolio consolidation).

Outcomes. The learner can model data so a dashboard performs at production volume; writes DAX and LOD expressions rather than pre-aggregating in the source; can hand over a documented model; can build the single source of truth an organisation stops arguing about.

Track 3: Marketing and Growth Analyst

Progression: Growth Lead in 3 to 4 years. What the role does: quantifies acquisition, retention and campaign return. Core skills: AARRR; attribution; market basket analysis.

Projects in this track

Project	Objective	Deliverables
AARRR Cohort Analysis	Measure the full growth funnel	Funnel, cohorts, LTV to CAC, payback
Market Basket Analysis	Find what sells with what	Association rules with support, confidence and lift; cross-sell and bundling recommendations
Pricing Strategy Analysis	Set price on evidence	Elasticity estimate; dynamic, penetration and skimming comparison; margin impact
Capstone	Present a growth strategy	Analysis, recommendation and executive presentation

Assignments in this track: Module 05 (customer journey, pricing and segmentation); Module 07 (cohort, market basket and unit economics); Module 02 (statistical inference); Module 04 (reporting).

Outcomes. The learner can quantify a funnel rather than describe it; can produce cross-sell recommendations with statistical support; can defend a price change with elasticity evidence; can tell which channel to cut.

Projects in this track

Project	Objective	Deliverables
Strategic Synthesis Report	Turn a body of analysis into a decision document	Pyramid Principle structure; supporting evidence hierarchy; explicit recommendation and the conditions under which it changes
Business KPI Dashboard	Give the narrative a standing evidence base	Metric model and executive dashboard
Root Cause and Contribution Analysis	Explain a movement rather than report it	MECE decomposition; quantified segment contribution; Ishikawa and five whys
Capstone	Present to a decision-making audience	Full analysis, written report and live presentation

Assignments in this track: Module 04 (Pyramid Principle and reporting); Module 05 (MECE and root cause); Module 07 (strategy modelling); Module 08 (stakeholder presentation).

Outcomes. The learner can lead with the answer and support it, rather than narrating the analysis chronologically; can select the chart that carries the point; can write a recommendation an executive can act on without a follow-up meeting.

7.3 Projects by module (reference view)

Module	Project work attached
01	Excel financial analysis; economic indicator analysis; Python exploratory analysis
02	Statistical inference exercise; control chart study; first Tableau and Power BI dashboards
03	SQL extraction and ER modelling; SQL-to-BI Integration Project
04	Business KPI Dashboard; ARIMA and SARIMA Forecast; Financial Statement Analysis; Drill-Through Financial Reporting Suite
05	Customer Journey and Funnel Quantification; Pricing Strategy Analysis; BPMN process map
06	Monte Carlo Risk Simulation; EOQ and Inventory Policy; Six Sigma DMAIC Process Audit; Feature Adoption and Retention Analysis
07	AARRR Cohort Analysis; Market Basket Analysis; unit economics model
08	Strategic Synthesis Report; capstone and stakeholder presentation

7.4 Assignments by module (reference view)

Module	Assignment focus
01	Excel, economics and Python foundations
02	Statistical inference, SPC and BI dashboarding
03	SQL extraction and ER modelling
04	Time series, financial analysis and Pyramid Principle reporting
05	Customer journey, pricing and segmentation
06	Risk, EOQ, DMAIC and product analysis
07	Cohort, market basket and unit economics
08	Strategic synthesis and stakeholder presentation

7.5 Tests

A module test at the close of each module in the house multiple-correct scenario format, with difficulty escalating across the sequence. All eight must be attempted for certification.

7.6 Submissions

Eighteen project submissions plus the capstone, reviewed by mentors with written feedback.

7.7 Tools

Analyst core stack: Excel, SQL, Power BI, Tableau, Python. AI tool layer: ThoughtSpot, KNIME, Microsoft Fabric, MonkeyLearn ‡, Notion AI, Lucidchart, Kissflow.

Company timeline

Date	Event
2 August 2023	Meritshot Zetta Edutech Private Limited founded and incorporated in Gautam Buddha Nagar, Uttar Pradesh
2023–2024	Initial programme launches; Noida operations established; first cohorts delivered
2024–2025	Portfolio expansion across finance, data, engineering and security; accreditation partnerships established with Cisco and Microsoft; NSDC-aligned certification through Medhavi Skills University
April 2026	ANI coverage of the company's activity in the Indian professional education market

Glossary

Terms used in this article that carry a specific meaning within Meritshot's programme architecture.

Term	Meaning
Assignment	The graded practice layer attached to a module. One per module, eight in total across most programmes and nine in Investment Banking. Required for completion.
Test	The module-closing assessment, in multiple-correct format with four lettered statements per question, no fixed number of correct options, and credit only for the complete correct set.
Submission	A portfolio project deliverable submitted through the learner portal, reviewed by a mentor and returned with written feedback for revision.
Career track	A role-specific path within a programme carrying its own projects, assignments and outcomes. Determines specialisation weighting in the second half of the programme.
Assessment track	A role-specific variant of the module test papers, distinct from the career track. Cyber Security uses three (Security Operations; Ethical Hacking; GRC), as does Business Analytics (Analyst; Product and Consulting; Risk and Financial Analyst).
Elective	A specialised subject chosen within a programme, such as Cloud Security or DevSecOps in the Cyber Security track.
Foundation Track / Beginner Path	In Cyber Security, the first four modules across sixteen weeks.
Advanced Path	In Cyber Security, the full eight-module, thirty-four-week structure.
Capstone	The integrating deliverable in the final module, common to every career track within a programme.
Six-month refresh	The standing policy under which every programme curriculum is rewritten every six months.
Scenario wrapper	The narrative frame around a test paper, built on a fictional organisation, in which the learner inherits an incomplete analysis under a compressed deadline.
Skills assessment score	The performance measure derived from assignment and test results and printed on the certificate.