Investment Case Study Portfolio

Create a reusable GitHub repository structure for data, models, applications, reports, presentations, and sources.

Select a sector whose relevance, scope, and public-data availability support a credible portfolio study.

Define transparent inclusion criteria for selecting 12–15 companies in a sector landscape.

Design a structured company-data schema covering business model, ownership, funding, investors, competitors, transactions, and disclosed financials.

Extract a source-linked company profile from public filings, company materials, databases, and reputable reporting.

Normalize inconsistent company descriptions, currencies, dates, funding stages, and financial fields for comparison.

Classify companies by business-model and competitive-positioning archetypes.

Create Python validation checks that flag missing values, duplicate records, invalid categories, and unsupported claims.

Assign confidence levels to private-company data without converting estimates into apparent facts.

Derive evidence-based sector trends from the structured company dataset.

Construct a competitive landscape that distinguishes direct competitors, substitutes, and adjacent players.

Identify strategically relevant funding patterns, ownership changes, partnerships, and acquisitions in the sector.

Build an Excel dashboard with decision-relevant filters, charts, summary metrics, and source references.

Write an executive summary that separates findings, interpretations, and recommendations.

Structure a four-page research note around market context, company landscape, trends, risks, and implications.

Package the dataset, workbook, research note, methodology, and limitations as a reproducible GitHub release.

Write a defensible resume bullet and a two-minute interview explanation based only on the completed work.

Map how a company creates, delivers, and captures value using a business-model framework.

Build a KPI tree linking operating drivers to revenue, margins, cash flow, and enterprise value.

Calculate and interpret sector-appropriate unit-economics metrics from available company data.

Reconcile the income statement, balance sheet, and cash-flow statement for analytical use.

Normalize reported financials for material non-recurring or non-operating items.

Decompose historical growth into volume, price, mix, acquisition, and market-share drivers where disclosures permit.

Identify financial and operating red flags that require further diligence.

Translate a business narrative into explicit revenue, margin, reinvestment, and cash-flow assumptions.

Construct a driver-based forecast of revenue and operating profit.

Calculate unlevered free cash flow from forecast financial statements.

Estimate a defensible discount rate using cost of equity, cost of debt, capital structure, and country-risk inputs.

Calculate terminal value using both perpetuity-growth and exit-multiple methods.

Bridge enterprise value to equity value while accounting for debt, cash, investments, and other relevant claims.

Build two-way sensitivity tables for key DCF assumptions.

Diagnose a DCF model using accounting checks, economic plausibility tests, and comparison with market values.

Specify the inputs, outputs, calculation boundaries, and error states of a reusable valuation tool.

Implement a transparent DCF calculation engine in Python.

Write tests for discounting, terminal value, enterprise-to-equity bridges, and invalid input handling.

Reconcile Python valuation outputs with an independently constructed spreadsheet model.

Build a Streamlit interface for base, upside, and downside valuation scenarios.

Add sensitivity visualizations and downloadable assumption and result tables to the application.

Document methodology, limitations, installation steps, and sample inputs for an external reviewer.

Reconstruct an acquisition timeline and key transaction terms from public disclosures.

Evaluate an acquisition's strategic rationale against market structure and the buyer's stated priorities.

Analyze the buyer's and target's standalone performance using only sufficiently disclosed figures.

Calculate transaction valuation multiples only when numerator and denominator definitions are compatible.

Develop a bottom-up estimate of revenue, cost, and capital synergies with explicit timing assumptions.

Model a simplified pro forma earnings accretion or dilution scenario under alternative financing assumptions.

Assess integration, execution, regulatory, competitive, and valuation risks using scenario analysis.

Create a sourced six-slide transaction presentation and one-page executive summary with a clear conclusion.

Convert an ambiguous business situation into a precise problem statement and decision criterion.

Build a mutually exclusive, collectively exhaustive issue tree for a growth or profitability question.

Estimate market size using both top-down and bottom-up methods and reconcile the results.

Benchmark a company's performance and strategic position against relevant peers.

Generate distinct growth options across core expansion, adjacency, market entry, partnership, and acquisition.

Prioritize strategic options using quantified impact, feasibility, risk, and implementation timing.

Present an evidence-based recommendation with implementation steps, milestones, risks, and contingencies.

Frame a private-equity investment thesis around value-creation levers and disconfirming evidence.

Estimate normalized EBITDA and cash conversion from public company information.

Select a defensible entry valuation using trading, transaction, and fundamental valuation evidence.

Construct a simplified debt schedule with interest, mandatory repayment, and optional cash sweeps.

Model operating cash flow, debt paydown, and exit equity value over the holding period.

Calculate MOIC and IRR across entry, operating, leverage, and exit assumptions.

Identify the assumptions that create downside loss or prevent achievement of a target return.

Write a concise investment-committee memo that distinguishes thesis, evidence, risks, diligence needs, and recommendation.

Evaluate a startup market using customer need, market structure, adoption drivers, and competitive intensity.

Map a startup's revenue model to acquisition, retention, monetization, and gross-margin drivers.

Calculate CAC, LTV, payback period, burn multiple, runway, and related unit-economics metrics when data permits.

Construct a pre-money and post-money cap table for a priced funding round.

Model founder and investor dilution across multiple funding rounds and an option-pool increase.

Translate ownership and exit assumptions into investor proceeds and fund-return implications.

Write a VC-style investment memo that clearly labels verified facts, estimates, assumptions, and unanswered diligence questions.

Select a balanced portfolio of cases that demonstrates strategy, transaction, valuation, investing, and technical capabilities.

Audit every material claim, chart, and model input for citation quality and reproducibility.

Standardize visual design, file naming, model conventions, and disclaimers across the repository.

Create a portfolio landing page that explains each project's decision question, methods, outputs, and limitations.

Connect Streamlit tools to sanitized sample data without exposing credentials or restricted information.

Write accurate resume bullets that quantify completed work without overstating professional involvement.

Deliver a five-minute walkthrough of a case study from question through recommendation.

Defend key assumptions under interviewer challenges and explain what evidence would change the conclusion.