Private Market Analytics : Global Primary & Secondary Market

Learn how to analyse global private markets using Excel, Python, and Streamlit — from private equity and venture capital to private credit, valuation, fund performance, secondary markets, and risk analytics.

This comprehensive, hands-on course is designed to give you a practical understanding of the global private markets ecosystem and the analytical tools used to evaluate private-market investments.

You will begin by understanding how private markets differ from public markets and explore the global ecosystem across Private Equity, Venture Capital, Private Credit, Infrastructure, and Real Assets. You will learn about LPs, GPs, institutional investors, fund lifecycles, capital flows, and the role of primary and secondary markets.

The course then takes you into Primary vs Secondary Markets, covering primary capital formation, secondary transactions, LP-led and GP-led secondaries, continuation vehicles, pricing, liquidity, information asymmetry, the J-Curve, portfolio construction, and secondary-market growth.

You will develop a deeper understanding of Private Equity and Venture Capital, including buyouts, growth equity, venture stages, LBOs, MBOs, minority and control investments, due diligence, investment theses, term sheets, value creation, and exit strategies.

The course also covers Private Credit and Alternative Assets, including direct lending, mezzanine finance, venture debt, distressed debt, special situations, asset-based lending, infrastructure, real estate, project finance, credit risk, recovery analysis, yield analysis, and covenants.

You will learn how private-market deals are structured, including SPVs, fund structures, commitments, capital calls, management fees, carried interest, hurdle rates, distribution waterfalls, and deal-level versus fund-level returns.

The valuation section covers comparable company analysis, precedent transactions, DCF, cost of capital, terminal value, LBO valuation, early-stage valuation, discounts, premiums, and fair value.

You will also learn how to measure and interpret private-market performance using IRR, XIRR, MOIC, DPI, RVPI, TVPI, PME, benchmarking, quartiles, vintage analysis, pacing, and fee drag.

The course then moves into Secondary Market Analytics and Risk Analytics, covering NAV discounts, buyer return drivers, liquidity risk, funding risk, concentration, diversification, leverage, operational risk, volatility, drawdowns, and stress testing.

A major focus is practical analytics. You will use Python and Excel to analyse cash flows, calculate returns, build valuation models, perform Monte Carlo simulations, analyse portfolios, and develop reproducible private-market analytics workflows.

You will then build an interactive Streamlit dashboard, transforming private-market analysis into a practical application with KPI tiles, interactive charts, fund drilldowns, portfolio views, simulations, and deployment.

Finally, you will complete a comprehensive Private Markets Capstone Project, bringing together valuation, performance analysis, benchmarking, secondary pricing, risk assessment, investment recommendations, Python analysis, Excel modelling, and an interactive decision dashboard. Private_Markets_Analytics_Sylla…

By the end of this course, you will be able to:

• Understand the global private markets ecosystem

• Analyse primary and secondary private markets

• Evaluate private equity and venture capital investments

• Understand private credit and alternative assets

• Structure private-market deals and fund economics

• Value private companies and assets

• Analyse secondary-market pricing and returns

• Measure private fund performance using IRR, MOIC, TVPI, DPI, and PME

• Assess liquidity, concentration, leverage, and other private-market risks

• Build private-market analytics using Python and Excel

• Develop interactive Streamlit dashboards

• Perform Monte Carlo and portfolio simulations

• Build a complete private-markets investment analysis

• Present a defensible investment recommendation backed by data

The course is designed around practical application: concepts are explained first and then implemented using Excel and Python, allowing you to develop both investment reasoning and technical analytics skills.