Coursera, Inc (COUR) Fair Value & Analysis
Consumer Defensive · US · Market cap $1.5B
Analysis
Coursera, Inc (COUR) currently trades at $5.41, while our model-based Fair Value estimate is $8.88 — implying the stock looks roughly 64.1% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Defensive sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Coursera, Inc. operates an online learning platform that provides education and skills training in the United States, Europe, the Middle East, Africa, the Asia Pacific, and internationally. It operates through Consumer and Enterprise segments. The company offers guided projects, courses, and specializations; online bachelor's and master's degrees; postgraduate diplomas; and certificates for entry-level professional, non-entry level professional, university, and MasterTrack programs in the domains of business, computer science, technology, and data science through Coursera.org for Individuals, Coursera Plus, Coursera for Enterprise, Coursera for Business, Coursera for Campus, and Coursera for Government. It offers its products to individuals, businesses, institutions, employers, colleges and universities, organizations, and governments. The company was formerly known as Dkandu, Inc. and changed its name to Coursera, Inc. in April 2012. Coursera, Inc. was incorporated in 2011 and is h…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.