DTS 4262 units400 LevelSecond Semester

Data Science Innovation and Entrepreneurship

B.Sc. Data Science, University of Uyo

Entrepreneurship specific to data: what makes a data product commercially viable, how data businesses are structured, and how to price, protect and scale an offering built on data and models.

On Areté, DTS 426 comes with

  • 2 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
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Course outline

  1. 01The data product landscape: analytics services, embedded models, data marketplaces, decision tools
  2. 02Identifying commercially viable data opportunities; the value of a prediction
  3. 03Data as an asset: acquisition, ownership, licensing and the data moat
  4. 04Business models for data products: subscription, usage-based, freemium, marketplace
  5. 05Costing a data product: infrastructure, compute, storage and human-in-the-loop costs
  6. 06Building an MVP for a data product; when a spreadsheet is the right first version
  7. 07Intellectual property for data and models; trade secrets vs patents
  8. 08Regulatory constraints on data businesses in Nigeria
  9. 09Scaling: from a one-off analysis to a repeatable, automated service

Recommended textbooks

  • Data Science for Business — Foster Provost & Tom Fawcett

    The core text for thinking commercially about data

  • Prediction Machines — Agrawal, Gans & Goldfarb

    On the economics of AI — what falling prediction cost changes about a business

How to pass DTS 426

  • The central question of this course is "who pays, and why?" — a technically excellent model with no buyer is a hobby
  • Prediction Machines' framing (AI reduces the cost of prediction; judgement becomes the scarce input) is a genuinely useful lens for essay answers
  • Cost out the infrastructure for your idea honestly; cloud compute has bankrupted more data startups than bad models have
  • Combine this with ENT 321 and DTS 428 — one coherent venture built on your project is worth far more than three disconnected submissions

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