Entrepreneurship In A.I. and Data Science

Course info:

Semester: 7

Elective

ECTS: 6

Hours per week: 2

Professor: T.B.D.

Teaching style: Face to face, project work

Grading: Final Project (business building) (70%), Class presentation (30%)

Activity Workload
Lectures 26
Tutorials 13
Group work on Laboratory projects 48
Individual study 63
Course total 150

Learning Results

This course will introduce the notions innovation and entrepreneurship, principles of operation of successful high-tech enterprises, customer identification and validation, product development, business models, lean startup techniques, and financing of high-technology ventures in the fields of AI and Data Science. Students will work in teams to develop their own innovative product idea, and will produce a sound business plan to support their product.

Upon completion of the course the students will know how:

  • To turn an idea into a business
  • Create value proposition statements for a business
  • Identify specific needs of AI-focus business
  • To do networking and talk to potential investors for funding opportunities
  • Build a “fictitious” company in the fields of AI/Data Science
  • Use tools, such as the Business Model Canvas for developing business models
  • Understand the specific needs of productizing AI/Data Science methods

Skills acquired

  • Search, analysis and synthesis of data and information, using the necessary technologies
  • Learn how to speak in a professional environment
  • Individual work
  • Group work
  • Work in an multi-disciplinary environment
  • Work on new case studies
  • Creative, business and critical thinking
  • Starting a business: Identify, asses, and communicate an idea related to AI/Data Science
  • Customer Discovery and Market Analysis
    • Is your problem AI/Data Science related ?
    • Is there enough competition/room for a new idea ?
  • Pitching a business idea
  • Operational issues (annual plan, keeping logs, etc.)
  • Funding
  • Business plan creation
  • Networking in the right places and with the right people
  • Intellectual property strategy
  • Building a team
    • Identifying people with an AI/Data Science background
  • Financial matters
  1. G. Kawasaki, The art of the start, v2.0, Portfolio Inc., 2015

  2. B. Barringer, Preparing effective business plans: An entrepreneurial approach, Pearson/Prentice Hall, 2009

  3. Eric Ries, The Lean Startup, Currency, 2011

  4. A. Osterwalder, Y.Pigneur, Business Model Generation, Wiley, 2010

  5. A. Osterwalder, Y.Pigneur, G. Barnarda, Value Proposition Design: How to Create Products and Services Customer Want, Wiley, 2014

  6. A. Fontana, The AI-First Company, Protfolio, 2021

  7. T. Davenport, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, MIT Press, 2019

Learning Results - Skills acquired

Learning Results

This course will introduce the notions innovation and entrepreneurship, principles of operation of successful high-tech enterprises, customer identification and validation, product development, business models, lean startup techniques, and financing of high-technology ventures in the fields of AI and Data Science. Students will work in teams to develop their own innovative product idea, and will produce a sound business plan to support their product.

Upon completion of the course the students will know how:

  • To turn an idea into a business
  • Create value proposition statements for a business
  • Identify specific needs of AI-focus business
  • To do networking and talk to potential investors for funding opportunities
  • Build a “fictitious” company in the fields of AI/Data Science
  • Use tools, such as the Business Model Canvas for developing business models
  • Understand the specific needs of productizing AI/Data Science methods

Skills acquired

  • Search, analysis and synthesis of data and information, using the necessary technologies
  • Learn how to speak in a professional environment
  • Individual work
  • Group work
  • Work in an multi-disciplinary environment
  • Work on new case studies
  • Creative, business and critical thinking
Course content
  • Starting a business: Identify, asses, and communicate an idea related to AI/Data Science
  • Customer Discovery and Market Analysis
    • Is your problem AI/Data Science related ?
    • Is there enough competition/room for a new idea ?
  • Pitching a business idea
  • Operational issues (annual plan, keeping logs, etc.)
  • Funding
  • Business plan creation
  • Networking in the right places and with the right people
  • Intellectual property strategy
  • Building a team
    • Identifying people with an AI/Data Science background
  • Financial matters
Recommended bibliography
  1. G. Kawasaki, The art of the start, v2.0, Portfolio Inc., 2015

  2. B. Barringer, Preparing effective business plans: An entrepreneurial approach, Pearson/Prentice Hall, 2009

  3. Eric Ries, The Lean Startup, Currency, 2011

  4. A. Osterwalder, Y.Pigneur, Business Model Generation, Wiley, 2010

  5. A. Osterwalder, Y.Pigneur, G. Barnarda, Value Proposition Design: How to Create Products and Services Customer Want, Wiley, 2014

  6. A. Fontana, The AI-First Company, Protfolio, 2021

  7. T. Davenport, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, MIT Press, 2019