University of West Attica
Artificial Intelligence 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
-
G. Kawasaki, The art of the start, v2.0, Portfolio Inc., 2015
-
B. Barringer, Preparing effective business plans: An entrepreneurial approach, Pearson/Prentice Hall, 2009
-
Eric Ries, The Lean Startup, Currency, 2011
-
A. Osterwalder, Y.Pigneur, Business Model Generation, Wiley, 2010
-
A. Osterwalder, Y.Pigneur, G. Barnarda, Value Proposition Design: How to Create Products and Services Customer Want, Wiley, 2014
-
A. Fontana, The AI-First Company, Protfolio, 2021
-
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
-
-
G. Kawasaki, The art of the start, v2.0, Portfolio Inc., 2015
-
B. Barringer, Preparing effective business plans: An entrepreneurial approach, Pearson/Prentice Hall, 2009
-
Eric Ries, The Lean Startup, Currency, 2011
-
A. Osterwalder, Y.Pigneur, Business Model Generation, Wiley, 2010
-
A. Osterwalder, Y.Pigneur, G. Barnarda, Value Proposition Design: How to Create Products and Services Customer Want, Wiley, 2014
-
A. Fontana, The AI-First Company, Protfolio, 2021
-
T. Davenport, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, MIT Press, 2019
-