Motivation
«Many organizations focus on the need for data scientists. But another equally vital role is that of the business translator who can serve as the link analytical talentand practical applicationsto business questions.»
Target of the course
- No technicalities or programming skills
- Aimed at informed decision making on data driven projects
- Strategic planning, project management,
- product development, team management,
- buy or make decisions
Agenda & Material
The course covers two days with a mix of inspirational, theoretical, and workgroup activities. The material refers to the Amsterdam edition where prof. Marco Brambilla and Emanuele Della Valle covered the theoretical [T] parts, Herminio Vazquez and Riccardo Vincelli gave the inspirational keynotes [I] and participants performed the workgroup activities [W].
Day 1
- 9:15 Welcome [slides]
- 9:30 Self-Definition and Learning Goals [W]
- 9:45 Keynote: Data Driven Innovation [I] [slides]
- 10:45 Break
- 11:00 Self-Positioning [W] [photos: working, and result]
- 11:15 Principles and motivation of big data and data science [T] [slides]
- 12:30 Lunch
- 13:30 Principles of data science project management [T] [slides]
- 14:30 Business case method [T] [slides] and types of problem [T] [slides]
- 15:00 Business case – Part 1 [T+W] [photos: working]
- 15:30 Break
- 15:45 Business case – Part 2 [W] [photos: goal-poster-1, goal-poster-2, goal-poster-3]
Day 2
- 9:15 Summary of business cases [W]
- 9:45 Risks and challenges in data science [T] [slides]
- 10:45 Break
- 11:00 Challenges in business case Technical solutions for the business case [W] [photos: data-risks-1,data-risks-2,data-risks-3]
- 11:30 Principles and methods for data science (1/4): ML and Gradient Descent [T] [slides]
- 12:00 Principles and methods for data science (2/4): Product Price Prediction using Regression [T] [slides]
- 12:30 Lunch
- 13:15 Techniques in business case (1/2). Regression [W]
- 13:45 Principles and methods for data science (3/4): Learning to hire fresh graduates using classification [T] [slides]
- 14:15 Principles and methods for data science (4/4): Using Clustering to decide where to open new shops in franchising [T] [slides]
- 14:30 Techniques in business case (2/2). Classification, Clustering [W]
- 15:00 Break
- 15:15 Reporting on business cases [W] [photos: result-1, result-2, result-3]
- 15:45 Keynote Speech – Success story [I]
- 16:45 Closing
Acknowledgment
This is copy of the Webpage originally published on the Website of the Big Data Science group of DEIB – Politecnico di Milano.