Titular Professors
The goals will focus on:
Understand that it is a data-based organization and what benefits it brings
Identify the impact of artificial intelligence and data science on business
How to implement a data-based project
1. Big Data and Data Science in Business
a. Definition
b. Big Data before Big Data
c. Data Science
d. Thinking like a Data Scientist
e. Cognitive Biases
f. Data Visualization
2. Artificial Intelligence for Business
a. How Does Artificial Intelligence Work in a Company?
b. Spotifys Recommendation System
c. Artificial Intelligence at Netflix
d. Creating House of Cards
3. Big (Brother) Data
a. Beyond the Tip of the Iceberg
b. Data Brokerage and Consumers
c. Predictive Analytics at Facebook
d. Big Data at Tinder The Big Lies People Tell
4. Finding the Evidence in Business
a. Causality
b. Instrumental variables
c. Natural experiments
d. Regression discontinuity
e. A/B Testing
f. Running Experiments
g. How Obama raised $60 Million?
5. Business with Geolocated data
a. Predicting human movement
b. Retail location
c. Mobile phone data
6. AI Business Model Canvas
a. Presentations
Note: Topics can be adjusted and/or modified at the discretion of the master's coordination.
The methodology used combines master classes, student participation, exercises, and practices. For the student, this will involve both individual and group works, as well as conceptual exercises, written exercises, and oral presentations.
This subject will be assessed on a continuous via from exercises, assignments, practices, and presentations in class.
Continuous assessment
This subject will be assessed on a continuous via from exercises, assignments, practices, and presentations in class. The final grade will be a weighting of:
- Written exercise: 30%
- Participation: 10%
- Project: 40%
- Oral presentation: 20%
Extraordinary call
The exam and/or works of extraordinary call will be determined from the coordination of the subject.
Copies regulations
The subject is governed by the general regulations of copies of La Salle Campus BCN:
https://www.salleurl.edu/en/copies-regulation
The training activities will be considered to have the following category:
Exercises: moderately significant
Project: highly significant
Final Evaluation: highly significant
The bibliography will be detailed throughout the course:
Class/Lecture notes
Documentation and papers uploaded to Intranet (eStudy)
Hadley Wickham - R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2021)
Laudon, Kenneth C. and Laudon, Jane P. (2014). Management Information Systems: Managing the Digital Firm. Global Edition. 13th edition.
Davenport, T & Harris, J. Competing on Analytics: The New Science of Winning (2014, 2017). Havard Business School
Foster Provost and Tom Fawcett. Data Science for Business. O'Reilly (2013).
Carl Anderson. Creating a Data-Driven Organization. O'Reilly. (2015).
DJ Patil. Building Data Science Teams. O'Reilly. (2011).
DJ Patil and Hilary Mason. Data Driven: Creating a Data Culture. O'Reilly. (2015).
All class material (presentations, exercises, articles, documents, etc.) will be shared in the subject folder of the La Salle Intranet: eStudy.