Master of Science in Data Science La Salle Campus Barcelona URL

Master of Science in Data Science

Become an expert in analysing, structuring, filtering, visualizing and valuing the production of generated data

Data analysis and visualization tools

To start talking about data, it is necessary to properly cement the knowledge of probability, statistics and, on this basis, understand the essential concepts of data visualization. We aim to establish a common language and review acquired concepts, to ensure and deepen them. The class sessions will be completed with previous assignments and readings, as well as with consolidation exercises, using the R language as a tool.
Type Subject
Primer - Obligatoria

Titular Professors

Previous Knowledge

It is recommended to have previous knowledge of probability and statistics at the degree level, although the subject will do a complete review of both topics to standardize and establish a common vocabulary.


The goals will focus on:
• Understand the basics of statistical analysis of data and the principles that underpin it.
• Know the bases of statistics that allow to approach advanced techniques and concepts of Machine Learning.
• Know how to use R as a tool for statistical analysis.
• Understand the best way to visualize a data set based on the type of data and the recipient of the information.
• Understand the best way to use graphs and organize results to efficiently convey the knowledge generated by the data.


1. Presentation
2. Probability
3. Descriptive statistics
4. Hypothesis test
5. Regression
6. Data visualization
7. Data Storytelling
8. Covariance matrix and ANOVA
9. Factor Analysis
10. Entropy and information
11. Bayes and his friends
12. Survival analysis
13. Data preparation

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 works, as well as conceptual exercises and written exercises.


This subject will be assessed on a continuous via from exercises, assignments, practices, and presentations in class.

Evaluation Criteria

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:
 A weekly exercise as a summary of the session - Individual
 An exercise from the Data Visualization part
 An exercice from the Data Storytelling part.
 A final work.

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:
The training activities will be considered to have the following category:
• Exercises: moderately significant
• Project: highly significant
• Final Evaluation: highly significant

Basic Bibliography

The bibliography will be detailed throughout the course.

• Class/Lecture notes
• Documentation and papers uploaded to Intranet (eStudy)

Additional Material

All class material (presentations, exercises, articles, documents, etc.) will be shared in the subject folder of the La Salle Intranet: eStudy.