Degree in Business Intelligence and Data Analytics

Lead the transformation of companies through the use and analysis of data.


The course is mostly dedicated to inference techniques that allow generalization of the conclusions derived from a sample to the population from which it came, together with an assessment of the uncertainty and the degree of precision derived from such generalization. It will mainly deal with parameter estimation procedures, either in a specific way or by means of intervals of confidence, and hypothesis testing procedures, both exemplified for some relevant cases for data-driven business management. Statistics is not about memorizing formulas or knowing how to use a calculator, but identifying which method suits best to find a solution to the problem we are facing. The course assumes a solid knowledge of mathematical analysis and understanding of Basic Descriptive Statistics. It is structured so that the students apply the theoretical concepts on a set of practical cases and a project, which allows employing all the methods and techniques learned. The objective of this course is to provide students with the necessary statistical tools to carry out a basic descriptive and inferential analysis. It will emphasize the use of 2 tools: Excel and SPSS (Statistical Package for the Social Sciences). Upon completion of the course, students will be able to understand and carry out basic techniques of descriptive and inferential statistics.
Type Subject
Primer - Obligatoria
Previous Knowledge

The learning outcomes are:
LO01 - Using Excel and SPSS for data analysis
LO02 - Looking for relations between data
LO03 - Learning Random Variable Theory and discrete and continuous distributions
LO04 - Building confidence intervals and designing research on hypothesis testing
LO06 - Conducting a study case and elaborate adequate survey to address a problem


1. Probability
- Combination
- Probability, Conditional Probability and Bayes Theorem
- Random variables
- Binomial distribution & Bernoulli
- Normal distribution (and Student distribution)
2. Statistical Tools Presentation
- Excel
- SPSS (Descriptive statistics and Contingency Tables)
3. Sampling
- Sampling distribution
- Population estimation
- Confidence intervals
- Hypothesis Testing
4. Correlation and regression
- Correlation between variables
- Simple and Multiple Linear Regression
5. Anova Model


Weekly teaching will consist of one lecturing session to explain basic concepts and lab session to apply knowledge to practical situations. Practice sessions are for problem solving.


Theory Exam 20%
Work in class 20%
Assignments 20%
Midterm 15%
Group project 25%

Mid-semester exam and the final project grades must be greater than 4 points. In other words, you fail the subject
if you get less than a 4 in any of these concepts.
Important: The subject will be passed if the overall calculation of the grade is equal or higher than 5.
Prohibition of AI tools: The use of AI is forbidden in this course. Thus, the use of these tools by students will be considered fraud and will involve the application of the copy regulations of La Salle Campus Barcelona (
RETAKE POLICY: Retake exam will be cumulative and the grade from the retake exam will count as the gradebook grade.
Individual and group assignments, along with the final project must be uploaded to campus virtual before the retake exam. Otherwise, you won’t be allowed to write the exam.
The maximum grade will be equal to 6 points.

Evaluation Criteria
Basic Bibliography

Recommended textbook is:
Fundamentals of Business Statistics, Sweeney, D. Williams, T. & Anderson, D. Cengage Learning; 6th edition 2011.
Recommended online course (free) is:

Additional Material