Checking date: 28/04/2023

Course: 2023/2024

Data Analysis and Visualization
Master In Business Administration - MBA (Plan: 466 - Estudio: 301)

Coordinating teacher: MUÑOZ GARCIA, ALBERTO

Department assigned to the subject: Business Administration Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Statistical notions are convenient.
Acquire the ability to gather information, analyse it and draw conclusions from it, using specialised data mining software and real case studies. Acquisition of the ability to relate theory and practice, so that they can apply concepts and solutions to specific organisational contexts.
Skills and learning outcomes
Description of contents: programme
1: Introduction and Descriptive Statistics 1.1 Introduction to the course. 1.2 Introduction to R. Basics, arithmetic with R, variable assignment. Basic data types in R. 1.3 Vectors, matrices, factors, data frames. 1.4 Reading and writing data in R. 2: Exploring categorical and numerical data data. 2.1 Bar charts, contingency tables, counts, proportions, piecharts. 2.2 Histograms, boxplots, visualizing in higher dimensions. 3: Numerical Summaries. 3.1 Measures of center. Median, median, quartiles and quantiles. 3.2 Measures of variability. Variance, standard deviation, IQR. 3.3 Shape and transformations. 3.4 Outliers. 4. Case Study for lessons 1-3. 5. Multivariate Data 5.1 Description of multivariate data. 5.2 Covariance, correlation, distances. 5.3 Visualization of multivariate data: scatterplots, bubble plots, etc. 6. Principal Component Analysis for visualization 6.1 Introduction and main ideas. 6.2 Implementing PCA in R. 6.3 Case Study. 7. Cluster Analysis for data exploration 7.1 Introduction and main ideas. 7.2 Hierarchical Methods. 7.3 Partitioning Methods. 7.4 Case study. 8. Linear Regression 8.1 Univariate Case. 8.2 Multivariate Case. 8.3 Case Study 9. Introduction to Tidyverse. 9.1 Data wrangling 9.2 Data Visualization: ggplot2 9.3 Grouping and summarizing. 10. Final Real case study.
Learning activities and methodology
ACTIVIDADES FORMATIVAS Theory (15 hours) Practices (15 hours) Complementary tutoring classes (5 hours) Office Hours (10 horas) Group Work and Individual Work
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
  • Antony Unwin. Graphical Data Analisis with R. CRC Press. 2015
  • Robert I. Kabacoff. R in action. Data analysis and graphics with R. Manning. 2015
Additional Bibliography
  • Brian Everitt, Torsten Hothorn. An introduction to Applied Multivariate Analysis with R. Springer. 2011
  • Chris Chapman, Elea McDonnell Feit. R for Marketing Research and Analytics. Springer. 2015
  • James E. Monogan III. Political Analysis using R. Springer. 2015
  • Peter Dalgaard. Introductory Statistics with R, 2 Ed. Springer. 2008

The course syllabus may change due academic events or other reasons.