Checking date: 24/06/2021


Course: 2021/2022

Data Analysis and Visualization
(18670)
Study: Master in Business Administration (MBA) (301)
EPE


Coordinating teacher: MUÑOZ GARCIA, ALBERTO

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Statistical notions are convenient.
Objectives
1. STATISTICS KNOWLEDGE AND SKILLS 1.1. Students should gain a thorough understanding of the problems relevant to the different functional areas. 1.2. Students should be able to diagnose potentially complex real-world statistical problems. 1.3. Students should be able to relate theory and practice. 2. ORGANIZATION TEAM AND PERSONAL SKILLS 2.1. Students should be able to explain their diagnosis and the solutions they propose in a clear and convincing way. 2.2. Students should be able to work effectively in teams and to demonstrate their capacity in managing diversity. 2.3. Students should be able to demonstrate their capacity to lead others and their own professional life.
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.