Data Analytics with R (2235.YR.002016.1)

General information

Type:	OPT	Curs:	1	Period:	S semester
ECTS Credits:	3 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 1	Ruben Coca Marin	Operaciones, Innovación y Data Sciences	ENG

Laptop computer with Windows, MacOS or Linux installed.

Basics of R programming.
Basic computer skills: filesystems, operating systems.

R pre-test (to refresh concepts of the pre-programme)
R Test
Concept test
Deliverable exercises.
Final business case presentation.

R is one of the leading softwares for Data Science worldwide. It is widely used in big companies and institutions, such as McKinsey&Company, Pfizer and others.

Learning R is fundamental to understand better what is data science and to learn how to put analytics and artificial intelligence at work.

The objective of this course is to introduce relevant concepts of R and data analysis, focusing in particular on the interpretation and validity of the results that are obtained. At the end of the course, students should:

1) Understand the very basics of statistics applied to business and decision-making.
2) Be familiar with R, and be able to make statistical analysis and basic modelling (hypothesis tests, regression models, etc.)
3) Getting tools to solve data related problems. Understanding available data and how to leverage it.

1. Using R to analyze data: the Tidyverse Getting, cleaning, manipulating and visualizing data using Tidyverse libraries to get insigths.

2. Basics of machine Learning Solving regression and classification problems using the Tidymodels framework. Clustering and segmentation methods.

3. Creating reports and dashboards with R Create professional markdown reports to communicate results and insights using Quarto. Create dashboards to present KPIs and relevant information using Shiny.

The course format and methodological approach are based on a combination of explanations and practical parts. During the sessions participants will be provided with the material needed to follow this course. The material includes both the theoretical content of the different subjects to be discussed and the data needed to practice the concepts learned.

Participants will be with provided with real datasets for practices and will work in groups to solve different challenges by applying quantitative methods.

David Spiegelhalter (2019) The Art of Statistics: How to Learn from Data.
de Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013) An Introduction to Statistical Learning: with Applications in R
R for Data Science (R4DS): https://r4ds.had.co.nz/
Max Kuhn and Kjell Johnson, Feature Engineering and Selection: A Practical Approach for Predictive Models. Chapman and Hall/CRC http://www.feat.engineering/index.html
Max Kuhn and Julia Silge, Tidy Modeling with R https://www.tmwr.org/
Mastering Shiny: https://mastering-shiny.org/index.html

Group	Teacher	Department
Year 1	Ruben Coca Marin	Operaciones, Innovación y Data Sciences