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R for Data Sciences (19CIE11871)

General information






S semester

ECTS Credits:


Teaching Staff:

Group Teacher Department Language
German Sánchez Hernández Operaciones, Innovación y Data Sciences ENG



Previous Knowledge



As it has been recently said, data is the new oil. Data is everywhere: in the company, data is not only related to features of costumers and products and their purchases, but also the interaction between the company and each customer through the different communication channels (purchases, email, phone, social networks, etc.). The main challenge for companies is to take advantage of it.

The analysis of these data requires new skills that comes from the classical statistical analysis to the (new) machine learning methods, going through the paradigm of Big Data. A couple of decades ago, managing an Excel spreadsheet could be the difference between a good and a bad analyst. Nowadays, and despite of having an analytics department, the difference is in being able to program and to move around this universe of huge data.

This seminar pretends to introduce the relevant concepts of programming with R to perform data analysis. R is classically the language for performing analysis tasks, due to its big community of developers and researchers, the ease to learn and to use, and the versatility of the generated solutions.

Course Learning Objectives

In this Skill Seminar we will introduce the relevant concepts of programming in the R environment to be prepared for the data analytics tasks that students will perform in the subsequent courses and in the real world.

The seminar is organised in a mixed format, with both master explanations and hands-on exercises, focusing in the basis of R, its performance and the interpretation, validation and visualisation of the obtained results.

At the end of the seminar, students should:
- Be familiar with the main methodologies to analyse data in a formal way
- Be confident with the basic concepts of programming with R.


1. Fundamentals of data analysis with R

2. Good practises in R: scripting and performance

3. Evaluating the results: visualisation, interpretation and validation

4. Group exercise


The methodologic approach chosen for this seminar is a combination of both master explanations and hands-on exercises. During the sessions attendants will be provided with the theoretical and practical material needed to follow the seminar.

The practical material includes real datasets for practising the theoretical framework introduced in the theoretical part of the seminar. Attendants will work in pairs to solve the in-class proposed exercises and will develop a final group exercise to demonstrate the skills acquired during the seminar.

The seminar is divided in five sessions, each one including practice cases coming from real business situations. Students can use their own laptops or the PCs provided by ESADE in the lab classes, and they should only use those devices for the seminar activities.

Assessment criteria

Class Participation 60%
Group Exercise 40%

The class participation note is divided into Atendance to sessions (20%) and Assignments (80%).


- Bishop, C. M. (2006). Pattern recognition. Machine Learning, 128.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
- Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons.

Timetable and sections

Group Teacher Department
German Sánchez Hernández Operaciones, Innovación y Data Sciences