Degree in Business Intelligence and Data Analytics

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

Data analysis tools

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Description
The subject Data Analysis Tools intends to prepare the students to fluently perform qualitative and quantitative analyses of datasets. The course will teach the students how to perform a number of actions with datasets, such as extracting relevant statistical data through exploratory techniques, combine different datasets, and represent them through compelling visualizations enabling more detailed and thorough analysis. Through the development of the subject the student will learn how to use R, the programming language that will allow to perform the explained actions, thus providing the subject of a strong practical component. Different datasets from the real world will be used so that the student easily perceives the pragmatism of the topis explained and can understand the kind of use cases that the subject intends to address.
Type Subject
Primer - Obligatoria
Semester
First
Course
1
Credits
5.00

Professors

Ignacio Villaseca Boj
Karl Erik Jonas Viklund
Giuseppe D'Amico
Objectives

Learning Outcomes of this subject are:
R1. Ability to perform statistical analyses over a given dataset
R2. Ability to combine datasets
R3. Fundamental ability to program in R
R4. Ability to create relevant and compelling visualizations from a dataset

Contents

1. Data and statistics
1.1.Descriptive statistics
1.2.Introduction to R
1.3.Exploratory Data Analysis

2. Combining Datasets
2.1.Data concatenation
2.2.Joining Data
2.3.Combining Datasets with R

3. Data Visualization
3.1.Introduction
3.2.Dimensions, metrics and KPIs
3.3.Types of visualizations
3.4.Dashboarding
3.5.Data Visualization Tools

Methodology

The subject has a weekly operation with two class sessions, one lasting one hour and the other one, two. Roughly half of the teaching hours will be lectures, whereas the other half will be classroom activities involving problem solving in the aim of reinforcing the abilities of the students. Every two or three weeks the students will be encouraged to do homework activities which will become an essential part of the Continuous Evaluation (more info in the next section). The goal of these activities will be consolidating the knowledge of the contents of the subject and prepare the student both for the exams and for the project. They will also help the students understand the applicability of the contents of the course.

Evaluation

- Exams: 40%
o Midterm exam: 15%
o Final exam: 25% (*)
- Group project: 20% (*)
- Continuous evaluation: 35%
o Assistance and participation in class: 15%
o Homework assignments: 20%
- Innovation and Inspiration (I&I): 5%

(*) The final exam and the group project must be passed in order to make it possible to pass the subject.

In case the student fails the final exam or de group project, they will have an Extraordinary Call in July, in which a new exam will be prepared. In this case, the final evaluation will be composed of the exam mark (70%) and the project mark (30%).

Basic Bibliography

Hadley Wickham & Garrett Grolemund, R For Data Science, O’REILLY, 2017 Jonas Holt, Sets of Data. Where We re Going. Where We ve Been. Contents. Statistics in Action. Using Technology, 2017.