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 topics explained and can understand the kind of use cases that the subject intends to address.
Titular Professors
Professors
None
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
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
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.
The subject lasts one semester and is divided into two parts which complement each other: a theoretical side and a practical one, which is performed in a group project. In order to pass the subject, the student must pass both the theoretical part (final exam) and the practical examination (group project).
The final mark of the subject is composed of the following percentages:
- Exams: 45% (High importance)
o Midterm exam: 20%
o Final exam: 25%
- Group project: 20%
- Continuous evaluation: 35% (Less importance)
o Assistance and participation in class: 15%
o Homework assignments: 20%
Retake policy: This course has no retake exam. Students not achieving an average grade of 5 need to take the class again next year.
Use of AI tools: No use of AI tools are allowed during the highly important exams. If AI tools are used in other activities the students need to show that they are significantly improving on the output of the chatbot to achieve a grade >0.
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Diez, D. M., Barr, C. D., & Cetinkaya-Rundel, M. (2012). OpenIntro statistics (Vol. 4). Boston, MA, USA:: OpenIntro
R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund.
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