Data Science
Code: 104540 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2503743 Management of Smart and Sustainable Cities | OB | 2 | 2 |
Contact
- Name:
- Dimosthenis Karatzas
- Email:
- Dimosthenis.Karatzas@uab.cat
Use of Languages
- Principal working language:
- english (eng)
- Some groups entirely in English:
- Yes
- Some groups entirely in Catalan:
- No
- Some groups entirely in Spanish:
- No
Other comments on languages
Written material of the subject will be prepared in English.Teachers
- Yael Tudela Barroso
- Guillermo Torres
Prerequisites
To have completed the first-year subjects of Computer Science (Informàtica), Mathematics, Internet applications programming, and the second year subject of Databases.
Objectives and Contextualisation
This subject must allow the student to discover the existing technologies and the different ways for managing and analysing the data generated in the city on a daily basis.
Students will learn techniques for visualization, analysis and modelling of data that will allow them to generate new knowledge and intuitions from the city data.
Competences
- Demonstrate creativity, initiative and sensitivity in the different social and environmental topic areas.
- Solve urban management problems using knowledge, methodology and procedures for the design and implementation of computer applications for different types of environment (web, mobile, cloud) and different paradigms.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Work cooperatively in complex and uncertain environments and with limited resources in a multidisciplinary context, assuming and respecting the role of the different members of the group.
Learning Outcomes
- Apply automated decision-making techniques.
- Demonstrate creativity, initiative and sensitivity in the different social and environmental topic areas.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Work cooperatively in complex and uncertain environments and with limited resources in a multidisciplinary context, assuming and respecting the role of the different members of the group.
Content
- Data preparation
- Data visualization
- Normalization
- Unknown values
- Reduction of dimensionality
- Feature selection
- Classification and regression (supervised techniques)
- Linear and polynomial regression
- Logistic regression
- Probabilities, Naive Bayes Classifier
- Decision trees and "random forests"
- Hierarchical classification
- Generation of knowledge (unsupervised techniques)
- Rules of association
- Recommendation systems
Methodology
Data science is defined by the types of problems that it aims to solve; therefore, it will be that typology of problems that will direct the organization of all the contents.
There will be three types of activities: theory classes, solving practical exercises individually (problems) and developing projects in small teams.
1. Theory classes: The objective of these sessions is for the teacher to explain the theoretical background of the subject. For each one of the topics studied, the theory and mathematical formulation is explained, as well as the corresponding algorithmic solutions.
2. Laboratory sessions: Laboratory sessions aim to facilitate interaction and to reinforce the comprehension of the topics seen in the theory classes. During laboratory sessions we will tackle two types of activities: solving practical exercises and performing team-project follow ups and presentations.
2.1 Problems: A weekly set of problems to work through will be used, that require the implementation of methods seen in the theory classes. Work on the problems will be initiated in class and should be completed by each student individually at home. Students will be required to make a weekly submission of their work, that will comprise the problems portfolio.
2.2 Projects: Project sessions comprise activities related to the realization of two short projects during the semester. Students will work collaboratively on these projects in small teams. During the project sessions (1) the teacher will present and discuss the projects and possible approaches, and (2) the teams will present their final results to the class. The teams will have to design and implement a solution, manage the distribution and organization of the work to be carried out, and present final results to the teacher.
The above activities will be complemented by a system of tutoring and consultations outside class hours.
All the information of the subject and the related documents that the students need will be available at the virtual campus (cv.uab.cat).
The transversal competence T01 is addressed through teamwork and collaboration during the development of the projects. The evaluation of the projects includes an oral presentation of each team, during which the students will have to present their work and explain the organization of the team.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Exercise sessions | 12 | 0.48 | 1, 5 |
| Theory classes | 26 | 1.04 | 5, 3, 4 |
| Type: Supervised | |||
| Project sessions | 12 | 0.48 | 1, 5, 3, 4, 6 |
| Tutoring | 5 | 0.2 | 1, 5, 3, 4 |
| Type: Autonomous | |||
| Dedication to resolve exercises | 12 | 0.48 | 1, 3, 4 |
| Further reading and study of the material | 40 | 1.6 | 1 |
| Work on practicals (projects) | 37 | 1.48 | 1, 3, 4, 6 |
Assessment
To assess the level of student learning, a formula is established that combines knowledge acquisition, the ability to solve problems and the ability to work as a team, as well as the presentation of the results obtained.
Final grade
The final grade is calculated weighted in the following way and according to the different activities that are carried out:
Final grade = 0.4 * Theory Grade + 0.1 * Exercises Grade + 0.5 * Projects Grade
This formula will be applied as long as the theory and the laboratory grades, are higher than 5. There is no restriction on the exercises grade. If doing the calculation of the formula yields >= 5 but does not reach the minimum required in any of the evaluation activities, then a final grade of 4.5 will be given.
Theory Grade
The theory grade aims to assess the individual abilities of the student in terms of the theoretical content of the subject, this is done continuously during the course through two partial exams:
Theory Grade = 0.5 * Grade Exam 1 + 0.5 * Grade Exam 2
Exam 1 is done in the middle of the semester and serves to eliminate part of the subject if it is passed.
Exam 2 is done at the end of the semester and serves to eliminate part of the subject if it is passed.
These exams aim to assess the abilities of each student in an individualized manner, both in terms of solving exercises using the techniques explained in class, as well as evaluating the level of conceptualization that the student has made of the techniques seen. In order to obtain a final pass theory grade, it will be required for the partial exam grades 1 and 2 to be both higher than 4.
Recovery exam. In case the theory grade does not reach the adequate level to pass, the students can take a recovery exam, destined to recover the failed part (1, 2 or both) of the continuous evaluation process.
Exercises Grade
The aim of the exercises is for the student to train with the contents of the subject continuously and become familiar with the application of the theoretical concepts. As evidence of this work, the presentation of a portfolio is requested in which the exercises worked out will be collated.
In order to obtain a grade for exercises, it is necessary that more than 50% of the exercises are submitted during the semester. In the contrary, the exercises grade will be 0.
Exercises Grade = Portfolio evaluation
Projects Grade
The part of projects carries an essential weight in the overall mark of the subject. Developing the projects requires that the students work in groups and design an integral solution to the defined challenge. In addition, the students must demonstrate their teamwork skills and present the results to the class.
Each of the two projects is evaluated through its deliverable, an oral presentation that students will make in class, and a self-evaluation process. The participation of students in all three activities (preparing the deliverable, presentation and auto evaluation) is necessary in order to obtain a projects grade. The grade is calculated as follows:
Project Grade X = 0.6 * Grade Deliverables + 0.3 * Grade Presentation + 0.1 * Grade Self-evaluation
If performing the above calculation yields >= 5 but the student did not participate in any of the activities (deliverable, presentation, auto evaluation), then a final grade of 4.5 will be given to the corresponding project.
Laboratory Grade = 0.5 * Grade Project 1 + 0.5 * Grade Project 2
To obtain a project grade, it will be necessary that the grades of both projects are above 4.
In case of not passing any of the projects, it will be allowed to recover it restricted to a maximum grade of 7/10.
Important notes
Notwithstanding other disciplinary measures deemed appropriate, and in accordance with the academic regulations in force, evaluation activities will be suspended with zero (0) whenever a student commits any academic irregularitiesthat may alter such evaluation (for example, plagiarizing, copying, letting copy, ...). The evaluation activities qualified in this way and by this procedure will not be recoverable. If you need to pass any of these assessment activities to pass the subject, this subject will be failed directly, without opportunity to recover it in the same course.
In case there the student does not deliver any exercise solutions, does not attend any project presentation session during the laboratory sessions and does not take any exam, the corresponding grade will be a "non-evaluable". In another case, the “no shows” count as a 0 for the calculation of the weighted average.
In order to pass the course with honours, the final grade obtained must be equal or higher than 9 points. Because the number of students with this distinction cannot exceed 5% of the total number of students enrolled in the course, it is given to whoever has the highest final marks. In case of a tie, the results of the partial exams will be taken into account.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Exams | 40 | 5 | 0.2 | 5, 3 |
| Exercises deliverables | 10 | 0 | 0 | 1, 4 |
| Project deliverables | 3 | 0 | 0 | 1, 2, 3, 4, 6 |
| Project presentations | 15 | 1 | 0.04 | 5, 3, 6 |
| Self-evaluation | 5 | 0 | 0 | 6 |
Bibliography
- Data Science from Scratch: First Principles with Python, Joel Grus, O'Reilly Media, 2015, 1st Ed.
- Python Data Science Handbook, Jake VanderPlas, O’Reilly Media, 2016, 1st Ed.
- Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2011
- Model-Based Machine Learning, J. Winn, C. Bishop, early access: http://mbmlbook.com/
- Computational and Inferential Thinking: The Foundations of Data Science, Ani Adhikari and John DeNero, online: https://ds8.gitbooks.io/textbook/content/
Software
For the problems and projects of the course we will use Python, and the Python: libraries NumPy, MatPlotLib, SciKit Learn, Pandas