Data Preparation and Visualization

Code:

MECD04

Acronym:

VPD

Keywords
Classification	Keyword
CNAEF	Informatics

Instance: 2021/2022 - 1S (of 04-10-2021 to 26-02-2022)

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Data Science and Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MECD	28	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

This curricular unit has two main objectives, prepare students to:

(i) use the principles and techniques for pre-processing and prepare a data in order to obtain a dataset with quality to be analyzed, in order to obtain results with quality

(ii) and to use methods for visual representation of data that improves understanding and gain new insights on complex data.

Learning outcomes and competences

At the end of the course unit students should be able to:

- apply pre-processing techniques to improve the quality of a data set;

- design effective data visualizations that allow you to capture information from a data set;

- use the R, ggplot2 and tableau tools for data preparation and visualization.

Working method

Presencial

Program

Data Pre-processing: Data types; Data integration; Discretization of continuous variables; Treatment of Outliers; Treatment of Missing values; Data reduction, feature selection and feature engineering. Sampling.

Data visualization: Fundamentals of data visualization; Perception in data visualization; Color Usage; Visual encoding of discrete and continuous variables; The Grammar of Graphics; Interactive Visualization.

Mandatory literature

Salvador García; Data preprocessing in data mining. ISBN: 978-3-319-10247-4
Tamara Munzner; Visualization analysis & design. ISBN: 978-1-4665-0891-0

Complementary Bibliography

Colin Ware; Information visualization. ISBN: 1-55860-819-2
Garrett Grolemund and Hadley Wickham; R for Data Science, O'Reilly Media, 2016. ISBN: ISBN-10: 1491910399

Teaching methods and learning activities

The curricular unit is based on the following complementary activities that involve teaching and learning, and associated assessment:

- The theoretical concepts taught during theoretical classes should be learnt by study activities and conceptualization.

- Students should study test cases and solutions presented by lecturers in classes, and practice with new problems.

- The presentation in the classes of: methods available in R packages for data preparation; the Tableau tool for exploratory data analysis and the ggplot2 language for creating new ways for visualizing data, should lead to autonomous exploration and experimentation by students.

- Realization of a group project with the objective of providing the practical experience of designing and implementing a solution to a concrete visualization problem

Software

Tableau
R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	55,00
Trabalho escrito	45,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	40,00
Elaboração de relatório/dissertação/tese	0,00
Estudo autónomo	70,00
Frequência das aulas	40,00
Trabalho de investigação	12,00
Total:	162,00

Eligibility for exams

Students can only complete this course if they achieve a minimum grade indicated and if they do not exceed the limit for absences to classes according to the General Evaluation Rules of FEUP.

The tests will be closed book (the most important formulas will be provided if needed).

The evaluation will also take into account the presentation, correction and quality of the language used.

Calculation formula of final grade

P1: Project with ggplot2 (45%) (A minimum mark of 10 out of 20 is required)

P2: Mini-test (55%) (A minimum mark of 8 out of 20 is required)

 

Final Mark: 0.45 x P1 + 0.55 x P2

Examinations or Special Assignments

The method for the computation of the final mark for students with special status is identical to the method used for regular students.

Special assessment (TE, DA, ...)

Students with special status (working-student, military personnel or high-competition athletes) who cannot attend classes, should, like the other students, perform the group project and present it at the scheduled evaluation times.

If they choose to perform the special season for assessment , they can carry out the project individually or in group (together with other students in the same circumstances) with a schedule of evaluation previously agreed with the teachers of the discipline.

Classification improvement

Students may improve the mark of the component P1 (project) in the following academic year.

Students may improve the mark of the component P2 (Mini-test) in the exam season.