Data Sciences

Recommended Prerequisites

Not applicable.

Teaching Methods

T: In theoretical classes, the fundamental concepts contextualized in real problems are presented. The expository and dialectical method is used. Digital tools for asynchronous teaching are also explored, such as questionnaires, discussion forums, videos and email.

P: In practical classes, practical problems are introduced for students to solve and computer tools suitable for solving the problems are presented. Asynchronous teaching is also used, which is based on exercises, discussion forums, tutorials and email.

Learning Outcomes

To understand the different natures of data and their implications for analysis

To plan data acquisition, its organization and proper storage

To apply usual data description techniques

To recognize the differences between univariate and multivariate analysis

To identify suitable situations for the application of supervised, unsupervised and reinforcement learning

To know some advanced ways of generating and integrating data with reality.

Work Placement(s)

No

Syllabus

1. Data acquisition and organization:

        i. Unstructured and structured data (on-line content)

        ii. Data acquisition in the digital age (on-line content)

        iii. Smart sensors, apps and the internet of things (face-to-face content)

        iv. Imaging data: the cases of radiography, CT and oral scanners (face-to-face content)

        v. Organization, storage and data protection (on-line content)

2. Data analysis

        i. Data description and visualization: numerical indicators and charts (on-line content)

        ii. Univariate and multivariate analysis (on-line content)

        iii. Artificial intelligence methods applied to data: supervised, unsupervised and reinforcement learning (face-to-face content)

3. Data generation and integration

        i. Computer simulation (face-to-face content)

        ii. Augmented reality (face-to-face content).

Head Lecturer(s)

Francisco José Fernandes Vale

Assessment Methods

Assessment
Exam: 50.0%
Continuous assessment according to e-log book : 50.0%

Bibliography

- Knaflic, C. N. (2015). Storytelling with data: a data visualization guide for business professionals. Hoboken, New Jersey: John Wiley & Sons, Inc.

- Emc Education Services (2015, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer

- Bruce, P. C., & Bruce, A. (2017). Practical statistics for data scientists: 50 essential concepts.