Big Data Processing

Ryza, Sandy et al. (2017). Advanced Analytics with Spark: Patterns for Learning from Data at Scale. O'Reilly Media. Ofer Mendelevitch, O., Stella, C. & Eadline, D. (2016). Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale. Addison-Wesley. Li, Kuan-Ching et al. (2015). Big Data: Algorithms, Analytics, and Applications. Chapman and Hall/CRC.
Data Science
6321
23091
Big Data Processing
ISMAT6321-23091
3
6
0
12
Não
Português
The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve classroom exercises, with the use of a computer. The assessment is made by continuous assessment or written exam. The continuous assessment consists of two written tests with a weight of 30% each and a group work (40%).
Mandatory
LO1: Know the technologies for processing large data sets; LO2: Apply techniques and algorithms to extract information and develop models from large amounts of data. LO3: Develop recommendation systems
S1: Programming for large scale S2: Data stream analysis S3: Machine learning for large scale S4: Analysis of hyperlinks S5: Recommendation systems

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Semestral
The primary objective of the Big Data Processing course is to introduce students to the technologies, techniques, and algorithms used to process and analyze large volumes of data. In a context where organizations generate and collect data on a massive scale, mastering methods capable of transforming this data into useful information, actionable knowledge, and predictive models is essential. This course covers fundamental topics such as large-scale programming, data stream analysis, machine learning in large-scale environments, link analysis, and the development of recommendation systems.