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Big Data Seminar

Objectives

The Big Data field is emerging as one of the transformative business processes of recent times. By using classic techniques from Business Intelligence & Analytics, along with a new set of tools, it is able to deal with the volume, velocity, and variety associate with big data.

This seminar will build on the understanding of the basic concepts of big data and analytics to provide students the background to succeed in a data centric world, not only from the point of view of the technologies required, but also in terms of management, governance, and organization.

General characterization

Code

2469

Credits

3.5

Responsible teacher

Roberto Henriques

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

Available soon

Bibliography

•    Franks, Bill. Taming The Big Data Tidal Wave. 1st ed. New Jersey: Wiley, 2012
•    Morabito, Vincenzo. Big Data and Analytics. 1st ed. Cham: Springer International Publishing, 2015.
•    Pries, Kim H and Robert Dunnigan. Big Data Analytics. 1st ed. CRC Press, 2015.
•    Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl Jr., Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, Wiley, 2018

Teaching method

Lectures and class discussions (students are expected to actively participate in the discussions).

Case studies.

Team seminar works (group paper projects).

Evaluation method

The Final Exam is mandatory and must cover the entire span of the course. The weight of the final exam should not be less than 30% nor exceed 70%. The remainder of the evaluation can consist of class participation, midterm exams, in class tests, etc. Overall, written in class assessment (final exam, midterm) must have a weight of at least 50%.


Handout 10%, project 30%, final examination 60%.


Subject matter

  • CUC1. Introduction to Big Data, Definitions, Applications, Tools, and Governance;

  • CUC2. Introduction to Hadoop. Understanding the Hadoop Architecture. The Distributed File System (HDFS);

  • CUC3. Understanding MapReduce;

  • CUC4. Introduction to Data analytics;

  • CUC5. Data analytics in a big data environment;

  • CUC6. Introduction to Data Governance: security, privacy, integrity and quality.

Programs

Programs where the course is taught: