Recommended prerequisite for participation in the module

The project is expected to combine knowledge and skills from the course module Data Mining and Analysis.

Content, progress and pedagogy of the module

The project consists of a theoretical and practical work on a chosen problem that deals with large-scale data processing in the context of, e.g., Internet-of-Things (smart homes, smart energy), Industry 4.0 (smart factories), social networks or network security. The students should use relevant methods to analyze the problem, breaking it down into subproblems that can be solved using methods and theories from distributed systems, communication networks, data mining and analysis, machine learning etc., and demonstrate the functional solution

Learning objectives

Knowledge

• Must have knowledge of methods for system-level engineering 

• Must have knowledge of methods of programming of distributed systems 

• Must have knowledge of principles of data mining and analysis 

• Must have knowledge of performance metrics related to computation and communication  

• Must have knowledge of recognized principles and best-practices for documentation of programs and network-based solutions 

• Must demonstrate knowledge of relevant theory and methods to an extent so that the theory and method of the project can be explained and argued 

• Must have knowledge of the relevant subject terminology

Skills

• Must be able to identify, formulate and tackle problems within the subject area using contextual and technical analysis methods 

• Must be able to develop a requirements specification and determine the requirements specification is met via adequate testing and validation 

• Must be able to break down the given problem into constituent sub-problems and show a systematic treatment of these 

• Must demonstrate skills to plan how the individual sub-problems can be tackled in a network-based system. 

• Must be able to implement parts of the chosen solution in distributed and/or embedded system. 

• Must be able to implement parts of the chosen solution using principles and methods of data mining and analysis 

• Must be able to set requirements for and implement a user interface that is adequate for the project topic, including processed data visualization

• Must be able to draw up a validation plan and test procedures for the individual subsystems as well as the overall system. 

• Must be able to carry out a methodical and consistent professional assessment of the results obtained and their reliability and validity. 

• Must be able to convey knowledge and skills with correct use of subject terminology, orally as well as in writing through a project report 

Competences

• Must have the competence to plan, structure, implement and reflect on a project that is based on a socially or professionally relevant issue, and in which large-scale data processing by computer systems is included as the key element 

• Must have the competence to formulate and implement the models that can be used in design, implementation and testing of an overall system that satisfies the requirements. 

• Must be able to assess and take responsibility for scientific and technical solutions in the project area. 

• Must be able to generalize and put into perspective the experiences with project planning and collaboration. 

Type of instruction

As described in §17

Exam

Exams

Name of exam	Big Data Engineering in Practice
Type of exam	Oral exam based on a project
ECTS	20
Permitted aids	All written and all electronic aids
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures