TU Delft
Year
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NEDERLANDSENGLISH
Organization
2017/2018 Technology, Policy and Management Master Complex Systems Engineering and Management
SEN1631
Fundamentals of Data Analytics
ECTS: 5
Module Manager
Name E-mail
T. Fiebig    T.Fiebig@tudelft.nl
Instructor
Name E-mail
T. Fiebig    T.Fiebig@tudelft.nl
M.Y. Maknoon    M.Y.Maknoon@tudelft.nl
Responsible for assignments
Name E-mail
T. Fiebig    T.Fiebig@tudelft.nl
Co-responsible for assignments
Name E-mail
Dr. W.A.G.A. Bouwman    W.A.G.A.Bouwman@tudelft.nl
Contact Hours / Week x/x/x/x
0/0/4/0
Education Period
3
Start Education
3
Exam Period
3
4
Course Language
English
Expected prior knowledge
Although it is not required to grasp all the mathematical details beforehand, students should be prepared on an advanced course containing quite some fundamental thinking, calculus and statistics. Skills in programming and databases are also useful for the project assignment.
Course Contents
The current Information and Communication Technology enables governments, infrastructure operators and individuals to monitor their activities at a detailed level. In this way, large amounts of raw data are being collected, which can be transformed into ‘information’ and ‘knowledge’. This transformation of data into information and knowledge is based on the use of a broad spectrum of data analytics techniques. Then, based on the insights obtained from the data, operational activities can be optimized and strategic decisions underpinned

The concepts, algorithms, and application of these data analytics methods are the central topics of this course. Together, they entail an engineering approach where a few fundamental pitfalls and dilemmas have to be overcome. Additionally, special attention will be given to the role of data analytics in critical infrastructures, such as smart energy grids, transportation and logistics, and water management systems, particularly concerning topics like privacy, (cyber) security, and resiliency.
Study Goals
The students are able to:
• describe the data mining process and its objectives;
• identify and understand the main fundamental pitfalls inherent to data mining algorithms;
• propose and employ suitable data mining algorithms for different problems;
• describe basic data mining algorithms;
• discuss how data analytics can be related to privacy, security, and resiliency;
• use a data mining software tool to analyse structured data and produce different concept descriptions related to classification and prediction problems.

Education Method
In class learning activities involve:
- Lectures explaining the concepts and (sometimes mathematical) theories behind the data analytics process and methods;
- Guest lectures on relevant topics.

Out of class learning activities involve:
- Practical assignments to explore fundamental pitfalls and dilemmas in data analytics
- A data mining project assignment in small groups of students;
- Peer-review of other groups projects;
- Solving problem sets in preparation for classes and exam.
Course Relations
This course is an advanced version of the introductory BSc course "Intelligent Data Analysis" (TB242IA) (there is little overlap and all topics are treated at much more fundamental level).

Cybersecurity-related topics are addressed more in-depth in the follow-up course “Cyber Data Analytics” (CS4035).
Literature and Study Materials
Recommended book: "Intelligent Data Analysis", see http://www.springer.com/computer/image+processing/book/978-3-540-43060-5

Other materials (slides, books, articles, references to software packages, etc.) will be made available as well.
Assessment
Groups of (in principle two) students work on 2 small practical assignments and one large project assignment. Groups of students work separately and get supervision and feedback on their work progress from the lecturer(s).

The first practical assignment concerns a study on the bias-variance dilemma using a simulation study. The second practical assignment concerns a study related to the curse of dimensionality also by executing a simulation study.

The deliverable of the final project assignment is a scientific report that includes, among others, a literature review related to the topic of choice. The focus can either be on a practical data mining exercise or on a more theoretical subject.

The final grade is established based on a weighted average of the exam grade (2/3), and the project assignment (1/3). The practical assignments are mandatory to obtain a grade in the project assignment.