Minor Computer Science

Samenvatting

Categorie
Broadening minor
Code
MINFEW01

Content

The Computer Science minor addresses techniques from Computer Science that allows storing, manipulating, and processing business data in order to extract business knowledge. The minor consists of four modules:

Module 1: Introduction to Programming (pre-master)
The module addresses the following questions: What are the basic concepts of programming? and What are the main programming principles? The emphasis of the work is on practical sessions in computer labs. During computer labs, assignments are solved as a preparation for the exam. The practical sessions are compulsory and a satisfactory result for the assignments is needed in order to be allowed to do the exam.

Module 2: Databases
The module addresses the following questions: What is the entity-relationship data model?, What is the relational data model?, What is the relational algebra?, How to query relational databases using SQL?, and How to design relational databases using functional dependencies and normalizations? The students will work in teams on several assignments related to database design and languages.

Module 3: Data Mining
The module addresses the following questions: What are the basic datatypes, data quality, and data preprocessing?, What are similarity and dissimilarity measures?, What are classification techniques?, What are clustering techniques?, and How to evaluate data mining techniques? The students will work in teams on several assignments related to data mining topics.

Module 4: Topics in Business Intelligence
The students will work on case studies applying data mining techniques to a business intelligence-relevant problem. The presentations attendance is compulsory and students are required to complete the case study with a scientific report.

*** IMPORTANT: When you choose to follow this minor the central minor department of the EUR will handle your selection and registration for the several modules. You should not register for the individual modules yourself in any way! ***

Learning objectives

At the end of the module Introduction to Programming, the students will:

  • Be familiar with the basic concepts of imperative programming;
  • Understand the object-oriented programming paradigm;
  • Be able to write Java programs for solving elementary computational problems.

At the end the module Databases, the students will:

  • Be able to draw an entity-relationship diagram (ERD);
  • Know what a relational database is;
  • Know how to query a relational database using the Structured Query Language (SQL);
  • Know how to design a relational database.

At the end of the module Data Mining, students will:

  • Understand the basic types of data, data quality, and preprocessing techniques;
  • Comprehend the measures of similarity and dissimilarity;
  • Understand data classification techniques;
  • Understand data clustering techniques;
  • Be able to evaluate data mining techniques.

At the end of the module Topics in Business Intelligence, students will:

  • Be able to analyse a business intelligence case;
  • Be able to apply data mining techniques in a business intelligence case;
  • Be able to describe the applied techniques and findings in a scientific report.

Special aspects

Successful participation in this minor requires a significant ability to deal with abstract concepts. In addition, a good mathematical background is desired. Furthermore, experience with a personal computer (e.g., Windows, Mac, or Linux) is essential. Previous experience with a programming language is an advantage, but it is not necessary. The minor is given in English.

Overview modules

Module 1: Introduction to Programming (pre-master)

  • Code: FEB21011S
  • ECTS: 4
  • Content: The course starts by introducing the fundamental data types and operations. After that, control statements, i.e., decision and repetition, are presented. Then, methods, as computations consisting of multiple steps, are given. Arrays, representing a data structure storing multiple values, are depicted next. Last, the main concepts of object-oriented programming paradigm, i.e., class and instance, are described.
    Meanwhile, students are expected to roll up their sleeves and acquire hands-on experience with the Java programming language. For this purpose, programming assignments will be carried out in the computer lab.  Students will develop an algorithmic way of thinking by implementing solutions for elementary computational problems.
  • Teaching method: Lectures and exercise sessions including computer tutorials.
  • Teaching materials: Course book and supplementary material.
  • Contact hours: 4 hours per week.
  • Self study: 8 hours per week

Module 2: Databases

  • Code: FEB53018
  • ECTS: 4 ECTS
  • Content: Databases offer various models for storing, retrieving, and processing large amounts of data. In this course we focus on the most popular database model, namely the relational model. First relational databases are introduced, followed by formal languages for querying as relational algebra, tuple relational calculus, and domain relational calculus. Then, the entity-relationship diagram (ERD) is presented as a high-level abstraction of the modeled world. After that, the Structured Query Language (SQL), one of the most used query languages, is described. Last, the normalization theory employed for designing relational databases is given.
    In order to better assimilate the topics covered during lectures, the students will work in teams on several assignments on the discussed topics. The students will also write a report describing the proposed solutions.
  • Teaching method: Lectures and exercise sessions.
  • Teaching materials: Course book and supplementary material.
  • Contact hours: 4 hours per week.
  • Self study: 8 hours per week.

Module 3: Data Mining

  • Code: TBA
  • ECTS: 4 ECTS
  • Content: The course starts by introducing the basic types of data, data quality, preprocessing techniques, and measures of similarity and dissimilarity. Then it describes data classification techniques and classification evaluation measures. After that data clustering techniques and clustering evaluation measures are explained.
    In order to better assimilate the topics covered during lectures, the students will work in teams on several assignments on the discussed topics. The students will also write a report describing the proposed solutions.
  • Teaching method: Lectures and exercise sessions.
  • Teaching materials: Course book and supplementary material.
  • Contact hours: 4 hours per week.
  • Self study: 8 hours per week.

Module 4: Topics in Business Intelligence (optional)

  • Code: FEB53016
  • ECTS: 3 ECTS
  • Content: For this course the students will work in teams on several projects related to the application of data mining techniques for knowledge discovery in a business intelligence context. For these assignments, the students are encouraged to make use of existing data mining software. The students will also have the opportunity to make presentations and write a scientific paper based on their results.
  • Teaching method: Plenary sessions with presentations of the various case topics.
  • Teaching materials: Course book and supplementary material.
  • Contact hours: 2 hours per week
  • Self study: 6 hours per week.

Examination

Method of examination
Module 1. Computer exam, assignments, and mandatory participation in the tutorials.
Module 2. Written exam, written report, and mandatory participation in the tutorials.
Module 3. Written exam, written report, and mandatory participation in the tutorials.
Module 4. Written report, presentations, and mandatory participation in the plenary sessions.

Composition final grade
Students need to pass each module with a grade of at least 5.5 (there is no compensation) in order to complete the minor.  Students who have opted for a 12 ECTS minor do not have to pass module 4.

Feedback
Module 1. After grading, students can review their exams; the lecturer will present sample solutions to the questions.
Module 2. After grading, students can review their exams and reports; the lecturer will present sample solutions to the exam questions and report exercises.
Module 3. After grading, students can review their exams and reports; the lecturer will present sample solutions to the exam questions and report exercises.
Module 4. After grading, students can review their reports; the lecturer will provide feedback for improvement.

Contact information

Dr. Flavius Frasincar
frasincar@ese.eur.nl
(010) 408 1340
Room Tinbergen H11-18

Faculty website
www.eur.nl/ese

Factsheet

Categorie
Broadening minor
Code
MINFEW01
Organisatie
Erasmus School of Economics
Studiepunten (ECTS)
15
Voertaal
Engels
Locatie
Campus Woudestein, Rotterdam

Registration

Please read the application procedure for more information.