Minor Deepening Minor: Advanced Computer Science

Category
Deepening minor
Minor code
MINFEW16
Belongs to study programme

Content

The Advanced Computer Science minor addresses advanced techniques from Computer Science that allows storing, manipulating, and processing business data in order to extract business knowledge. The minor consists of four modules (the last module being optional):

Module 1: Advanced Programming (minor)
The module addresses the following questions: What are the advanced concepts of programming? and What are advanced programming techniques (e.g., recursion, sorting, searching, etc.)? The emphasis of the work is on individual assignments.

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 (optional)
The students will work on case studies applying data mining techniques on 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 Advanced Programming, the students will:

  • Be familiar with the advanced concepts of imperative programming;
  • Understand advanced programming techniques (recursion, searching, sorting, etc.);
  • Be able to write Java programs for solving advanced 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, much like mathematics. The minor is given in English.

Overview modules

Module 1: Advanced Programming (minor)

  • Code: FEB53019
  • ECTS: 4
  • Content: The course starts by studying the concept of recursion and its relation to iteration. Then problems will be analyzed that are much easier to solve by recursion than iteration. After that we study several searching and sorting algorithms, and how to estimate and compare the performance of these algorithms. Several applications of these techniques will also be investigated. Students will do individual assignments on the previously mentioned topics.
  • Teaching method: Self-study and exercise sessions.
  • Teaching materials: Course book and supplementary material.
  • Contact hours: 2 hours per week
  • Self study: 10 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. Written report, 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 reports; the lecturer will present sample solutions to the report exercises.
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.ese.eur.nl/students/minor

Category
Deepening minor
Minor code
MINFEW16
Belongs to study programme
Organisation
Erasmus School of Economics
Study points (ECTS)
15
Instruction language
English
Location
Campus Woudestein, Rotterdam

Registration

Please read the application procedure for more information.