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Computer Science (Broadening)

MINFEW01
Economics

About this minor

Turning data into knowledge using computer science. Have you ever wondered how one can use computers to process data for business analytics? Do you want to perform advanced computational analysis for your thesis? Then this minor is for you. In the Computer Science minor students learn Java programming, relational databases, data mining, and how to apply these in business intelligence. Successful participation in this minor requires a significant ability to deal with abstract concepts. In addition, a good mathematical background (algebra, calculus, and statistics) is needed.

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 (the last module being optional):

Module 1: Introduction to Programming
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 on a business intelligence-relevant problem. The presentations attendance is compulsory and students are required to complete the case study with a scientific report.

Learning outcomes

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.

Good to know

Successful participation in this minor requires a significant ability to deal with abstract concepts.
In addition, a good mathematical background (algebra, calculus, and statistics) is needed. 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 taught in English, given offline, and requires mandatory presence for tutorials (modules 1, 2, and 3) and plenary sessions (module 4).

Teaching method and examination

Lectures and exercise sessions including computer tutorials, plenary sessions with presentations of the various case topics

Teaching material
The teaching material is provided as slides, books, and scientific papers.

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.

In order to pass the minor, the average grade of the four modules combined needs to be at least 5.5 and the grade for each individual module cannot be lower than 4.5.
Students who have opted for a 12 ECTS minor, do not need to complete module 4. Their average grade will be calculated over 3 modules.

Link to more information

  • Credits
    ECTS 15
  • Level
    bachelor
  • Contact coordinator
  • Selection minor
    No
If anything remains unclear, please check the FAQ of Erasmus University.

Offering(s)

  • Start date

    2 September 2024

    • Ends
      8 November 2024
    • Term *
      Block MINOR
    • Location
      Rotterdam
    • Instruction language
      English
    Currently no more seats available
These offerings are valid for students of Leiden University