Subject: Methods and Techniques in Data Science (17 - IFE223)


Basic Information

CategoryProfessional-applicative
Scientific or art field:Applied Computer Science and Informatics
InterdisciplinaryNo
ECTS8
Native organizations units

Chair of Applied Computer Science
Course specification

Course is active from 30.09.2005..


Precondition courses

Course idMandatoryMandatory
Mathematical LogicYesYes
Adoption of basic knowledge about selected terms, concepts, methods and techniques in data science.
Students are acquainted with theoretical and practical foundations of data science. Students are capable of solving selected basic types of problems in the area of data science and prepared to further extend and improve their knowledge of data science methods and techniques.
Notion, origin and development of data science. Structure of data science projects. Overview of data science methods and techniques. Examples of application of data science methods and techniques. Programming languages in data science. Usage of a selected programming language (Python) within data science. Fundamentals of the usage of version control systems for source code. Introduction to logic programming. Fundamentals of the Prolog programming language. Introduction to search strategies and metaheuristics. Fundamentals of genetic algorithms and evolutionary computation. Introduction to fuzzy set theory, fuzzy logic and fuzzy systems. Introduction to neural networks. Introduction to natural language processing and text mining. Introduction to knowledge representations and knowledge-based systems.
Teaching is performed through lectures, regular practice classes, computer practice classes and consultations. In lectures, students primarily get acquainted with theoretical foundations of selected concepts, and possibilities and examples concerning application of theoretical knowledge. In practice classes, students conduct most of their activities at the computer and further improve their knowledge acquired in lectures by analysing additional examples and solving problems that are mostly oriented towards practical application. The teaching process is organised in a manner that facilitates active participation of students and development of their problem solving skills. During consultations, students obtain additional explanation and instructions that help them solve problems, understand topics related to the course syllabus and complete course assignments.
AuthorsNameYearPublisherLanguage
Davy Cielen, Arno D. B. Meysman, Mohamed AliIntroducing Data Science: Big data, machine learning, and more, using Python tools2016Manning PublicationsEnglish
Allen B. DowneyThink Python: How to Think Like a Computer Scientist (2nd Edition)2015Green Tea PressEnglish
Wes McKinneyPython for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd Edition)2017O’Reilly MediaEnglish
Stuart Russel, Peter NorvigArtificial Intelligence: A Modern Approach (3rd Edition)2009PearsonEnglish
Zbigniew Michalewicz, David B. Fogel How to Solve It: Modern Heuristics (2nd Edition)2004SpringerEnglish
El-Ghazali TalbiMetaheuristics: From Design to Implementation2009John Wiley & Sons, Inc.English
Provost, F., Fawcett, T.Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking2013O’Reilly Media, SebastopolEnglish
Course activity Pre-examination ObligationsNumber of points
Complex exercisesYesYes10.00
Complex exercisesYesYes10.00
Complex exercisesYesYes10.00
ProjectYesYes30.00
TestYesYes10.00
Oral part of the examNoYes30.00
Name and surnameForm of classes
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Ivančević Vladimir
Associate Professor

Lectures
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Turović Radovan
Assistant - Master

Practical classes
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Todorović Nikola
Assistant - Master

Practical classes
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Turović Radovan
Assistant - Master

Computational classes
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Akik Elena
Teaching Associate

Computational classes
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Todorović Nikola
Assistant - Master

Computational classes