Courses – Fakultet tehničkih nauka u Novom Sadu

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

Native organizations units: Chair of Applied Computer Science

Type of studies	Title
Undergraduate Academic Studies	Information Engineering (Godina: 3, Summer)

General information:

Category	Professional-applicative
Scientific or art field	Applied Computer Science and Informatics
Interdisciplinary	No
ECTS	8

Educational goal:

Adoption of basic knowledge about selected terms, concepts, methods and techniques in data science.

Educational outcome:

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.

Course content:

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 methods:

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.

Literature:

Authors	Title	Year	Publisher	Language
Zbigniew Michalewicz, David B. Fogel	How to Solve It: Modern Heuristics (2nd Edition)	2004	Springer	English
El-Ghazali Talbi	Metaheuristics: From Design to Implementation	2009	John Wiley & Sons, Inc.	English
Wes McKinney	Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd Edition)	2017	O’Reilly Media	English
Davy Cielen, Arno D. B. Meysman, Mohamed Ali	Introducing Data Science: Big data, machine learning, and more, using Python tools	2016	Manning Publications	English
Stuart Russel, Peter Norvig	Artificial Intelligence: A Modern Approach (3rd Edition)	2009	Pearson	English
Provost, F., Fawcett, T.	Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking	2013	O’Reilly Media, Sebastopol	English
Allen B. Downey	Think Python: How to Think Like a Computer Scientist (2nd Edition)	2015	Green Tea Press	English