26E113PVP - Practicum in Data Collection and Visualization

Course specification
Course title		Practicum in Data Collection and Visualization
Acronym		26E113PVP
Study programme		Electrical Engineering and Computing
Module		Computer Engineering and Informatics
Type of study		bachelor academic studies
Lecturer (for classes)		professor PhD Marko Mišić
Lecturer/Associate (for practice)		professor PhD Marko Mišić
Lecturer/Associate (for OTC)		asst. MA Aleksa Srbljanović, electrical and computer engineer asst. MA Miloš Obradović, electrical and computer engineer asst. MA Mihajlo Ogrizović, electrical and computer engineer asst. MA Matija Dodović, electrical and computer engineer
ESPB		3.0	Status	elective
Condition		none
The goal		The aim of this course is for students to acquire practical knowledge and skills in collecting, processing, storing, and visualizing data using modern tools and technologies, with a focus on web data collection, data analysis, and interactive visualization.
The outcome		Upon completing the course, students will be able to automatically collect data from the web, process and analyze data in various formats, and create both static and interactive visualizations.
Contents
Contents of lectures		Basic concepts in working with data - structured and unstructured data. Representation of data in various formats. Process of indexing and collecting data from the web. Preprocessing of collected data from various sources. Techniques for processing and visualizing data.
Contents of exercises		Implementation of a web indexer and web scraper using libraries such as BeautifulSoup, Scrapy. Data storage formats (CSV, JSON, XML). Data processing techniques with the Pandas library. Jupyter Notebook environment for data analysis. Data visualization methods using Matplotlib and JavaScript D3. Interactive visualization with tools such as Tableau and Plotly.
Literature
R. Lawson, Web scraping with Python, Packt Publishing Ltd, 2015. (Original title) W. McKinney, Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3rd edition, O'Reilly Media, 2022. (Original title) M.O. Ward, G. Grinstein, D. Keim, Interactive data visualization: foundations, techniques, and applications, AK Peters/CRC Press, 2010. (Original title) M. Meier, Mastering Tableau 2023: Implement advanced business intelligence techniques, analytics, and machine learning models with Tableau, 4th edition, Packt Publishing, 2023. (Original title) H. da Rocha, Learn D3.js: Create interactive data-driven visualizations for the web with the D3.js library, 1st edition, Packt Publishing, 2019. (Original title)
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
1	1	0.5
Methods of teaching		Lectures and laboratory exercises.
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures			Test paper	30
Practical lessons		70	Oral examination
Projects
Colloquia
Seminars