Courses

Research Methodology and Quantitative Data Analysis

Research Methodology and Quantitative Data Analysis This winter semester course focuses on research methodology with thematic modules for scientific approaches, research building blocks, important data collection tools, basic research stages, description of research methods: object, design, advantages, and disadvantages. This course is a highly recommend course if you want to follow this specific specialization. The practical part introduces the use of the SPSS statistical package with modules for the SPSS environment, data entry, control and correction, data processing, descriptive and inductive statistical parameter estimation and case-control methods in real-time digital media research and interactive data environments. The learning objectives of the course are: Participants to understand the theoretical approaches, characteristics, and differences of quantitative and qualitative research methods, to acquire basic knowledge of the necessary research tools and stages, to have a complete view of the various research methods and to acquire the knowledge and skills necessary to theoretically design and conduct scientific research. Also, the students will work with the SPSS statistical package, import, control, edit and save data into digital files, process and analyze research data using descriptive and inductive statistics, and control research hypotheses using the SPSS statistical package.

Methodology for User-Centered Design

This winter semester course focuses on the methodology of the user-centered design of building digital interactive environments. These new environments need to be designed with rules that guarantee usability, reduce complexity, support increased levels of interaction, adapt to each user’s needs and preferences, ensure a user-friendly environment and ergonomics, as well as human-centered design, and easy navigation of the users. At the first stage of analysis, the purpose, the audience, the content and the schedule for implementing such an environment are decided. At the design stage, the type and degree of interaction, navigation, and aesthetics of the interaction environment are determined. The next stage of production is the implementation of prototypes of the interaction environment using specialized software. The practical lessons at the lab are devoted to the use of these tools. The final stage describes ways of evaluating a digital interactive environment by first checking the technical integrity of the environment but mainly its usability and the degree of acceptance by the end-user through technical interviews, questionnaires, qualitative and quantitative measurements.

Big Data Analysis and Communication (Data Journalism)

This summer semester course begins with an introduction to Python programming. Unstructured real-world data sets are cleaned, analyzed, and processed, highlighting contemporary journalistic practices. Participants are familiar with a variety of data formats and methods for storing, accessing, and processing information, such as comma-separated documents, interaction with Web and JSON APIs, document libraries, regular expressions, text mining, SQL databases, and more. Students also have to deal with less accessible data, create web scrapers and turn difficult-to-use PDFs into useful information. The course also includes basic principles of data visualization, as well as leveraging well-known tools such as Google and StackOverflow to solve problems. The ultimate goal of the course is to familiarize participants with (big) data planning and processing. For this students will utilize Python, basic statistical analysis, HTML, CSVs, APIs, SQL, regular expressions, PDF processing, pandas, BeautifulSoup, Jupyter / IPython Notebooks, git / GitHub, StackOverflow, data cleaning, command-line tools and more.

Machine Learning Algorithms in Data mining and Processing

Machine learning and data science are an integral part of the processing and understanding of large data sets. Machine learning algorithms can help with most modern applications, such as searching for associations, analyzing emotions, opinions, and customer reviews, or searching for information in large volumes of documents. As part of this course, which is also taught in the summer semester, through supervised and non-supervised learning, participants are invited to implement best practices, generate opinions and understand how they can better focus their information research efforts on large datasets. The course also aims to develop a critical overview of the applications of machine learning algorithms, revealing the pitfalls and biases that are hiding behind hand coded datasets. The ultimate goal of the course is to develop skills in data analysis, pattern recognition, and knowledge mining. To this end, the course content includes modules and tools such as linear regression, clustering, text mining, natural language processing, decision trees, machine learning, scikit ‐ learn and more.