Starting Month: February 2026

Discover the fundamentals of data science. Learn statistics, Python for data visualisation, linear modelling, and time series analysis to analyse and interpret data effectively. This course introduces fundamental statistical concepts and methods, from descriptive and inferential analysis to time series and data visualisation, with practical applications using Python. This course is useful for anyone wishing to gain an understanding of the basic principles of bivariate analysis and time series. The standard procedures and terminology for conducting a statistical test are illustrated in detail. The aim of the course is to enable students to carry out simple statistical analyses. To get a good idea of the course content and the availbable material and exercises, see https://github.com/HoGentTIN/dsai-labs After successful completion of the course, participants are able to: visualise data using the appropriate plots create a simple linear model to show the relationship between two or more variables calculate some descriptive measures for data using statistical software discuss some common models for predicting time series and/or detecting anomalies. indicate the importance of testing the accuracy of a model in a methodologically correct way quantify and appropriately test the relationship between two variables Furthermore, they will know: basic rules regarding calculating with probabilities the properties of some important probability distributions some descriptive measures for data different types of plots to represent data visually Topics that are covered in this course: Basic concepts of statistics, sampling Univariate analysis: measures of centre and dispersion, data visualisation Probability theory, the central limit theorem, and statistical tests Bivariate Analysis: chi-square test, 2-sample t-test, correlation and regression, data visualisation Time series analysis Applying Python for data visualisation and analysis The course is completed through self‑study. o support your learning, you will have access to a full set of materials in English. The course consists solely of Python notebooks. On the one hand, there are notebooks in which new terms and concepts are explained. On the other hand, there are notebooks for the exercises. Assessment The exam is open-book and consists of two parts. A Python notebook containing open-ended questions, consisting mainly of exercises. A sample exam is available on the GitHub page, and many past exam questions are also covered throughout the course. For this part, all the solved Python notebooks that have been made available, may be used. A part that makes use of the ANS software environment. In this section, for example, the completed ‘theory’ notebooks are added as attachments to ANS. During the academic year in question, there may be (minor) changes to what is described above. Detailed instructions regarding the exam will be communicated well in advance! Please note: As a U!REKA student, please use your email address from your home institution to avoid fees. As a HOGENT student, you cannot register for this course via this link. Please check with your study counsellor whether this course is also available to you.

Develop core research and writing skills to prepare for your Bachelor’s thesis. This course prepares students for their Bachelor’s thesis by combining IT and language skills to conduct research, manage sources, write and present professionally using LaTeX, and communicate findings clearly and accurately. The student is able to: refer correctly. explain quality criteria of professional literature. explain the characteristics of a good research question. explain the different research methods and distinguish them according to suitability. explain and distinguish the different types of research. format a document in a correctly structured manner and provide it with references using a text typesetting system. write a structured text. select a research method depending on the problem definition. carry out a research method in function of the problem definition. formulate a research question and test it against the characteristics of a good research question. split a research question into concrete sub-questions and associated objectives. explain the importance of correct referencing. explain characteristics of a structured text. distinguish between methods for collecting qualitative data. distinguish and apply methods to collect quantitative data. draw the essence from professional literature. consult professional literature in relation to a specific research domain. distinguish between different types of sources. adjust his search strategy according to the search results. This course prepares you for the Bachelor’s thesis. We pay attention to subject-specific matters on the one hand, but also to clear communication on the other: IT component: formulating a research question working with the LaTeX typesetting system carrying out a literature review maintaining a bibliographic database research methods reporting with LaTeX This open course uses English-language learning materials and is designed as a distance-learning format. You can expect: group work (collaboration with local student(s)), including an oral presentation and defence. An individual exam, taken in the ANS software environment during the official exam period. Language component: structuring a text for a professional audience using a professional writing style avoiding typical language errors critically approaching sources and referring to them correctly writing an abstract giving a good research presentation Please note: As a U!REKA student, please use your email address from your home institution to avoid fees. As a HOGENT student, you cannot register for this course via this link. Please check with your study counsellor whether this course is also available to you.