Courses

Data science

Visual to show that this course helps to improve transversal skills by covering Data science.

Course overview

This course introduces fundamental statistical concepts and methods, from descriptive and inferential analysis to time series and data visualisation, with practical applications using Python.

Course content

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.

  1. 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.
  2. 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.

Learning outcomes

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

Enrollment

Enroll

Course information

Category

Transversal skills

Format

Online

Target groups

U!REKA students

Academic year

Starting month

Language

English

Duration​

8/02/2027-2/07/2027

Contact

Organising institution​

HOGENT University of Applied Sciences and Arts

Places reserved for U!REKA participants​

Limited

Assessment

The exam is open-book and consists of two parts (see details above in Course content)

Credit/degree/certificate

4 ECTS

Course fee

None

Prerequisites

A basic knowledge of Python is required.

Level

Bachelor
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