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Certain prior knowledge can be validated, meaning that you do not have to take all six modules. You can also take a 7.5-credit alternative since we have divided the 30 credits into four courses, each worth 7.5 credits. There can be no validation of prior knowledge in these courses.

The course package is set up as follows:

  • Course 1 (7.5 credits): From Data Gathering to Explorative Data Analysis - comprises modules 1, 2 and 3
  • Course 2 (7.5 credits): Explorative Data Analysis and Visualisation - comprises module 4
  • Course 3 (7.5 credits): Statististical Modelling - comprises module 5
  • Course 4 (7.5 credits): Statistical Report Writing - comprises module 6

Module 1 - Data Collection and Techniques of Measurement

This module examines how you plan an investigation while considering such factors as cost. Different types of collection methods and data systems are presented. Difficulties that may arise betweeen individuals with different roles within a project are highlighted using an example.

Objective: Upon completion of the module, the student shall be able to choose the appropriate means of collecting and summarising data. The student shall also demonstrate understanding of the fact that each individual may have a different perspective on the problem.

Introduction to Module 1

Module 2 - Data Storage and Processing

Module 2 covers different data formats and different means of storing data, for example, client data. Multiple and repeated observations as well as transformation and coding are also covered.

Objective: Upon completion of the module, the student shall be able to describe different data formats and different means of storing data. The student shall also be able to transform and code data as well as work with multiple observations.

Introduction to Module 2

Module 3 - EDA (Explorative Data Analysis) and Visualisation

This module focuses on the way in which the results from data collection are visualised and illustrated in a simple and comprehensible manner.

Objective: Upon completion of the module, the students shall be able to present different data with various diagrams and summarising measurements. The student shall also be able to determine the distribution of data and pinpoint any extreme values. As well as this, the student shall know about the significance of "Simpson's Paradox".

Introduction to Module 3

Module 4 - Modelling

Module 4 focuses on normal distribution and the correlation between variables, regression and confidence intervals as well as the testing of hypotheses.

Objective: Upon completion of this module, the student shall be able to complete a regression analysis, calculate and interpret confidence intervals, and test hypotheses for quantitative and categoric variables.

Introduction to Module 4

Module 5 - Model Assessment

Module 5 covers normality tests and data quality. It also examines logistical regression and common problems that arise with regression analysis as well as a number of different time series models.

Objective: Upon completion of the module, the student shall be able to determine whether the data has a normal distribution and shall be able to conduct logistic regression and time series analyses.

Introduction to Module 5

Module 6 - Report Writing

Module 6 focuses on the writing of statistical reports and on explaining results for those who are familiar with the subject as well as those who are not.

Objective: Upon completion of the module, the students shall be able to write a statistical report and explain the results in the report for those who are familiar with the subject as well as those who are not.

Introduction to Module 6

Commissioned Education: Prerequisites

Please note that if you are purchasing the course as a form of commissioned education, then the prerequisites that are described in each course syllabus do not apply. It is up to the person buying the course, in most cases your employer, who decides whether you can take the course regardless of your previous qualifications.

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