Data Analysis Skills for Accounting and Control

2019-2020

Course Objective

The purpose of this course is to explore and provide an understanding of
big data and data analytics for accounting and control (Bridging theory
and practice – knowledge), and to teach students to solve problems using
structured programming (Academic and research skills).

After following this course, you:
- will have an understanding of the current and potential future impact
of big data and data analytics on the practice of accounting and
control;
- will be able to write and evaluate basic scripts in the programming
language Python.

Course Content

It is evident that technological developments, such as the wide
availability of ‘big data’, are increasingly and fundamentally changing
the roles, tasks and responsibilities of people working in the various
areas of accounting and control, such as accountants, auditors and
controllers. At the same time, the term ‘data analytics’ is used so
widely that it is not easy to get it clearly into focus. This course is
intended as an introduction to this field, giving students both an
overview of issues and trends, and hands-on experience of what data
analytics in accounting and control may look like.
This course will discuss the main implications of developments in data
analytics for accounting and control and some of the major
applications. The course will also discuss many conceptual issues
related to big data and data analytics, such as different types of
research (e.g., exploratory vs. explanatory vs. predictive), research
designs (e.g., cross-sectional vs. longitudinal) and data (i.e.,
structured vs. unstructured), and multivariate and inferential
statistics, in order to provide students with a better understanding of
the possibilities and limitations of data analytics in accounting and
control. The course has a strong practice component, as it will teach
students to solve relevant problems by writing basic scripts in Python,
a high-level, multi-purpose programming language that is widely used for
data analytics purposes in many areas.

Teaching Methods

Lectures, Tutorials

Method of Assessment

Written exam, Assignments

Entry Requirements

None.

Literature

To be announced.

General Information

Course Code E_ACC_DASAC
Credits 6 EC
Period P5
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. M. Schoute
Examiner dr. M. Schoute
Teaching Staff

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Study Group
Target audiences

This course is also available as: