COURSE UNIT TITLE

Offered By

BUSINESS ADMINISTRATION

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ENGIN BAĞIŞ ÖZTÜRK

Offered to

BUSINESS ADMINISTRATION

Course Objective

HR Analytics is the systematic application of quantitative data analysis techniques on people-related data in order to increase decision-making quality in HR. HR Analytics can be considered another step to advance human resource management related performance indicators and contribute greater understanding of HR systems.
The purpose of this course is to advance students' understanding of people/talent/workforce analytics. This course integrates different research designs with statistical models to explore and predict certain HR indicators. The course will equip students with necessary knowledge and skills to provide data-driven solutions to HR related issues.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

MNO 2002 - HUMAN RESOURCE MANAGEMENT

Recomended Optional Programme Components

None

Course Contents

Recomended or Required Reading

Fitz-Enz, J., & John Mattox, I. I. (2014). Predictive analytics for human resources. John Wiley & Sons.

Planned Learning Activities and Teaching Methods

1. Lecture
2. Individual Assignments
3. Group Work & Presentations

Assessment Methods

Further Notes About Assessment Methods

Quiz:
There will be two quizzes. Each quiz can be taken a couple of times until a predetermined deadline and only the highest grade will be recorded. The purpose of these online quizzes is to accelerate students' learning curve related to statistical programming we will use during our lectures.

Paper and Presentation (Term Project):
The term project will depend on students to analyze HR-related data. Students will prepare the data and conduct certain types of analyses and share their findings. The projects will be written in a reproducible format and students present their project. Each term project will be carefully examined and graded.

Participation:
Students are expected to criticize particular topics, and comment on their colleagues' ideas. Code suggestions for data analysis is also expected from students. Contribution to the each inclass discussion will be monitored and graded.

Assessment Criteria

1. Students will prepare data.
2. Students will conduct exploratory data analysis.
3. Students will visuliaze important points in the data.
4. Students will create reproducible reports.
5. Students will identify issues in data analytics.

Language of Instruction

English

Course Policies and Rules

Academic integrity is to demonstrate responsbile and honest behaviors and follow ethical principles in academia. All students should respect the intellectual property rights of others. Specifically every student should avoid plagiarism. All types of plagiarism are serious and violate academic integrity policy.
To understand and prevent plagiarism, please see the following link: https://www.plagiarism.org/understanding-plagiarism. During our lectures, a variety of information will be provided but if you have any problems you can ask me.

Contact Details for the Lecturer(s)

Assistant Professor Engin Bağış Öztürk, engin.ozturk@deu.edu.tr

Office Hours

Due to Covid-19 precautions, please send an e-mail to get an online appointment.

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes