COURSE UNIT TITLE

Offered By

BUSINESS ADMINISTRATION (English)

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ENGIN BAĞIŞ ÖZTÜRK

Offered to

BUSINESS ADMINISTRATION (English)

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

Explanations related to the assessment methods:

Quiz:
Two quizzes are planned as part of the quizzes. Each quiz can be taken multiple times, and only the quiz with the highest score will be recorded in the system. The purpose of these online quizzes is to accelerate the learning curve of the students.

Active Participation:
During our course, students are expected to take the floor, criticize certain topics, and comment on the ideas of other students. In certain weeks, students' suggestions for code for data analysis will also be considered as active participation. Each contribution to the in-class discussion will be monitored and graded.

Term Project:
The term project is mainly related to examining and storytelling with an HR dataset. The project will consist of three parts: data preparation, exploratory data analysis, and focused analysis through storytelling. Each part within the project will be evaluated with its own unique features, expectations related to each one of them will be explained in class.

Term projects will be evaluated with a scale ranging from 1-5: weak, fair, good, very good, and excellent. Weak (0-39%): Does not meet expectations due to missing content, plagiarism, lack of effort, or significant errors in data analysis. Fair (40-59%): Meets some expectations but needs improvement in data analysis, storytelling. May lack depth, insight, or professional polish. Good (60-79%): Meets all expectations with a satisfactory level of data analysis, storytelling, and demonstrating competency in key areas. Very Good (80-89%): Demonstrates strong understanding and critical thinking, exceeding most expectations with well-developed analysis, engaging storytelling. Excellent (90-100%): Exceeds all expectations in all areas, demonstrating exceptional data analysis skills, insightful storytelling.

Presentation:
This refers to the presentation of the term project. It will be evaluated based on professionalism, communication, and visualization. Presentations will be evaluated with a scale ranging from 1-5: weak, fair, good, very good, and excellent. Weak (0-39%): Unorganized, unprofessional formatting, unclear writing, ineffective delivery. Fair (40-59%): Disorganized, unprofessional formatting, unclear writing, distracting delivery. Good (60-79%): Organized, appropriate formatting, clear writing, acceptable delivery. Very Good (80-89%): Well-organized, professional formatting, clear writing, confident delivery. Excellent (90-100%): Flawless organization, professional formatting, clear and concise writing, engaging delivery.

Assessment Criteria

1. Students will prepare data.
2. Students will conduct exploratory data analysis.
3. Students will visualize 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)

Associate Professor Engin Bağış Öztürk, engin.ozturk[at]deu.edu.tr

Office Hours

As a general rule, please send an e-mail before stopping by the office. (Room No: 131/A)
Office hours will be announced in class by the instructor.

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes