COURSE UNIT TITLE

Offered By

BUSINESS ADMINISTRATION (English)

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR AYSUN KAPUÇUGIL IKIZ

Offered to

BUSINESS ADMINISTRATION (English)

Course Objective

The main purpose of this course is to discuss the role of business analytics in business administration; to conduct analyses in the scope of business analytics and business intelligence for discovering the related technologies and developing the right strategies to adopt in business.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Recomended or Required Reading

1.Textbooks

Pochiraju, B. & Seshadri,S. (2019). Essentials of Business Analytics: An Introduction to the Methodology and its Applications, Springer Cham. https://doi.org/10.1007/978-3-319-68837-4

Evans, J. Business Analytics, Global Edition, 3rd Edition, Pearson

2. Software and Coding Platforms (vary based on the accessibility conditions).

Main platform:
Google Colab

Possible platforms:
MS Excel
Rapidminer
R, Python
Power BI

Planned Learning Activities and Teaching Methods

* Lessons will be conducted with computer support.
* Theoretical sections will be supported with the relevant business cases. Basic applications will be developed using a programming language and/or analysis environment. Homework can be assigned to increase understanding of these applications.
* Each student will complete three individual assignments, each consisting of a continuation of the previous work, addressing a business problem using business analytics methods.
* The first assignment will be submitted before the midterm exam, and the other two will be submitted consecutively before the final exam.

Assessment Methods

Further Notes About Assessment Methods

1. Exams will evaluate the understanding of the theoretical background of business analytics and related applications in business.
2. Assignments will be assessed based on the approaches and technologies in the proposed work and its alignment with the strategies related to business analytics.

Assessment Criteria

In this course, each student will complete 3 individual assignments. These assignments aim to improve the students' problem-solving and written communication skills. Each of the three assignments will account for 15% of the final grade.

In each assignment, students are required to complete one of the business analytics problem-solving steps using a company dataset (hypothetic or real). The instructor supplies datasets regarding specific companies, guidelines, and template files for each one, and the students are informed about the assignment requirements.

Assignments are requested to be completed by adhering to the following structural elements (including 20 traits in total):

First Assignment--Data Cleaning&Analysis:
* Introduction (20%) (company background, important factors affecting the primary variable in the problem)
* Data Preprocessing (20%) (Explore the data for identifying quality problems, Validate the data, Clean the data)
* Descriptive Analysis and Visualization (50%) (Numerical descriptive statistics -mean, median, standard deviation, minimum, maximum, percentiles; Visuals -Histograms, Bar charts, Boxplots, Cross-tabulations, Scatter plots with Correlation Analysis; investigate the patterns in data)
* The report quality of the assignment (10%) (Assessed based on the following six traits: Logic & Organization, Language, Spelling and Grammar, Development of Ideas, Format.)

Second Assignment--Predictive Modeling with Linear Regression:
* Model Specification (15%)
* Model Building (20%)
* Diagnostics by residual analysis and improved model (25%)
* Model Validation and Prediction (20%)
* Managerial Implications (explaining what the obtained predictions imply for the business and how these predictions can facilitate the decision-making process.) (10%)
* The report quality of the assignment (10%)

Third Assignment -- Predictive Modeling with Logistic Regression:
* Model Specification (15%)
* Model Building (20%)
* Diagnostics by goodness-of-fit and improved model (25%)
* Model Validation and Prediction (20%)
* Managerial Implications (10%)
* The report quality of the assignment (10%)

Responsible Use of Generative AI (GAI):
GAI should be used transparently to support skill development and academic integrity.

GAI is permitted (with source citation & self-assessment): idea generation, outlining, clarifying concepts, proofreading, code hints, and debugging suggestions.
GAI prohibited: generating complete solutions, writing significant sections of reports, generating code/analyses you do not understand, fabricating references or results, or presenting GAI output as entirely your own.

Your responsibilities:
a) At the end of each assignment, include an AI Usage Statement specifying the product/version used.
Submissions without this statement will not be considered for content evaluation.
b) In the statement, explain the benefits and challenges of using AI in the assignment.
c) Specify any specific prompts and sections you saved/edited.
d) Verify all claims, numbers, and code; you are responsible for accuracy.
e) Be prepared for a brief follow-up (oral check or in-class review).

Language of Instruction

English

Course Policies and Rules

1. Plagiarism will result in disciplinary action.
2. It is compulsory to attend at least 70 % of the courses.
3. Assignments will be submitted through the course web page at online.deu.edu.tr by the posted deadlines. Late or emailed work will not be accepted unless approved in advance or covered by documented reasons.
4. Responsible use of Generative AI: See "Assessment Criteria" for details.

Contact Details for the Lecturer(s)

DEU Faculty of Business
Department of Business Administration
Division of Quantitative Methods

Prof. Dr. Aysun KAPUCUGIL IKIZ
aysun.kapucugil@deu.edu.tr
Office No: 125/A
Office Phone #: 0.232.3018226

Assistant Instructor:
Berk Pişkin
berk.piskin@deu.edu.tr
Office No: 129

Office Hours

To be announced later

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes