COURSE UNIT TITLE

: BUSINESS ANALYTICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
QMT 2005 BUSINESS ANALYTICS COMPULSORY 3 0 0 4

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

1   LO1: Understand the evolution of business analytics and the related technologies;
2   LO2: Evaluate learning and reasoning approaches for business applications;
3   LO3: Decide on the most suitable strategies for organizations based on their strategic objectives;
4   LO4: Design and develop target-specific statistical reports.
5   LO5: Conduct advanced analysis with the help of statistics, business intelligence, and related approaches.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Business Analytics, The Core Concepts Analysis Platforms Application Examples
2 Exploring Data
3 Descriptive Statistics: Summarizing Data Basic Statistics and Visualizations
4 Descriptive Statistics: Summarizing Data Basic Statistics and Visualizations
5 Inferential Statistics
6 Inferential Statistics
7 Fundamental Tasks of Data Mining Classification
8 Fundamental Tasks of Data Mining Classification
9 Fundamental Tasks of Data Mining Regression Analysis
10 Fundamental Tasks of Data Mining Regression Analysis
11 Clustering Association Rules
12 Clustering Association Rules
13 Project Presentations
14 Project Presentations

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
2. Software and Coding Platforms (vary based on the accessibility conditions). 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 by using a programming language and/or analysis environment. Regarding these applications, homeworks can be assigned to increase understanding.
* Each student will participate in a group project, and each project will cover a comprehensive application for a business problem.
* The projects will be presented at the end of the semester.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MT Midterm
2 TP TermProject
3 PRS Presentation
4 BNS BNS MT * 0.30 +TP * 0.40 + PRS * 0.30


Further Notes About Assessment Methods

1. Midterm exam will evaluate the understanding of the theoretical background of business analytics and related applications in business.
2. Term project will be assessed based on the approaches and technologies in the proposed work and its alignment with the strategies related to business analytics.
3. Presentations will be assessed over the expected presentation skills as well as students' mastery of the subject.

Assessment Criteria

In this course, each student is working on a term project as a team member. The term project aims to improve the students' problem-solving and written/oral communication skills. Depending on their preferences, students form their teams. Maximum number of team members are limited to 5.

The team is required to design a complete business analytics application using a company dataset (hypothetetic or real). For completing this project, datasets regarding specific companies, guidelines and a template file are supplied by the instructor and the students are informed about the project requirements.

The project work is requested to be completed by adhering the following structural elements (including 18 traits in total):
* Introduction (10%) (company background, important factors affecting the primary variable in the problem)
* Data Preprocessing (18%) (Explore the data for identifying quality problems, Validate the data, Clean the data)
* Descriptive Analysis and Visualization (18%) (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)
* Predictive Modeling I: Regression (24%) (Model Specification, Model Building, Diagnostics by residual analysis and improved model, Model Validation and Prediction)
* Predictive Modeling II: Logistic Regression (24%) (Model Specification, Model Building, Diagnostics by residual analysis and improved model, Model Validation and Prediction)
* Managerial Implications (6%) (explaining what the obtained predictions imply for the business and how these predictions can facilitate the decision making process.)

Written/oral communication skills are assessed with the help of presenting project work. The presentation are examined in three ways as including 12 traits in total:

1. The report quality of the term project (45%)is also assessed based on the following six traits: Logic & Organization, Language, Spelling and Grammar, Development of Ideas, Format.
2. Presentation quality (from video recording) (45%) is assessed based on these traits: Clarity and Organization, Logical flow of the presentation, Clear and concise slides or visuals., Effectiveness in conveying information (verbal and non-verbal cues), using of visuals & Relevance and effectiveness of visual aids (charts, graphs, etc.).
3. Presentation (in person) (10%): Understanding the questions accurately, Answering the questions effectively.

In project work, it is critical that all team members contribute together as well as provide quality content. The Peer Evaluation form is used to assess each team member's individual contribution by allowing them to evaluate both their own performance and that of other team members. Based on these peer evaluation scores, a multiplier coefficient is calculated to reflect each group member's contribution to the project. Therefore, students get their term project grade individually according to their contributions.

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 not be accepted unless submitted on time.

Contact Details for the Lecturer(s)

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

Office Hours

To be announced later

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 15 15
Preparing assignments 4 3 12
Preparing presentations 1 8 8
Project Preparation 1 20 20
Midterm 1 1,5 2
TOTAL WORKLOAD (hours) 111

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.111535133
LO.233535133
LO.333535433
LO.433535433
LO.533535233