COURSE UNIT TITLE

: BUSINESS ANALYTICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DBA 6206 BUSINESS ANALYTICS ELECTIVE 3 0 0 6

Offered By

Business Administration (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR GÜZIN ÖZDAĞOĞLU

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   Understand the evolution of business analytics and the related technologies;
2   Evaluate learning and reasoning approaches for business applications;
3   Decide on the most suitable business intelligence strategies for organizations based on their strategic objectives;
4   Design and develop target-specific dashboards.
5   Conduct advanced analysis with the help of data mining 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 Interactions among Business Intelligence, Business Analytics, and Data Mining Case Studies
3 Exploring Data Data Types, Data Quality Data Preprocessing, Databases, Data Warehouses, OLAP Cubes
4 Report Design Dashboard Design Key Performance Indicators
5 Fundamental Tasks of Data Mining Classification-1 Decision Trees Logistic Regression
6 Classification-2 Naive Bayes, K-Nearest Neighbour Algorithms, Support Vector Machines
7 Clustering-1 The Fundamentals K-Means Algorithm
8 Clustering-2 Hierarchical Clustering Density-based Clustering
9 Association Rules Apriori, FP-Growth Algorithms
10 Text Mining Web Mining Text Preprocessing Text Clustering Text Classification
11 Text Mining Web Mining Sentiment Analysis Topic Modeling Named Entity Recognition
12 Recommender Systems-1 Collaborative Filtering Approaches
13 Recommender Systems-2 Content-based Filtering Approaches
14 Presentations

Recomended or Required Reading

1.Textbooks
-Business Intelligence and Analytics: Systems for Decision Support (Sharba, Delen, Turban), Pearson.
-Business Intelligence: Managerial Approach, Efraim Turban, Pearson, 2011.
-Introduction to Data Mining , Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson.
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

1) Lessons will be conducted with computer support. Students are expected to attend classes with their personal computers.

2) Theoretical sections will be supported with the relevant business cases. Basic applications will be developed by using a programming language and/or analysis environment.

3) Each student will participate in a group project, and each project will cover an business intelligence application for a business problem.

4) The group projects will also be enhanced with an AI Strategy Canvas.

5) The projects will be presented at the end of the semester

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm and final exam will evaluate the understanding of theoretical background of AI and related applications in business.

Term project will be assessed based on the approaches and technologies in the proposed work and its alignment with business intelligence strategies.

Language of Instruction

English

Course Policies and Rules

Plagiarism will result in disciplinary action.
It is compulsory to attend at least 70 % of the face-to-face courses and strongly recommended for online courses.
Assignments will not be accepted unless submitted on time.

Contact Details for the Lecturer(s)

guzin.kavrukkoca@deu.edu.tr

Office Hours

TBA

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 13 2 26
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparing assignments 7 5 35
Preparing presentations 1 3 3
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 142

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5
LO.11
LO.2335
LO.3353
LO.4343
LO.545344

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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