COURSE UNIT TITLE

: BUSINESS ANALYTICS AND DATA MINING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ETC 5008 BUSINESS ANALYTICS AND DATA MINING ELECTIVE 3 0 0 5

Offered By

Electronic Trade

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ONUR DOĞAN

Offered to

Electronic Trade

Course Objective

The main purpose of data mining operations is to find and extract useful information from the data stack. Business Analytics is a field that aims to improve the strategy based on the business related to business activities by utilizing different disciplines. The main aim of this course is to provide students with skills in data mining and business analysis.

Learning Outcomes of the Course Unit

1   Be competent in data collection and editing processes
2   Understand the basic concepts of data analysis at the level of expertise.
3   Evaluate data obtained from data analysis based on cause-effect relationship
4   Be competent in report and present the knowledge which has discovered
5   Provide creative solutions in the workplace or academic fields using software tools.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Data: data types, the quality of the data, the preparation of the data
2 Data preprocessing techniques
3 Data control
4 Data Mining Capabilities
5 Classification Methods: Basic concepts and algorithms, information gain and decision trees
6 Classification Methods: Advanced topics in classification
7 Classification Methods: Advanced topics in classification
8 Midterm
9 Association Analysis: basic concepts and algorithms
10 Association Analysis: advanced concepts
11 Clustering: Basic concepts and algorithms
12 Clustering: advanced concepts
13 Artificial neural networks
14 Web mining, Text mining

Recomended or Required Reading

Main resource:
Han J., Kamber M., "Data Mining Concepts and Techniques", Morgan Kaufmann, 2006.
Supplementary resource
Witten H., Frank E., "Data Mining", Morgan Kaufmann, 2000.
Wu X., Kumar K., "The Top Ten Algorithms in Data Mining", Chapman & Hall, 2009.

Planned Learning Activities and Teaching Methods

Computer, internet, communication tools, course notes, written and visual model practices, presentation tools,

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FCG FINAL COURSE GRADE
3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60


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

Further Notes About Assessment Methods

None

Assessment Criteria

5 learning outcomes shall be accessed through midterm and final exam. The exams will be distributed and collected in digital.

Language of Instruction

Turkish

Course Policies and Rules

Attending at least 70 percent of lectures is mandatory.
Plagiarism of any type will result in disciplinary action.
Students are expected tp participate actively in class discussions.
Students are expected to attend to classes on time.

Contact Details for the Lecturer(s)

onur.dogan@deu.edu.tr
0.232.3012562

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Midterm 1 1 1
Final 1 1 1
TOTAL WORKLOAD (hours) 115

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.111
LO.211
LO.3111
LO.4111
LO.5111

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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