• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 4011 DATA MINING ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR IPEK DEVECI KOCAKOÇ

Offered to

Econometrics Econometrics

Course Objective

The main objective of the course is to understand data structure and to learn essential techniques to expose hidden relations among them.

Learning Outcomes of the Course Unit

1   To be able to explain exploratory data analysis concept 2   To be able to introduce data mining concept 3   To be able to introduce statistical techniques in order to summarize data 4   To be able to explain tecniques to expose hidden relations among data 5   To be able to introduce techniques in order to realise predictions 6   To be able to report data mining project.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Problem Definition and Project Planning 2 Preparing Data 3 Table: Data Tables, Contingent Table, Summary Table 4 Graphs: Frequency polygrams and histograms, stem and leaf diagrams, boxplots 5 Descriptive Statistics: Central tendency, variation, skewness and kurtosis 6 Interpretive Statistics 7 Comparative Statistics. Visualisation of relations, correlation. Grouping: clustering, decision trees 8 Mid-term 9 Mid-term 10 Artificial Neural Networks, Regression 11 Classification and regression trees 12 Deployment of results 13 Sample applications 14 Advanced Data Mining

Recomended or Required Reading

1-Veri Madenciliği Yöntemleri, Dr.Yalçın Özkan, Papatya Yayıncılık, 2008 2- Myatt, G. J. (2007) Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining, Wiley, New Jersey.

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 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

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparations before/after weekly lectures 12 2 24 Preparation for midterm exam 1 25 25 Preparation for final exam 1 28 28 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 115

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1 LO.6 1