IEM756 Data Mining Methods II

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code IEM756
Name Data Mining Methods II
Term 2023-2024 Academic Year
Term Fall and Spring
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. SÜLEYMAN BİLGİN KILIÇ
Course Instructor
1


Course Goal / Objective

Data mining course aims to produce useful information by means of discovering the patterns, basic relationships, interactions, changes, irregularities, rules, and statistically significant structures in the data

Course Content

The course covers the concept of data mining and design of the database, data warehousing and other storage techniques, database or data warehouse server, database objects creation and expansion, creation of database tables, designing and connecting database, creation and designing of the forms and sub forms, creation and designing of database queries, creation of reports, designing and summarizing the data, data cleaning, removing the noisy and inconsistent data, pattern evaluation and identification, data mining (application of intelligent methods to capture data patterns), presentation of information (to perform presentation of information to the users), convert HTML and ASP files to database objects, using and sharing the database on the internet, creation and using data access pages and query design in the data access pages, ensuring the security of the database

Course Precondition

No prerequisites

Resources

Relevant corporate Internet resources

Notes

Related computer packages


Course Learning Outcomes

Order Course Learning Outcomes
LO01 explain the concepts of econometrics
LO02 construct the economic models
LO03 collect, arrange and analyze the data
LO04 students can use an econometric software
LO05 students define informaiton about statistics, operations research and mathematics


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explains contemporary concepts about Econometrics, Statistics, and Operation Research 5
PLO02 Bilgi - Kuramsal, Olgusal Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research 4
PLO03 Bilgi - Kuramsal, Olgusal Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences 3
PLO04 Beceriler - Bilişsel, Uygulamalı Performs conceptual analysis to develop solutions to problems 4
PLO05 Beceriler - Bilişsel, Uygulamalı Models problems with Mathematics, Statistics, and Econometrics 1
PLO06 Beceriler - Bilişsel, Uygulamalı Interprets the results obtained from the most appropriate method to predict the model 4
PLO07 Beceriler - Bilişsel, Uygulamalı Synthesizes the information obtained by using different sources within the framework of academic rules in a field that does not research 3
PLO08 Beceriler - Bilişsel, Uygulamalı Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution 2
PLO09 Beceriler - Bilişsel, Uygulamalı Searches for new approaches and methods to solve problems being faced 4
PLO10 Beceriler - Bilişsel, Uygulamalı Presents analysis results conveniently
PLO11 Beceriler - Bilişsel, Uygulamalı Collects/analyzes data in a purposeful way 3
PLO12 Yetkinlikler - İletişim ve Sosyal Yetkinlik Converts its findings into a master's thesis or a professional report in Turkish or a foreign language 4
PLO13 Beceriler - Bilişsel, Uygulamalı Develops solutions for organizations using Econometrics, Statistics, and Operation Research 3
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research 4
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Leads by taking responsibility individually and/or within the team 1
PLO16 Yetkinlikler - Öğrenme Yetkinliği Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study 2
PLO17 Yetkinlikler - İletişim ve Sosyal Yetkinlik Uses a package program of Econometrics, Statistics, and Operation Research or writes a new code 2
PLO18 Yetkinlikler - İletişim ve Sosyal Yetkinlik Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form
PLO19 Yetkinlikler - Alana Özgü Yetkinlik Interprets data on economic and social events by following current issues
PLO20 Yetkinlikler - Alana Özgü Yetkinlik Applies social, scientific and professional ethical values


Week Plan

Week Topic Preparation Methods
1 Remembering basic data mining methods and statistical concepts Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
2 Discovering the basic interactions and relationships between the variables and dimensionality reduction methods; principal components factor analysis Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
3 Principal components factor analysis; continued Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
4 Nonparametric methods: Artificial neural networks method, Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
5 Artificial neural networks method; continued Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
6 Parametric methods:Logit analysis Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
7 Logit analysis; continued Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
8 Mid-Term Exam Reading relevant parts in the source books according to the weekly program Ölçme Yöntemleri:
Yazılı Sınav
9 Discriminant analysis Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
10 Discriminant analysis; continued Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
11 Hierarchical cluster analysis Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
12 Hierarchical cluster analysis; contiued Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
13 k-nearest neighbor algorithm Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
14 Decision tree classification algorithm Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
15 c4.5 algorithm Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım
16 Term Exams Reading relevant parts in the source books according to the weekly program Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
17 Term Exams Reading relevant parts in the source books according to the weekly program Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 11.05.2023 04:44