IEM755 Data Mining Methods I

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

Information

Code IEM755
Name Data Mining Methods I
Term 2022-2023 Academic Year
Semester . Semester
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 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

Internet resources of the relevant institution

Notes

Related computer packages


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Gains the ability to think analytically
LO02 Gains the ability to perform statistical analysis using computer
LO03 Gains the ability to produce useful information by means of discovering the patterns, basic relationships, interactions, changes, irregularities, rules, and statistically significant structures in the raw data
LO04 collect and analyze the data
LO05 Interpret the outputs obtained from estimated model


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explains contemporary concepts about Econometrics, Statistics, and Operation Research 3
PLO02 Bilgi - Kuramsal, Olgusal Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research 2
PLO03 Bilgi - Kuramsal, Olgusal Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences 4
PLO04 Beceriler - Bilişsel, Uygulamalı Performs conceptual analysis to develop solutions to problems 5
PLO05 Beceriler - Bilişsel, Uygulamalı Models problems with Mathematics, Statistics, and Econometrics 3
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 1
PLO08 Beceriler - Bilişsel, Uygulamalı Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution 3
PLO09 Beceriler - Bilişsel, Uygulamalı Searches for new approaches and methods to solve problems being faced 2
PLO10 Beceriler - Bilişsel, Uygulamalı Presents analysis results conveniently 4
PLO11 Beceriler - Bilişsel, Uygulamalı Collects/analyzes data in a purposeful way
PLO12 Yetkinlikler - İletişim ve Sosyal Yetkinlik Converts its findings into a master's thesis or a professional report in Turkish or a foreign language 3
PLO13 Beceriler - Bilişsel, Uygulamalı Develops solutions for organizations using Econometrics, Statistics, and Operation Research 5
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research 3
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Leads by taking responsibility individually and/or within the team 4
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 3
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 2
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 The concept of data mining and design of the database, data warehousing and other storage techniques Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
2 Database or data warehouse server, creation and expansion of database objects Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
3 Creation, design and connection of database tables Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
4 Creation and design of the database forms and sub forms Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
5 Creation and design of database queries Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
6 Creation of reports, design and summary of the data Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
7 Data cleaning, removal of the noisy and inconsistent data Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
8 Mid-Term Exam Reading relevant parts in the source boks according to the weekly program Ölçme Yöntemleri:
Yazılı Sınav
9 Pattern evaluation and identification in the data Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
10 Data mining (application of intelligent methods to capture data patterns) Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
11 Presentation of information ( performing presentation of information to the users) Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
12 Converting HTML and ASP files to database objects Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
13 Using and sharing the database on the internet Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
14 Creation and use of data access pages, and query design in the data access pages Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
15 Ensuring the security of the database Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım
16 Term Exams Reading relevant parts in the source boks according to the weekly program Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
17 Term Exams Reading relevant parts in the source boks 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 7 98
Assesment Related Works
Homeworks, Projects, Others 4 3 12
Mid-term Exams (Written, Oral, etc.) 2 2 4
Final Exam 2 2 4
Total Workload (Hour) 160
Total Workload / 25 (h) 6,40
ECTS 6 ECTS

Update Time: 17.11.2022 11:32