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Information
| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMETRICS (MASTER) (WITH THESIS) | |
| Code | IEM756 |
| Name | Data Mining Methods II |
| Term | 2024-2025 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 |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
data mining and database management, develop the ability to analyze large datasets, and enhance skills in managing data security and access processes. Improving analytical thinking in data processing and knowledge discovery is aimed.
Course Content
The course content covers the concept of data mining and database design, examining data warehousing and other storage techniques. The use of database and data warehouse servers is emphasized, along with the creation, expansion, and design of database objects and tables. The development of database forms and subforms, query design, report generation, and data summarization are explored. The process of cleaning noisy and inconsistent data, capturing and evaluating data patterns using data mining methods is analyzed. Information presentation techniques are discussed to effectively deliver extracted knowledge to users, while the conversion of database objects into HTML and ASP files and their use in online environments are examined. The creation and use of data access pages, query design, and database security management are also covered.
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 |
| LO06 | Detects patterns, relationships, interactions, and anomalies in data to generate meaningful insights. |
| LO07 | Conducts data analysis using various data mining algorithms. |
| LO08 | Shares database objects over the internet and manages data access processes. |
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 of 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 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | 2 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | 4 |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | 1 |
| PLO17 | 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 |
| 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 | Research | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Discovering the basic interactions and relationships between the variables and dimensionality reduction methods; principal components factor analysis | Research | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 3 | Principal components factor analysis; continued | Research | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 4 | Nonparametric methods: Artificial neural networks method, | Research | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Artificial neural networks method; continued | Research | Öğretim Yöntemleri: Anlatım |
| 6 | Parametric methods:Logit analysis | Research | Öğretim Yöntemleri: Anlatım |
| 7 | Logit analysis; continued | Research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Discriminant analysis | Research | Öğretim Yöntemleri: Anlatım |
| 10 | Discriminant analysis; continued | Research | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 11 | Hierarchical cluster analysis | Research | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 12 | Hierarchical cluster analysis; contiued | Research | Öğretim Yöntemleri: Anlatım |
| 13 | k-nearest neighbor algorithm | Research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Decision tree classification algorithm | Research | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 15 | c4.5 algorithm | Research | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Term Exams | 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 | ||