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Information
| Code | EM0034 |
| Name | Data Analytics |
| Term | 2023-2024 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 |
Course Goal / Objective
The aim of this course is to examine the analysis that can be done in data-oriented approaches and in accordance with today's increasing information value, to examine the basic data processing approaches, to provide the interpretation of data analysis methods and results, and to examine the solution of these approaches with the help of software.
Course Content
Introduction to Data Analytics. Visualization. Probability and Statistics. Inference and modeling. Regression, Machine Learning Methods.
Course Precondition
None
Resources
Ahmed, M., & Pathan, A. S. K. (2018). Data Analytics: Concepts, Techniques, and Applications. CRC Press. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. Albright, S. C., & Winston, W. L. (2014). Business analytics: Data analysis & decision making. Nelson Education.
Notes
Python, pandas, numpy, and sklearn user guide
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Having knowledge and skills about regression methods and applications |
| LO02 | Having knowledge and skills about classification methods and applications |
| LO03 | Having knowledge and skills about clustering methods and applications |
| LO04 | Designing and developing machine learning algorithms |
| LO05 | Gain knowledge and skills to apply innovative data analytics methods |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Conducts scientific research in industrial engineering, understands, interprets and applies knowledge in his/her field domain both in-depth and in-breadth. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquires detailed knowledge for methods and tools of industrial engineering and their limitations. | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Keeps up with the recent changes and applications in the field of Industrial Engineering and examines and learns these innovations when necessary. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Identifies, gathers and uses necessary information and data. | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Has the ability to develop/propose new and/or original ideas and methods, propose new solutions for designing systems, components or processes. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Designs Industrial Engineering problems, develops new methods to solve the problems and applies them. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and performs analytical modeling and experimental research and analyze/solves complex matters emerged in this process. | 4 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Works in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Completes and applies the knowledge by using limited resources in scientific methods and integrates the knowledge in the field with the knowledge form various disciplines. | 5 |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses a foreign language in verbal and written communication at least B2 level of European Language Portfolio. | 2 |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Presents his/her research findings systematically and clearly in oral or written forms in national and international platforms. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Understands social and environmental implications of engineering practice. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Considers social, scientific and ethical values in data collection, interpretation and announcement processes and professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to data analytics | General reading about data analytics | Öğretim Yöntemleri: Anlatım |
| 2 | Data visualization | Information about general data analytics | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Probability and Statistics | Information about basic statistics | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Inference and modeling | Preliminary research on data modeling | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Classification methods (Basic) | Searching datasets for classification problems | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Classification methods (Tree based) | Searching datasets for classification problems | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Classification methods (Ensemble) | Searching datasets for classification problems | Öğretim Yöntemleri: Gösterip Yaptırma |
| 8 | Midterm Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Regression methods (Basic) | Data exploration for regression problems | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Regression methods (Machine Learning) | Data exploration for regression problems | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Regression methods (Network based) | Data exploration for regression problems | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Clustering methods (Basic) | Data exploration for clustering problems | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Clustering methods (Advanced) | Data exploration for clustering problems | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Matlab software applications | Preliminary research on Matlab | Öğretim Yöntemleri: Örnek Olay, Gösterip Yaptırma |
| 15 | Python software applications | Preliminary research on Python | Öğretim Yöntemleri: Örnek Olay, Gösterip Yaptırma |
| 16 | Final exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Final exam | Exam preparation | 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 | ||