EM0034 Data Analytics

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

Information

Code EM0034
Name Data Analytics
Term 2024-2025 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

Update Time: 15.05.2024 04:10