Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Graduate School of Sciences
  • Department of Industrial Engineering
  • Master of Arts (MA) Degree
  • Course Structure Diagram with Credits
  • Advanced Data Analytics
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title ENM526 - Advanced Data Analytics
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ ZEYNEP İDİL ERZURUM ÇİÇEK
Mode of Delivery Face to face
Prerequisites There are no prerequisites or co-requisites for this course.
Courses Recomended
Recommended Reading List
Assessment methods and criteria 1 midterm, 1 homework, 1 project, 1 final
Work Placement
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Introduction to data and data analytics, classification and history of data analytics
Week - 2 Basic definitions and management of different data types
Week - 3 Data collection
Week - 4 Exploratory data analysis: Univariate, bivariate and multivariate analysis
Week - 5 Data quality and preprocessing
Week - 6 Missing data problems and solution methods in different types of data
Week - 7 Outlier data and detection methods
Week - 8 Data manipulation (Transformation, rescaling)
Week - 9 Data visualization (Visualization in univariate, bivariate and multivariate datasets, word cloud, infographic, dashboard, storytelling)
Week - 10 Dimensionality reduction (Data fusion, principal component analysis)
Week - 11 Feature selection (Filtering and wrapping based methods)
Week - 12 Frequent pattern mining (Frequent item sets, association rules)
Week - 13 Resampling in imbalanced data sets (Oversampling and undersampling methods)
Week - 14 Project presentations

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Team/Group Work
  • Drill - Practise
  • Problem Solving
  • Report Preparation and/or Presentation
  • Competences
  • Effective use of a foreign language
  • Work in teams
  • Problem solving
  • Applying theoretical knowledge into practice
  • Concern for quality
  • Information Management
  • To work in interdisciplinary projects

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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