The aim of this course is to introduce the students the fundamental concepts in data science and show them how to apply data science approaches such as classification, regression, clustering, deep learning, forecasting, big data analysis, to real world scenarios.
Prerequisite(s)
None
Corequisite(s)
None
Special Requisite(s)
None
Instructor(s)
Professor Ayça Çakmak Pehlivanlı
Course Assistant(s)
Schedule
Tuesday, 16.00-18.45, @CATS
Office Hour(s)
Wednesday, 13.00-14.00, apointment by e-mail (a.pehlivanli@iku.edu.tr), @CATS online
Teaching Methods and Techniques
-PowerPoint Lectures,
-Question - Answer,
-Discussion,
-Case Studies
-Reseach and Application,
-Demonstration,demonstrate
-Relate the demonstrated facts to real life problems,
-Problem solving.
Principle Sources
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison-Wesley, 2006, ISBN -13: 978032132136
- R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd Edition, Wiley Interscience, 2001
- Tom Mitchell. Machine Learning. McGraw Hill, 1997
- James, G., Witten, D., Hastie, T., Tibshirani, R., An introduction to statistical learning, 2013.
- Murphy, K.P., Machine Learning a probabilistic perspective, MIT Press, 2012.
- Han, J., Kamber, M., Data mining concepts and techniques, Morgan Kaufmann, 2006.
- Alpaydın, E., Introduction to machine learning, MIT Press, 2004.
- Norman, G.R., Streiner, D.L., PDQ statistics, Pmph Usa, 2003.
Other Sources
-
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to course content and data analysis
Oral presentation
2. Week
Basic concepts and definitions, learning types,
introducing software and tools that used in data science
Oral presentation
3. Week
Descriptive data analysis, data measurements, big data and data preprocessing
Oral presentation
4. Week
Visualization of numerical data, definition of categoric data and visualization
Oral presentation
5. Week
Descriptive statistics, summary of data
Oral presentation
6. Week
Inferential data analysis, statistical methods
Oral presentation, homework 1
7. Week
Basic linear regression and its applications
Oral presentation
8. Week
Multivariate regression and its applications
9. Week
Basic classification techniques, Decision Trees and its applications
Oral presentation, homework 1 due
10. Week
Basic classification techniques, Naive Bayes and its applications
Oral presentation
11. Week
Advanced classification techniques (Support Vecor Machines) and assesment methods
Advanced clustering tech. (Self Organizing Maps) and dimension reduction, feature selection and feature extraction
Oral presentation, homework 2 due
14. Week
Review, case studies, applications
Oral presentation, homework 3
15. Week
Final exam
16. Week
Final exam
17. Week
Final exam
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
40
Attendance
1
5
Final Exam
1
55
Program Outcomes
PO-1
Knowledge about management processes and management skills
PO-2
Knowledge and application skills related to the methods and competencies required for solving engineering problems
PO-3
Knowledge about developing areas of manufacturing and service sectors
PO-4
Ability to work in multi-disciplinary engineering teams
PO-5
Experience and knowledge of scientific research and publishing within the frame of academic ethics
Learning Outcomes
LO-1
They can combine different data from different data sources, transform the data into a single form and apply basic data cleaning techniques.
LO-2
They can understand what data means, how to use it and model the data on hand using classification, regression, clustering, deep learning, forecasting techniques and statistical models.
LO-3
They can apply visualization techniques on the data on hand and interpret the results.
LO-4
They can define the problems in real world scenarios using data.
LO-5
They can choose the most suitable machine learning techniques for solving real world problems.
