Course Information

Course Name	Turkish	Hesaplamalı Bilimler için İstatistiksel Veri Analizi
	English	Statistical Data Analysis for Computational Sciences
Course Code	MAT 555E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		English
Course Coordinator		Gül İnan Gül İnan
Course Objectives		The course will harmonize statistical theory and data analysis through examples. This course is designed such that: - To provide mathematical, statistical, and computational concepts behind supervised and unsupervised statistical learning methods and algorithms for inference and prediction. - To provide extensions of these methods to high-dimensional settings. - To provide the applications of these methods in real life data sets. - To provide the implementation of these methods in Python.
Course Description		MAT555E is a graduate level course which aims to provide an introduction to commonly used statistical methods for inference and prediction problems in data analysis. The course will harmonize statistical theory and data analysis through examples.
Course Outcomes		A student who completed this course successfully is expected: - To be fluent in the concepts and principles behind supervised and unsupervised statistical learning methods, - To be able to identify which method(s) might be suitable for conducting data analysis on specific real life data sets, - To get familiar with Python Scikit-Learn library, and - To be prepared for more advanced coursework or scientific research in machine learning and related fields immediately following the course.
Pre-requisite(s)		- Knowledge of linear algebra, probability, statistics, and optimization, - Familiarity with Python’s Numpy, Pandas, Matplotlib, Seaborn, Statsmodels, and Scikit-Learn libraries, - Familiarity with at least one computational document such as Jupyter Notebook, Google Colab, or Visual Studio Code.
Required Facilities		Portable Computer
Other		.
Textbook		James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R. New York: Springer. [Available online at https://www.statlearning.com/ ]
Other References		Hastie, T., Tibshirani, R., Friedman, J.H., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer. [Hard copy available at ITU Mustafa Inan Library with CALL #Q325.5 .H37 2009] [Available online at https://hastie.su.domains/ElemStatLearn/] Fan, J., Li, R., Zhang, C.H., and Zou, H. (2020). Statistical Foundations of Data Science. Chapman and Hall/CRC. Deisenroth, M.P., Faisal, A.A., and Ong, C. S. (2020). Mathematics for Machine Learning. Cambridge University Press. [Available online at https://mml-book.github.io/]. VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media, Inc. [Available online at https://jakevdp.github.io/PythonDataScienceHandbook/]. Müller, A.C., and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Inc. [Available online at https://github.com/amueller/introduction_to_ml_with_python].

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