MANAGEMENT ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
MAT1008	Mathematical Data Analysis	Spring	2	2	3	5

Basic information

Language of instruction:	English
Type of course:	Must Course
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Hybrid
Course Coordinator :	Dr. Öğr. Üyesi MÜRÜVVET ASLI AYDIN
Course Objectives:	In this course, data mining and computational algorithms used to explore patterns and regularities in the database using Pyhton language, and to make predictions will be discussed. Supervised and unsupervised learning approaches will focus on regression, model finding and cluster analysis.

Learning Outcomes

The students who have succeeded in this course;
In the end of the course students will be able to:
1) Programme in Python software,
2) Understand the problems that can be solved by data science and to look at these problems statistically,
3) extract features of multivariate data sources
4) understand feature selection methods
5) Get basic information about unsupervised learning techniques,
6) Understand the basics of supervised learning and regression techniques,
7) Understand the basics of supervised classification techniques,
8) Understand error analysis and measurements
9) Understand model selection techniques

Course Content

"In this course, necessary machine learning algorithms to collect useful information from data will be taught. The mathematical basis of eigenvalues and eigenvectors will be given by considering the principal components method that selects the most important features that will best express the data. Estimation problems will be emphasized by regression analysis which is the basis of statistical learning. Decision trees, support vector machines, neural networks algorithms, which are supervised learning methods, will be reviewed with their mathematical derivations and their applications will be made on the databases using python libraries. The k-mean value algorithm, which is one of the clustering methods under supervised learning, will be tested on databases. Cross-validation algorithms, one of the model selection techniques, will be used in databases with supervised and unsupervised learning methods. Error analysis, ROC curves and confusion matrices will be tested in computing machine learning algorithms.
"

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Data Analysis
2)	Statistical Learning
3)	Linear Regression
4)	Classification
5)	Classification Continues
6)	Resampling Methods
7)	Linear Model Selection and Regularization Methods
8)	Tree Based Methods
9)	MIDTERM
10)	Tree Based Methods Continue
11)	Support Vector Machines
12)	Unsupervised Learning
13)	Unsupervised Learning Continues
14)	Neural Networks

Sources

Course Notes / Textbooks:	Elements of Statistical Learning (2nd edition), Hastie, Tibshirani and Friedman (2009). Springer-Verlag. 763 pages.
References:	Elements of Statistical Learning (2nd edition), Hastie, Tibshirani and Friedman (2009). Springer-Verlag. 763 pages.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	28	% 0
Laboratory	28	% 0
Quizzes	3	% 15
Homework Assignments	3	% 15
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Laboratory	14	2	28
Study Hours Out of Class	14	1	14
Homework Assignments	3	5	15
Quizzes	3	5	15
Midterms	1	10	10
Final	1	10	10
Total Workload			120

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Build up a body of knowledge in mathematics, science and engineering subjects; use theoretical and applied information in these areas to model and solve engineering problems.
2)	identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose.
3)	Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.)
4)	Devise, select, and use modern techniques and tools needed for engineering management practice; employ information technologies effectively.
5)	Design and conduct experiments, collect data, analyze and interpret results for investigating engineering management problems.
6)	Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently.
7)	Demonstrate effective communication skills in both oral and written English and Turkish.
8)	Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself.
9)	Develop an awareness of professional and ethical responsibility.
10)	Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11)	Know contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions.
12)	Develop effective and efficient managerial skills.