Kocaeli University / European Credit Transfer System (ECTS)

Name of Lecturer(s)

Associate Prof. Dr. Taner ÜSTÜNTAŞ

Learning Outcomes of the Course Unit
Program Competencies-Learning Outcomes Relation

Learning Outcomes of the Course Unit

1) Learn Python programming language syntax structure
2) Numpy library is learned
3) Pandas library is learned
4) Learn to query and capture outlier data
5) Learns operations made with null data
6) Learns model verification and success evaluation methods
7) Learns regression model, estimation and model tuning methods
8) Learn the PCR and SVR methods

Program Competencies-Learning Outcomes Relation

		Program Competencies
		1	2	3	4
Learning Outcomes
	1	High	Middle	Middle	Middle
	2	Middle	Middle	Middle	Middle
	3	No relation	No relation	No relation	No relation
	4	High	Middle	Low	Low
	5	High	Middle	Low	Low
	6	High	Middle	Middle	Middle
	7	Middle	Middle	Low	Low
	8	High	High	Low	Low

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Introduction to Programming

Course Contents

Data preprocessing will be done with Python libraries, NumPy and Pandas. Then, linear and nonlinear regression methods from Machine Learning algorithms will be explained.

Weekly Schedule

1) Installing Python libraries
2) Python program essentials for data mining
3) Data formatting with the Numpy library
4) Data formatting with the Numpy library
5) Element operations with Pandas library (merge-join)
6) Element operations with Pandas library Grouping Operations
7) Element operations with the Pandas library Aggregate, Transform, Filter, Apply
8) Midterm exam
9) Outlier data query and capture
10) NULL data operations, deletion and value assignment methods
11) Model validation methods
12) Model success evaluation methods
13) Simple linear and multiple linear regression model, estimation and model tuning
14) Principal Component Regression (PCR) processes
15) Support Vector Regression (SVR) operations
16) Final exam

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Question-Answer
4) Question-Answer
5) Question-Answer
6) Question-Answer
7) Question-Answer
8) Problem Solving
9) Problem Solving
10) Problem Solving
11) Problem Solving
12) Problem Solving
13) Problem Solving
14) Problem Solving
15) Problem Solving
16) Problem Solving

Assessment Methods and Criteria

Contribution of Midterm Examination to Course Grade	40%
Contribution of Final Examination to Course Grade	60%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required

Course Unit Title	Course Unit Code	Type of Course Unit	Level of Course Unit	Year of Study	Semester	ECTS Credits
Spatial Data Mining	JJM618	Elective	Doctorate degree	1	Spring	8