Course Name : Data Mining

Degree: Associate's Degree

Code

Year/Semester

Local Credits

ECTS Credits

Course Implementation, Hours/Week

Course

Tutorial

Laboratory

BIP 2020

2/2 (Spring)

3

3

1

0

2

Department / Programme

Computer Programming

Instructors

Dr. Füsun Er

Contact Information

fer@pirireis.edu.tr

Office Hours

Web page

www.pirireis.edu.tr

Course Type

 Compulsory

Language

Turkish

Course Prerequisites

Course Category by Content, %

Basic Sciences

Engineering Science

Engineering Design

Humanities

0

100

0

0

Course Description

• The concept of datamining
• Weka datamining tool
• Descriptive statistical analysis
• IoT data analysis

Course Objectives

To have knowledge about the concept of data mining. Data types, statistical description and methods of representation; data similarity analysis; data preprocessing steps; pattern, association and correlation mining. To be able to classify with machine learning methods. Data analysis using Weka.

Course Learning Outcomes

Students who have successfully completed this course will have knowledge of the following topics.
1. Data mining concept
2. Data types, data representation and data preprocessing methods
3. Data descriptive statistical analyzes
4. Using Weka data mining tool
5. Clustering methods and machine learning
6. Data analysis from Smart Ship applications.

Instructional Methods and Techniques

Power point presentation, Computational Application

Tutorial Place

Class, Computer Laboratory

Co-term Condition

Textbook

1. Data Mining Concepts and Techniques, Third Edition, Jiawei Han, Micheline Kamber, Jian Pei , 2012 by Elsevier Inc.
2. Data Mining Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, 2005 by Elsevier Inc

Other References

http://www.coursera.com

Homework & Projects

Database System Project will be given to the students

Laboratory Work

2 hours of laboratory work each week

Computer Use

Power-Point, Word, Excel, Weka, Basic Programming Skills

Other Activities

None

Assessment Criteria

Activities

Quantity

Effects on Grading, %

Attendance

Midterm

1

30

Quiz

Homework

Term Paper/Project

1

10

Laboratory Work

2

20

Practices

Tutorial

Seminar

Presentation

Field Study                                             

Final Exam

1

40

TOTAL

100

Effects of Midterm on Grading, %

60

Effects of Final on Grading, %

40

TOTAL

100

ECTS/

WORKLOAD TABLE

Activities

Count

Hours

Total

Workload

Lecture

11

3

33

Midterm

1

12

12

Quiz

-

-

-

Homework

1

20

20

Term Paper/Project

-

-

-

Laboratory Work

1

3

3

Practices

-

-

-

Tutorial

-

-

-

Seminar

-

-

-

Presentation

-

-

-

Field Study

-

-

-

Final Exam

1

12

12

Total Workload

Total Workload/25

80

Course ECTS Credits

80/25

Week

Topics

Course Outcomes

1

Lecture: The concept of datamining

I

2

Lecture: Descriptive statistics

III

3

Lecture: Data display, data similarity and difference measurements

II

4

Lecture: Introduction to Weka, Project assignments

IV

5

Lecture: Data preprocessing (noise)

II

6

Lecture: Data preprocessing (dimension reduction)

II

7

Lecture: Data warehouse

I

8

Exam: Midterm

I,II,III,IV

9

Lecture: Clustering (k-means)

V

10

Lecture: Machine learning ( support vector machines)

V

11

Lecture: Machine learning ( decision trees)

V

12

Lecture: Machine learning ( artificial neural networks)

V

13

Lab: Labwork

I,II,III,IV,V

14

Project: Project presentations

I,II,III,IV,V

Exam: Final

I,II,III,IV,V

Program Outcomes

Level of Contribution

1

2

3

a

An ability to apply basic knowledge of mathematics, science, and engineering

X

b

To learn basic computer knowledge, to make use of software and hardware components needed in the professional work life

X

c

To think algorithmically and use this ability in conducting software planning

X

d

To define professional work life problems and to be able to solve them

X

e

To code by using uptodate software programming languages

X

f

To design and code software using Internet technologies, to code client/server based programs

g

To learn designing a database and to code programs that have connection with a database

X

h

To gain basic electrics and electronics knowledge related with the computer hardware

i

An ability to apply their knowledge to maritime discipline

X

j

To get responsibility in analyzing, designing, planning and coding phases of a software as well as writing reports in each of these phases. To have non-administrative responsibilities in project production

X

k

To have a general knowledge about computer networks

l

To have basic knowledge about operating systems

m

To attain an ability to communicate written and orally effectively

X

n

To take responsibility and initiative, to make decisions and be creative

o

A recognition of the need for, and an ability to engage in life-long learning

X

p

An understanding of professional and ethical responsibilities

r

To be able to read and understand technical documents written in both Turkish and English, to be able to communicate written and orally effectively

X

Course

outcomes

I

II

III

IV

V

VI

Programme

outcomes

a

b

c

d

e

f

g

h

i

j

k

l

m

n

o

p

r

Edited by

Dr. Füsun Er

Date

23.01.2018

Sign