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Data Mining

 

PÎRÎ REİS UNIVERSITY

FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES

Course Name : Data Mining

Degree: Bachelor

 

Code

 

 

Year/Semester

 

Local Credits

 

ECTS Credits

 

Course Implementation, Hours/Week

Course

Tutorial

Laboratory

YBS416

2020-2021 (FALL)

3

5

3

 -

-

Department

Management Information Systems

Instructors

 

Asst.Prof. Erkan KIYAK

Contact Information

 

e-mail: ekiyak@pirireis.edu.tr

Office Hours

 

Web page

 

Course Type

 Compulsory

Course Language

English

Course Prerequisites

  

Course Category by Content, %

Basic Sciences

Engineering Science

Engineering Design

Humanities

10

50

30

10

Course Description

This course provides the Data Mining elements. What is data mining and why is it needed, what pre-processes are required for data mining and algorithms used in data mining are discussed. Data mining applications are constructed by using the Python programming language.

Course Objectives

This course aims to provide a framework for understanding the fundamentals of data mining and developing applications by the help of python programming language.

 

Course Learning Outcomes

 

By students who passed from YBS416 successfully;

  1. Explain what data mining is and why it is needed
  2. Analyze data using data mining and find patterns that are frequently encountered
  3. Implements data mining by using Python programming language.

Instructional Methods and Techniques

Lectures, implementation

Tutorial Place

Distant Learning (computer)

Co-term Condition

-

Textbook

-

Other References

Documents and videos to be shared by the instructor.

Homework & Projects

Each student will prepare a Data Mining project at the end of the term,

Laboratory Work

Each student will have the chance to try out the data mining methods they have learned through the software they have installed on their own computers.

Computer Use

Computer use is needed to implement data mining methods.

Other Activities

---

                   

 

Assessment Criteria

Activities

Quantity

Effects on Grading, %

Attendance

 

 

Midterm

 

 

Quiz

10

30

Homework

 

 

Term Paper/Project

1

30

Laboratory Work

 

 

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

 

 

 

Week

 

Topics

Course Outcomes

1

Introduction to Data Mining

I

2

Python software environment installation and introduction (Anaconda, JupyterLab)

I

3

Python Numpy library

I, II

4

Python Pandas library

I, II

5

Exploratory Data Analysis and Data Visualization

I, VI

6

Data Pre-Processing

III, V

7

Simple Linear Regression, Multiple Linear Regression

 

8

Principal Component Regression, ElasticNet Regression

V

9

Ridge Regression, Lasso Regression

III

10

K-Nearest Neighbor (KNN), Support Vector Machines (SVM)

IV, V

11

Artificial Neural Networks (ANN), Random Forest

IV

12

Decision Trees (CART), Logistic Regression

V

13

K-Means, Naive Bayes

 

14

CRISP-DM Methodology

 

 

 

 

 

ECTS / WORKLOAD TABLE

Activity

Count

Hours

Total Workload

Course

14

3

42

Preparation for the lecture

12

1

12

Homeworks

 

 

 

Quiz

10

1

10

Presentations/ Seminars Preparation

 

 

 

Midterm(s) (Exam +Preparation)

     

Group Project

1

20

20

Lab.

 

 

 

Field Work

 

 

 

Final Exam  (Exam +Preparation)

1

35

35

Total Workload

 

 

119

Total Workload/25

 

 

4,76

Course ECTS Credits

 

5

 

 

 

 

 

Relationship between the Course and the Management Information Systems Curriculum

 

 

Program Outcomes

Level of Contribution

1

2

3

a

To use concepts and theories related to different basic functions of business, to analyze and solve related process problems.

 

 

X

b

As managers of the business, making decisions using appropriate analytical and quantitative techniques.

 

 

X

c

Having research skills on how to obtain the necessary resources to evaluate and solve business problems.

 

X

 

d

When adapting  information technology applications, be aware of relevant environmental, social and ethical rules

X

 

 

e

Using a foreign language and communicating verbally and in writing with colleagues from all over the world to follow new developments in business, management and information.

 

 

X

f

To demonstrate teamwork and leadership skills required in business environment and project management.

 

X

 

g

For information technology applications - for interdisciplinary work that can combine social and technical areas - to produce and analyze strategies that will improve operational efficiency, improve creativity and innovation.

 

X

 

h

Identify software, hardware, infrastructure, database and communication requirements according to business requirements, design the necessary components, make the selection, manage the system.

 

 

X

i

To create a project plan for an information system project, to analyze and document the necessary needs, to dominate the systematic database analysis, design and implementation stages, to give technical and managerial contributions, to take responsibility and to manage effectively.

 

X

 

j

To know programming and database logic and to use a modern programming language.

 

 

X

k

To have mastery of administrative / functional applications of enterprise information systems. To have knowledge about types of enterprise software, software selection and purchase decision, to plan and manage software development processes.

 

X

 

         1: Small, 2: Partial, 3: Full

 

 

Management Information Systems Programme Outcomes & Course Outcomes Connectivity Matrix

Course Outcomes

I

II

III

Program

Outcomes

a. 

X

X

 

b. 

X

X

 

c. 

X

 

 

d. 

X

 

 

e. 

X

X

X

f. 

 

X

X

g.

 

X

X

h. 

X

X

X

i. 

 

X

X

j.

 

 

X

k.

 

X

X

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Prepared by

Asst.Prof.Erkan KIYAK

Date

08.10.2020

Signature