COURSE INFORMATON
Course Title Code Semester L+P Hour Credits ECTS
Statistical Data Mining ISB   445 7 3 3 5

Prerequisites and co-requisites Yok
Recommended Optional Programme Components None

Language of Instruction Turkish
Course Level First Cycle Programmes (Bachelor's Degree)
Course Type
Course Coordinator
Instructors
Prof.Dr.HAMZA EROL1. Öğretim Grup:A
Prof.Dr.HAMZA EROL2. Öğretim Grup:A
 
Assistants
Goals
To teach and, give capasity and ability of analyzing big data by aplying data mining methods and algorithms using a computer software.
Content
Computer softwares used for analyzing big data. Weka environment for analyzing big data. Explore application. Experimenter application. Data Flow application. Command Line Interface application. Classification of big data in Explore application. Clustering of big data in Explore application. Association of big data in Explore application. Simple experiment in Experimenter application. Classification of big data in Data Flow application. Clustering of big data in Data Flow application. Association of big data in Data Flow application.

Learning Outcomes
1) Students who attend this course will have capacity and ability of analyzing big data using a software for data mining.


Course's Contribution To Program
NoProgram Learning OutcomesContribution
12345
1
Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics
2
Emphasize the importance of Statistics in life
3
Define basic principles and concepts in the field of Law and Economics
4
Produce numeric and statistical solutions in order to overcome the problems
5
Use proper methods and techniques to gather and/or to arrange the data
6
Utilize computer systems and softwares
7
Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events
8
Apply the statistical analyze methods
9
Make statistical inference(estimation, hypothesis tests etc.)
10
Generate solutions for the problems in other disciplines by using statistical techniques
11
Discover the visual, database and web programming techniques and posses the ability of writing programme
12
Construct a model and analyze it by using statistical packages
13
Distinguish the difference between the statistical methods
14
Be aware of the interaction between the disciplines related to statistics
15
Make oral and visual presentation for the results of statistical methods
16
Have capability on effective and productive work in a group and individually
17
Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs
18
Develop scientific and ethical values in the fields of statistics-and scientific data collection

Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
1 Explore application. Experimenter application. Data Flow application. Command Line Interface application. Lecture
2 Classification of big data in Explore application. Lecture
3 Clustering of big data in Explore application. Lecture
Drilland Practice
Project / Design
4 Association of big data in Explore application. Lecture
Drilland Practice
Project / Design
5 Visualization of big data in Explore application. Lecture
Drilland Practice
Project / Design
6 Simple experiment in Experimenter application. Lecture
Drilland Practice
Project / Design
7 Advanced experiment in Experimenter application. Lecture
Drilland Practice
Project / Design
8 Midterm exam Lecture
Drilland Practice
Project / Design
9 Desings for classification of big data in Data Flow application.
10 Applications for classification of big data in Data Flow application. Lecture
Drilland Practice
Project / Design
11 Desings for clustering of big data in Data Flow application. Lecture
Drilland Practice
Project / Design
12 Applications for clustering of big data in Data Flow application. Lecture
Drilland Practice
Project / Design
13 Designs for association of big data in Data Flow application. Lecture
Drilland Practice
Project / Design
14 Applications for association of big data in Data Flow application. Lecture
Drilland Practice
Project / Design
15 Command Line Interface application. Lecture
Drilland Practice
Project / Design
16-17 Final exam Lecture
Drilland Practice
Project / Design

Recommended or Required Reading
Textbook
Additional Resources