TÜRKÇE
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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB445 |
| Name | Statistical Data Mining |
| Term | 2015-2016 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Üniversite Dersi |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
Prof.Dr. HAMZA EROL
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To teach and, give capasity and ability of analyzing big data by aplying data mining methods and algorithms using a computer software.
Course 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.
Course Precondition
Yok
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Students who attend this course will have capacity and ability of analyzing big data using a software for data mining. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics | |
| PLO02 | - | Emphasize the importance of Statistics in life | |
| PLO03 | - | Define basic principles and concepts in the field of Law and Economics | |
| PLO04 | - | Produce numeric and statistical solutions in order to overcome the problems | |
| PLO05 | - | Use proper methods and techniques to gather and/or to arrange the data | |
| PLO06 | - | Utilize computer systems and softwares | |
| PLO07 | - | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | |
| PLO08 | - | Apply the statistical analyze methods | |
| PLO09 | - | Make statistical inference(estimation, hypothesis tests etc.) | |
| PLO10 | - | Generate solutions for the problems in other disciplines by using statistical techniques | |
| PLO11 | - | Discover the visual, database and web programming techniques and posses the ability of writing programme | |
| PLO12 | - | Construct a model and analyze it by using statistical packages | |
| PLO13 | - | Distinguish the difference between the statistical methods | |
| PLO14 | - | Be aware of the interaction between the disciplines related to statistics | |
| PLO15 | - | Make oral and visual presentation for the results of statistical methods | |
| PLO16 | - | Have capability on effective and productive work in a group and individually | |
| PLO17 | - | 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 | |
| PLO18 | - | Develop scientific and ethical values in the fields of statistics-and scientific data collection |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Explore application. Experimenter application. Data Flow application. Command Line Interface application. | ||
| 2 | Classification of big data in Explore application. | ||
| 3 | Clustering of big data in Explore application. | ||
| 4 | Association of big data in Explore application. | ||
| 5 | Visualization of big data in Explore application. | ||
| 6 | Simple experiment in Experimenter application. | ||
| 7 | Advanced experiment in Experimenter application. | ||
| 8 | Midterm exam | ||
| 9 | Desings for classification of big data in Data Flow application. | ||
| 10 | Applications for classification of big data in Data Flow application. | ||
| 11 | Desings for clustering of big data in Data Flow application. | ||
| 12 | Applications for clustering of big data in Data Flow application. | ||
| 13 | Designs for association of big data in Data Flow application. | ||
| 14 | Applications for association of big data in Data Flow application. | ||
| 15 | Command Line Interface application. | ||
| 16 | Final exam | ||
| 17 | Final exam |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 80 | -16 |
| 1. Performance Task (Application) | 20 | -4 |
| 1. Midterm Exam | 80 | -16 |
| 1. Performance Task (Application) | 20 | -4 |
| General Assessment | ||
| Midterm / Year Total | 200 | -20 |
| 1. Final Exam | - | 60 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 15 | 4 | 60 |
| Out of Class Study (Preliminary Work, Practice) | 15 | 1 | 15 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 10 | 2 | 20 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 125 | ||
| Total Workload / 25 (h) | 5,00 | ||
| ECTS | 5 ECTS | ||