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| Course Description |
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| Course Name |
: |
Data Mining Methods |
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| Course Code |
: |
IEM 755 |
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| Course Type |
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Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
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Assoc.Prof.Dr. S.BİLGİN KILIÇ
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| Learning Outcomes of the Course |
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Gains the ability to produce useful information by means of discovering the patterns, basic relationships, interactions, changes, irregularities, rules, and statistically significant structures in the raw data
Gains the ability to perform statistical analysis using computer
Gains the ability to think analytically
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
Data mining course aims to produce useful information by means of discovering the patterns, basic relationships, interactions, changes, irregularities, rules, and statistically significant structures in the data |
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| Course Contents |
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The course covers the concept of data mining and design of the database, data warehousing and other storage techniques, database or data warehouse server, database objects creation and expansion, creation of database tables, designing and connecting database, creation and designing of the forms and sub forms, creation and designing of database queries, creation of reports, designing and summarizing the data, data cleaning, removing the noisy and inconsistent data, pattern evaluation and identification, data mining (application of intelligent methods to capture data patterns), presentation of information (to perform presentation of information to the users), convert HTML and ASP files to database objects, using and sharing the database on the internet, creation and using data access pages and query design in the data access pages, ensuring the security of the database
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| Language of Instruction |
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Turkish |
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| Work Place |
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Classroom, Compurer Labrotary
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
The concept of data mining and design of the database, data warehousing and other storage techniques |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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2 |
Database or data warehouse server, creation and expansion of database objects |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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3 |
Creation, design and connection of database tables |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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4 |
Creation and design of the database forms and sub forms |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
|
5 |
Creation and design of database queries |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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6 |
Creation of reports, design and summary of the data |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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7 |
Data cleaning, removal of the noisy and inconsistent data |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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8 |
Midterm Exam |
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|
9 |
Pattern evaluation and identification in the data
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Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
|
10 |
Data mining (application of intelligent methods to capture data patterns) |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
|
11 |
Presentation of information ( performing presentation of information to the users) |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
|
12 |
Converting HTML and ASP files to database objects |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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13 |
Using and sharing the database on the internet |
Students will be prepared by studying relevant subjects from source books according to the weekly program |
Lecture and computer application in the laboratory |
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14 |
Creation and use of data access pages, and query design in the data access pages |
Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
|
15 |
Ensuring the security of the database
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Reading relevant parts in the source boks according to the weekly program |
Lecture and computer application in the laboratory |
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16/17 |
Final Exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Veri Madenciliği: Kavram ve Algoritmaları
Doç, Dr. Gökhan SİLAHTAROĞLU
Veri Madenciliği (Kavram ve Teknikler)
Aysan Şentürk
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| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
80 |
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Homeworks/Projects/Others |
10 |
20 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Explains Econometric concepts |
3 |
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2 |
Equipped with the foundations of Economics, develops Economic models |
3 |
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3 |
Models problems using the knowledge of Mathematics, Statistics, and Econometrics |
4 |
|
4 |
Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems |
5 |
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5 |
Collects, edits, and analyzes data |
5 |
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6 |
Uses advanced software packages concerning Econometrics, Statistics, and Operation Research |
5 |
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7 |
Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations |
5 |
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8 |
Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. |
3 |
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9 |
Questions traditional approaches and their implementation and develops alternative study programs when required |
4 |
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10 |
Recognizes and implements social, scientific, and professional ethic values |
4 |
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11 |
Gives a consistent estimate for the model and analyzes and interprets its results |
5 |
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12 |
Takes responsibility individually and/or as a member of a team; leads a team and works effectively |
3 |
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13 |
Defines the concepts of statistics, operations research and mathematics. |
4 |
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14 |
Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously |
3 |
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15 |
Follows the current issues, and interprets the data about economic and social events. |
3 |
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16 |
Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently |
3 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
10 |
6 |
60 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
2 |
2 |
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Final Exam |
1 |
2 |
2 |
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Total Workload: | 148 |
| Total Workload / 25 (h): | 5.92 |
| ECTS Credit: | 6 |
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