Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DATA MINING and BUSINESS INTELLIGENCE | - | Fall Semester | 3+0 | 3 | 5 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Name of Lecturer(s) | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Assistant(s) | |
| Aim | To provide students with research skills to create a data warehouse from databases, use OLAP and data mining models on these data warehouses, and to bring them to the level so that they can write data mining algorithms. |
| Course Content | This course contains; Introduction to Data Mining,Data warehouse –OLAP – ETL process – Database vs. Datawarehouse,Tools Used in Data Mining: Python, R, KNIME, pivoting.(KNIME Installation, Interface, Reading and viewing data),Data Preprocessing (Outliers, Missing data, Normalization, Data Transformation, Binning, Histogram),Association Rules (Shopping Cart Analysis),Supervised Learning, Classification And Decision Trees, Gini / Entropy Brief introduction. (Application: BEARS),Decision Trees (Application: MEDICINE and Wine / KNIME: filtering, , train-test, validation, accuracy, Color Manager),Random Forest, Booststrapping, Ensemble Learning, Perfume Application + Telco Application,Regression and Logistic regression,Neural Network Model (principle, Hyperparameters).,Unsupervised Learning / Clustering (Application: Wholesale Customer Data, Fuzzy C-means clustering, Quality measures),Data Reduction, Synthetic Data generation, PCA, clustering with Scatter Plot (DBSCAN),Big Data concepts – HADOOP, spark, MongoDB, NOSQL,Project Discussion & Evaluation. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Will be able to produce data warehouse from database. | 16, 9 | A |
| 1.1. Explains datamining. | 16 | A |
| 1.2. Defines Data Warehouse. | 16 | |
| 1.3. Designs the Star Data Warehouse Model. | ||
| 1.4. Main tables association designs the data warehouse model. | 6 | F |
| 2. Will be able to relate Data Mining Models to each other. | 10, 9 | A |
| 2.1. Explains data mining models. | 16, 9 | A |
| 2.2. Defines the concept of classification. | 16, 9 | |
| 2.3. Defines the concept of clustering. | 16, 9 | |
| 2.4. Defines the concept of connection analysis. | 16, 9 | |
| 3. will be able to apply the classification model. | 14, 6, 9 | A, F |
| 3.1. Defines supervised learning. | 16, 9 | F |
| 3.2. Defines the concept of class. | 9 | A, F |
| 3.3. sorts statistical algorithms. | 16 | |
| 3.4. Applies decision trees. | 16 | A, F |
| 3.5. Defines decision tree algorithms. | 16 | F |
| 3.6. Defines pruning and purity values. | 9 | |
| 4. Will be able to employ the clustering model. | 14, 16, 9 | A, F |
| 4.1. Defines unsupervised learning. | 16, 9 | F |
| 4.2. Explains the concept of clustering. | 16, 9 | |
| 4.3. Sorts clustering algorithms. | F | |
| 4.4. Applies K-means algorithm. | 14 | F |
| 4.5. Defines genetic algorithms. | F | |
| 5. Will be able employ the connection analysis model. | 14, 16, 9 | A, F |
| 5.1. Interprets connection analysis rules. | ||
| 5.2. Explains the concept of leverage. | 16, 9 | |
| 5.3. Applies the relationship analysis method. | 14, 9 | |
| 5.4. Combines clustering with link analysis. | 2 | |
| 6. Will be able to employ Data Mining Algorithms. | 2 | F |
| 6.1. Employs classification algorithms on data. | 2 | F |
| 6.2. Employs clustering algorithms on data. | 2 | F |
| 6.3. Employs link analysis algorithms on data. | 2 | F |
| 6.4. Interprets data mining application outputs. | 2 | F |
| Teaching Methods: | 10: Discussion Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Data Mining | |
| 2 | Data warehouse –OLAP – ETL process – Database vs. Datawarehouse | |
| 3 | Tools Used in Data Mining: Python, R, KNIME, pivoting.(KNIME Installation, Interface, Reading and viewing data) | Data Analyse Application |
| 4 | Data Preprocessing (Outliers, Missing data, Normalization, Data Transformation, Binning, Histogram) | |
| 5 | Association Rules (Shopping Cart Analysis) | |
| 6 | Supervised Learning, Classification And Decision Trees, Gini / Entropy Brief introduction. (Application: BEARS) | Applied Data Analysis |
| 7 | Decision Trees (Application: MEDICINE and Wine / KNIME: filtering, , train-test, validation, accuracy, Color Manager) | |
| 8 | Random Forest, Booststrapping, Ensemble Learning, Perfume Application + Telco Application | |
| 9 | Regression and Logistic regression | |
| 10 | Neural Network Model (principle, Hyperparameters). | |
| 11 | Unsupervised Learning / Clustering (Application: Wholesale Customer Data, Fuzzy C-means clustering, Quality measures) | |
| 12 | Data Reduction, Synthetic Data generation, PCA, clustering with Scatter Plot (DBSCAN) | |
| 13 | Big Data concepts – HADOOP, spark, MongoDB, NOSQL | |
| 14 | Project Discussion & Evaluation |
| Resources |
| 1.Veri madenciliği kavram ve algoritmaları, Gökhan Silahtaroğlu, Papatya Yayıncılık. Knime Application: https://docs.knime.com/ |
| 1. Data Mining Introductory and Advanced Topics, Margaret H. Dunham, Prentice Hall. 2. Data Mining Concepts and Techniques , J. Han & M. Kamber, Morgan Kaufman. 3. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results , Bernard Marr, Wiley, 2016 4. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil ,2017 5. Naked Statistics: Stripping the Dread from the Data, Charles Wheelan, 2013 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Defines the theoretical issues in the field of information and management. | X | |||||
| 2 | Describes the necessary mathematical and statistical methods in the field of information and management. | X | |||||
| 3 | Uses at least one computer program in the field of information and management. | X | |||||
| 4 | Sustains proficiency in a foreign language requiredor information and management studies. | ||||||
| 5 | Prepares informatics/software projects and work in a team. | ||||||
| 6 | Constantly updates himself / herself by following developments in science and technology with an understanding of the importance of lifelong learning through critically evaluating the knowledge and skills that s/he has got.7. Uses theoretical and practical expertise in the field of information and management | X | |||||
| 7 | Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication skills. | X | |||||
| 8 | Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication. | ||||||
| 9 | Adopts organizational / institutional and social ethical values. | ||||||
| 10 | Within the framework of community involvement adopts social responsibility principles and takes initiative when necessary. | ||||||
| 11 | Uses and analyses basic facts and data in various disciplines (economics, finance, sociology, law, business) in order to conduct interdisciplinary studies. | X | |||||
| 12 | Writes software in different platforms such as desktop, mobile, web on its own and / or in a team. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 2 | 10 | 20 | |||
| Resolution of Homework Problems and Submission as a Report | 12 | 2 | 24 | |||
| Term Project | 2 | 7 | 14 | |||
| Presentation of Project / Seminar | 3 | 1 | 3 | |||
| Quiz | 1 | 4 | 4 | |||
| Midterm Exam | 1 | 15 | 15 | |||
| General Exam | 1 | 20 | 20 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 142 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(142/30) | 5 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DATA MINING and BUSINESS INTELLIGENCE | - | Fall Semester | 3+0 | 3 | 5 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Required |
| Course Coordinator | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Name of Lecturer(s) | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Assistant(s) | |
| Aim | To provide students with research skills to create a data warehouse from databases, use OLAP and data mining models on these data warehouses, and to bring them to the level so that they can write data mining algorithms. |
| Course Content | This course contains; Introduction to Data Mining,Data warehouse –OLAP – ETL process – Database vs. Datawarehouse,Tools Used in Data Mining: Python, R, KNIME, pivoting.(KNIME Installation, Interface, Reading and viewing data),Data Preprocessing (Outliers, Missing data, Normalization, Data Transformation, Binning, Histogram),Association Rules (Shopping Cart Analysis),Supervised Learning, Classification And Decision Trees, Gini / Entropy Brief introduction. (Application: BEARS),Decision Trees (Application: MEDICINE and Wine / KNIME: filtering, , train-test, validation, accuracy, Color Manager),Random Forest, Booststrapping, Ensemble Learning, Perfume Application + Telco Application,Regression and Logistic regression,Neural Network Model (principle, Hyperparameters).,Unsupervised Learning / Clustering (Application: Wholesale Customer Data, Fuzzy C-means clustering, Quality measures),Data Reduction, Synthetic Data generation, PCA, clustering with Scatter Plot (DBSCAN),Big Data concepts – HADOOP, spark, MongoDB, NOSQL,Project Discussion & Evaluation. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Will be able to produce data warehouse from database. | 16, 9 | A |
| 1.1. Explains datamining. | 16 | A |
| 1.2. Defines Data Warehouse. | 16 | |
| 1.3. Designs the Star Data Warehouse Model. | ||
| 1.4. Main tables association designs the data warehouse model. | 6 | F |
| 2. Will be able to relate Data Mining Models to each other. | 10, 9 | A |
| 2.1. Explains data mining models. | 16, 9 | A |
| 2.2. Defines the concept of classification. | 16, 9 | |
| 2.3. Defines the concept of clustering. | 16, 9 | |
| 2.4. Defines the concept of connection analysis. | 16, 9 | |
| 3. will be able to apply the classification model. | 14, 6, 9 | A, F |
| 3.1. Defines supervised learning. | 16, 9 | F |
| 3.2. Defines the concept of class. | 9 | A, F |
| 3.3. sorts statistical algorithms. | 16 | |
| 3.4. Applies decision trees. | 16 | A, F |
| 3.5. Defines decision tree algorithms. | 16 | F |
| 3.6. Defines pruning and purity values. | 9 | |
| 4. Will be able to employ the clustering model. | 14, 16, 9 | A, F |
| 4.1. Defines unsupervised learning. | 16, 9 | F |
| 4.2. Explains the concept of clustering. | 16, 9 | |
| 4.3. Sorts clustering algorithms. | F | |
| 4.4. Applies K-means algorithm. | 14 | F |
| 4.5. Defines genetic algorithms. | F | |
| 5. Will be able employ the connection analysis model. | 14, 16, 9 | A, F |
| 5.1. Interprets connection analysis rules. | ||
| 5.2. Explains the concept of leverage. | 16, 9 | |
| 5.3. Applies the relationship analysis method. | 14, 9 | |
| 5.4. Combines clustering with link analysis. | 2 | |
| 6. Will be able to employ Data Mining Algorithms. | 2 | F |
| 6.1. Employs classification algorithms on data. | 2 | F |
| 6.2. Employs clustering algorithms on data. | 2 | F |
| 6.3. Employs link analysis algorithms on data. | 2 | F |
| 6.4. Interprets data mining application outputs. | 2 | F |
| Teaching Methods: | 10: Discussion Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Data Mining | |
| 2 | Data warehouse –OLAP – ETL process – Database vs. Datawarehouse | |
| 3 | Tools Used in Data Mining: Python, R, KNIME, pivoting.(KNIME Installation, Interface, Reading and viewing data) | Data Analyse Application |
| 4 | Data Preprocessing (Outliers, Missing data, Normalization, Data Transformation, Binning, Histogram) | |
| 5 | Association Rules (Shopping Cart Analysis) | |
| 6 | Supervised Learning, Classification And Decision Trees, Gini / Entropy Brief introduction. (Application: BEARS) | Applied Data Analysis |
| 7 | Decision Trees (Application: MEDICINE and Wine / KNIME: filtering, , train-test, validation, accuracy, Color Manager) | |
| 8 | Random Forest, Booststrapping, Ensemble Learning, Perfume Application + Telco Application | |
| 9 | Regression and Logistic regression | |
| 10 | Neural Network Model (principle, Hyperparameters). | |
| 11 | Unsupervised Learning / Clustering (Application: Wholesale Customer Data, Fuzzy C-means clustering, Quality measures) | |
| 12 | Data Reduction, Synthetic Data generation, PCA, clustering with Scatter Plot (DBSCAN) | |
| 13 | Big Data concepts – HADOOP, spark, MongoDB, NOSQL | |
| 14 | Project Discussion & Evaluation |
| Resources |
| 1.Veri madenciliği kavram ve algoritmaları, Gökhan Silahtaroğlu, Papatya Yayıncılık. Knime Application: https://docs.knime.com/ |
| 1. Data Mining Introductory and Advanced Topics, Margaret H. Dunham, Prentice Hall. 2. Data Mining Concepts and Techniques , J. Han & M. Kamber, Morgan Kaufman. 3. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results , Bernard Marr, Wiley, 2016 4. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil ,2017 5. Naked Statistics: Stripping the Dread from the Data, Charles Wheelan, 2013 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Defines the theoretical issues in the field of information and management. | X | |||||
| 2 | Describes the necessary mathematical and statistical methods in the field of information and management. | X | |||||
| 3 | Uses at least one computer program in the field of information and management. | X | |||||
| 4 | Sustains proficiency in a foreign language requiredor information and management studies. | ||||||
| 5 | Prepares informatics/software projects and work in a team. | ||||||
| 6 | Constantly updates himself / herself by following developments in science and technology with an understanding of the importance of lifelong learning through critically evaluating the knowledge and skills that s/he has got.7. Uses theoretical and practical expertise in the field of information and management | X | |||||
| 7 | Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication skills. | X | |||||
| 8 | Follows up-to-date technology using a foreign language at least A1 level, holds verbal / written communication. | ||||||
| 9 | Adopts organizational / institutional and social ethical values. | ||||||
| 10 | Within the framework of community involvement adopts social responsibility principles and takes initiative when necessary. | ||||||
| 11 | Uses and analyses basic facts and data in various disciplines (economics, finance, sociology, law, business) in order to conduct interdisciplinary studies. | X | |||||
| 12 | Writes software in different platforms such as desktop, mobile, web on its own and / or in a team. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |