CMP4336 Introduction to Data MiningBahçeşehir UniversityDegree Programs SPEECH AND LANGUAGE THERAPYGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
SPEECH AND LANGUAGE THERAPY
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP4336 Introduction to Data Mining Fall 3 0 3 6
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Recommended Optional Program Components: None
Course Objectives: In this course, data mining algorithms and computational paradigms that are used to extract useful knowledge, extract patterns and regularities in databases, and perform prediction and forecasting will be discussed. Supervised and unsupervised learning approaches will be covered with a focus on pattern discovery and cluster analysis.

Learning Outcomes

The students who have succeeded in this course;
1. Be able to understand Data Gathering and Pre-processing
2. Become familiar with Frequent Item Set Detection
3. Be able to understand Association Rule Mining
4. Be able to understand Classifiers, and their benefits
5. Be able to use Clustering
6. Be able to understand Clustering Evaluation

Course Content

1.Introduction to Basic Concepts
2.Data Exploration
3.Classification
4.Clustering
5.Dimensionality Reduction
6.Frequent Item Set Mining
7.Association Rule Mining

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to Basic Concepts None
2) Data Exploration: Summary Statistics, Visualization, OLAP and Multi-dimensional Data Analysis None
3) Data Pre-Processing, Transformation, Normalization, Standardization None
4) Classification and Regression: Model Selection and Generalization, Decision Trees, Performance Evaluation None
5) Classification: Bayesian Decision Theory, Parametric Classification, Naive Bayes Classifier, Instance-Based Classifiers
6) Classification None
6) Classification and Regression: Artificial Neural Networks, Support Vector Machines
7) Midterm I Review of all topics covered so far
8) Clustering: Partitioning and Hierarchical Algorithms None
9) Clustering: Density-Based Algorithms
10) Cluster Evaluation, Comparing Clusterings None
11) Midterm II none
12) Dimensionality Reduction none
13) Frequent Item Set Mining none
14) Association Rule Mining none

Sources

Course Notes / Textbooks: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar
References: Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber and Jian Pei

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 2 % 20
Project 1 % 20
Midterms 2 % 20
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 16 32
Project 5 15
Homework Assignments 6 12
Midterms 8 28
Final 6 26
Total Workload 155

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) To use theoretic and methodological approach, evidence-based principles and scientific literature in Speech and Language Therapy field systematically for practice.
2) To have theoretic and practical knowledge for individual's, family's and the community's health promotion and protection.
3) To use information and health technologies in practice and research in the field of Speech and Language Therapy.
4) To communicate effectively with advisee, colleagues for effective professional relationships.
5) To be able to monitor occupational information using at least one foreign language, to collaborate and communicate with colleagues at international level.
6) To use life-long learning, problem-solving and critical thinking skills.
7) To act in accordance with ethical principles and values in professional practice.
8) To take part in research, projects and activities within sense of social responsibility and interdisciplinary approach.
9) To be able to search for literature in health sciences databases and information sources to access to information and use the information effectively.
10) To take responsibility and participate in the processes actively for training of other therapist, education of health professionals and individuals about speech and languege therapy.
11) To carry out speech and languge therapy practices considering cultural differences and different health needs of different groups in the community.