This course introduces the basics of data science as the rapidly emerging most popular domain for researchers and practitioners in the 21st century. It highlights the basic skills to be acquired by a data scientist with various applications from medicine, homeland security, engineering, finance, etc. The objectives of the course are
(1) introducing the concept of knowledge discovery in data and discuss the steps to be followed including the problem definition, data collection, integration and management, data analysis, and visualization.
(2) highlighting the importance of dealing with various aspects of data, including volume, variety, velocity, veracity, value, etc.
(3) introducing the basic statistical and machine learning techniques which could be effectively used for knowledge discovery
(4) covering network modeling and graph analysis as powerful alternative mechanisms for making sense from data
(5) illustrating how data visualize is effective for communication
(6) covering basics of recommendation systems.
Course Content
This course contains; Introduction to Data Science, probability, statistics, linear algebra,Basic data models, Entity-Relationship model, Relational Model and SQL.,From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery.,NoSQL Databases, the case of Mongo DB.,Sources and types of big data, frequent pattern analysis. ,Presentations by students research articles / tools. ,Presentations by students research articles / tools.,Midterm Study,Clustering,Classification,Incremental data analysis and Scalable methods for Data management and analysis. ,Network model and graph analysis. ,Data visualization,Recommendation systems .
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Understands the fundamentals of data science and the different skills a data scientist should have.
2, 9
A, E, F, G
Understanding the fundamentals of data collection, modeling and management of data, which are among the processes of data science.
2, 9
A, E, F, G
Understands basic statistical modeling and analysis within data science processes.
2, 9
A, E, F, G
It analyzes the machine learning algorithms and techniques used to perform the work of data science.
2, 9
A, E, F, G
Understands basic network modeling and graph analysis that can be used to perform the work of data science.
2, 9
A, E, F, G
It expresses the importance of basic approaches to presenting or displaying data and the importance of data presentation for basic communication.
2, 9
A, E, F, G
Teaching Methods:
2: Project Based Learning Model, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to Data Science, probability, statistics, linear algebra
Lecture Notes, Week 1.
2
Basic data models, Entity-Relationship model, Relational Model and SQL.
Lecture Notes, Week 2.
3
From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery.
Lecture Notes, Week 3.
4
NoSQL Databases, the case of Mongo DB.
Lecture Notes, Week 4.
5
Sources and types of big data, frequent pattern analysis.
Lecture Notes, Week 5.
6
Presentations by students research articles / tools.
Literature survey.
7
Presentations by students research articles / tools.
Literature survey.
8
Midterm Study
All the topics till Week 8.
9
Clustering
Lecture Notes, Week 9.
10
Classification
Lecture Notes, Week 10.
11
Incremental data analysis and Scalable methods for Data management and analysis.
Lecture Notes, Week 11.
12
Network model and graph analysis.
Lecture Notes, Week 12.
13
Data visualization
Lecture Notes, Week 13.
14
Recommendation systems
Lecture Notes, Week 14
Resources
No specific text book, notes will be made available, including in class notes, (sometimes) slides, research papers, book chapters, etc.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas.
X
3
Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field.
X
4
Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field.
X
5
Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals.
6
Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements.
7
Independently perceive, design, apply, finalize and conduct a novel research process.
X
8
Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
X
9
Critical analysis, synthesis and evaluation of new and complex ideas in the field.
X
10
Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values.
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
50
Rate of Final Exam to Success
50
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
14
3
42
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
5
10
50
Term Project
0
0
0
Presentation of Project / Seminar
2
30
60
Quiz
5
1
5
Midterm Exam
1
30
30
General Exam
1
45
45
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
232
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(232/30)
8
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
FOUNDATIONS and APPLICATIONS of DATA SCIENCE
EECD1114659
Fall Semester
3+0
3
8
Course Program
Perşembe 16:30-17:15
Perşembe 17:30-18:15
Perşembe 18:30-19:15
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
Third Cycle (Doctorate Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Reda ALHAJJ
Name of Lecturer(s)
Prof.Dr. Reda ALHAJJ
Assistant(s)
Aim
This course introduces the basics of data science as the rapidly emerging most popular domain for researchers and practitioners in the 21st century. It highlights the basic skills to be acquired by a data scientist with various applications from medicine, homeland security, engineering, finance, etc. The objectives of the course are
(1) introducing the concept of knowledge discovery in data and discuss the steps to be followed including the problem definition, data collection, integration and management, data analysis, and visualization.
(2) highlighting the importance of dealing with various aspects of data, including volume, variety, velocity, veracity, value, etc.
(3) introducing the basic statistical and machine learning techniques which could be effectively used for knowledge discovery
(4) covering network modeling and graph analysis as powerful alternative mechanisms for making sense from data
(5) illustrating how data visualize is effective for communication
(6) covering basics of recommendation systems.
Course Content
This course contains; Introduction to Data Science, probability, statistics, linear algebra,Basic data models, Entity-Relationship model, Relational Model and SQL.,From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery.,NoSQL Databases, the case of Mongo DB.,Sources and types of big data, frequent pattern analysis. ,Presentations by students research articles / tools. ,Presentations by students research articles / tools.,Midterm Study,Clustering,Classification,Incremental data analysis and Scalable methods for Data management and analysis. ,Network model and graph analysis. ,Data visualization,Recommendation systems .
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Understands the fundamentals of data science and the different skills a data scientist should have.
2, 9
A, E, F, G
Understanding the fundamentals of data collection, modeling and management of data, which are among the processes of data science.
2, 9
A, E, F, G
Understands basic statistical modeling and analysis within data science processes.
2, 9
A, E, F, G
It analyzes the machine learning algorithms and techniques used to perform the work of data science.
2, 9
A, E, F, G
Understands basic network modeling and graph analysis that can be used to perform the work of data science.
2, 9
A, E, F, G
It expresses the importance of basic approaches to presenting or displaying data and the importance of data presentation for basic communication.
2, 9
A, E, F, G
Teaching Methods:
2: Project Based Learning Model, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to Data Science, probability, statistics, linear algebra
Lecture Notes, Week 1.
2
Basic data models, Entity-Relationship model, Relational Model and SQL.
Lecture Notes, Week 2.
3
From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery.
Lecture Notes, Week 3.
4
NoSQL Databases, the case of Mongo DB.
Lecture Notes, Week 4.
5
Sources and types of big data, frequent pattern analysis.
Lecture Notes, Week 5.
6
Presentations by students research articles / tools.
Literature survey.
7
Presentations by students research articles / tools.
Literature survey.
8
Midterm Study
All the topics till Week 8.
9
Clustering
Lecture Notes, Week 9.
10
Classification
Lecture Notes, Week 10.
11
Incremental data analysis and Scalable methods for Data management and analysis.
Lecture Notes, Week 11.
12
Network model and graph analysis.
Lecture Notes, Week 12.
13
Data visualization
Lecture Notes, Week 13.
14
Recommendation systems
Lecture Notes, Week 14
Resources
No specific text book, notes will be made available, including in class notes, (sometimes) slides, research papers, book chapters, etc.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas.
X
3
Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field.
X
4
Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field.
X
5
Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals.
6
Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements.
7
Independently perceive, design, apply, finalize and conduct a novel research process.
X
8
Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
X
9
Critical analysis, synthesis and evaluation of new and complex ideas in the field.
X
10
Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values.
