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, and (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 overview,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
Understanding of basic network modeling and graph analysis techniques to handle data science tasks.
2, 9
A, E, F, G
1. Understanding the basics of data science and the skill sets distinguishing a data scientist.
2, 9
A, E, F, G
2. Understanding the basics of data collection, modeling and management for data science tasks.
2, 9
A, E, F, G
3. Understanding of basic statistical modeling and analysis for data science tasks.
2, 9
A, E, F, G
4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks.
2, 9
A, E, F, G
4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks.
2, 9
A, E, F, G
6. Understanding of basic approaches to visualize data for effective communication and understanding.
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 overview
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
1. An ability to apply knowledge of mathematics, science, and engineering
X
2
2. An ability to identify, formulate, and solve engineering problems
X
3
3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
5. An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
6. An ability to function on multidisciplinary teams
X
7
7. An ability to communicate effectively
X
8
8. A recognition of the need for, and an ability to engage in life-long learning
X
9
9. An understanding of professional and ethical responsibility
X
10
10. A knowledge of contemporary issues
11
11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
X
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
30
Rate of Final Exam to Success
70
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
8
40
Term Project
0
0
0
Presentation of Project / Seminar
2
24
48
Quiz
5
1
5
Midterm Exam
1
24
24
General Exam
1
24
24
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
183
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(183/30)
6
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 SCIENCE
-
Fall Semester
3+0
3
6
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
First Cycle (Bachelor's 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, and (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 overview,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
Understanding of basic network modeling and graph analysis techniques to handle data science tasks.
2, 9
A, E, F, G
1. Understanding the basics of data science and the skill sets distinguishing a data scientist.
2, 9
A, E, F, G
2. Understanding the basics of data collection, modeling and management for data science tasks.
2, 9
A, E, F, G
3. Understanding of basic statistical modeling and analysis for data science tasks.
2, 9
A, E, F, G
4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks.
2, 9
A, E, F, G
4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks.
2, 9
A, E, F, G
6. Understanding of basic approaches to visualize data for effective communication and understanding.
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 overview
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
1. An ability to apply knowledge of mathematics, science, and engineering
X
2
2. An ability to identify, formulate, and solve engineering problems
X
3
3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
5. An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
6. An ability to function on multidisciplinary teams
X
7
7. An ability to communicate effectively
X
8
8. A recognition of the need for, and an ability to engage in life-long learning
X
9
9. An understanding of professional and ethical responsibility
X
10
10. A knowledge of contemporary issues
11
11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
