ECTS - Introduction to Data Science

Introduction to Data Science (SE422) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Introduction to Data Science SE422 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery
Learning and Teaching Strategies .
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives
Course Learning Outcomes The students who succeeded in this course;
Course Content Python programming language for data science, data scraping, data manipulation, data visualization, use of vectors and matrices in data science, review of statistical concepts for data science, conditional probability, Bayes?s theorem, normal distribution, prediction, regression, classification and clustering.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation

Sources

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury - -
Final Exam/Final Jury - -
Toplam 0 0
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems.
2 Develop solutions using different technologies, software architectures and life-cycle approaches.
3 An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices.
4 An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements.
5 Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects.
6 An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain.
7 An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8 Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies.
9 An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10 Promote the development, adoption and sustained use of standards of excellence for software engineering practices.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury
Prepration of Final Exams/Final Jury
Total Workload 0