Akademik Dersler - Tanıtım

Course Introduction Information


Code - Course Title	İST257 - Introduction to Data Science
Course Type	Area Elective Courses
Language of Instruction	İngilizce
Laboratory + Practice	2+0
ECTS	3.0
Course Instructor(s)	DOÇENT DOKTOR İSMAİL YENİLMEZ
Mode of Delivery	The mode of delivery of this course is face to face.
Prerequisites	There is no recommended optional programme component for this course.
Courses Recomended	There is no recommended optional programme component for this course.
Required or Recommended Resources	Essential Math for Data Science by Thomas Nield Released October 2022 Publisher(s): O'Reilly Media, Inc. ISBN: 9781098102869; Probability and Statistics for Data Science Math + R + Data By Norman Matloff ISBN 9781138393295 Published June 20, 2019 by Chapman and Hall/CRC; Introduction to Data Science Data Analysis and Prediction Algorithms with RBy Rafael A. Irizarry ISBN 9780367357986 Published November 7, 2019 by Chapman and Hall/CRC; Introduction to Data Science A Python Approach to Concepts, Techniques and Applications by Laura Igual, Santi Seguí 2017; Data Science A First Introduction By Tiffany Timbers, Trevor Campbell, Melissa Lee, ISBN 9780367524685 Chapman and Hall/CRC, 2022
Recommended Reading List	Practical Statistics for Data Scientists by Peter Bruce & Andrew Bruce, O'Reilly; Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas, O'Reilly.
Assessment methods and criteria	1 Midterm Exam, 1 Homework and Final Exam (Classic)
Work Placement	Not suitable for this course.
Sustainability Development Goals

Content

Weeks	Topics
Week - 1	Data Science
Week - 2	Statistics, Machine Learning, and Software for Data Science
Week - 3	Data Science Project and Data Science Experiment
Week - 4	The Need for Data Science: Organization Perspective
Week - 5	Data Scientist Skills
Week - 6	Data Science Team
Week - 7	Interdisciplinary Relation in Teams
Week - 8	Data Analysis
Week - 9	Descriptive Analysis
Week - 10	Inferential Analysis
Week - 11	Data Science in Real Life
Week - 12	Data Cleaning/Cleansing
Week - 13	Designing and Modelling
Week - 14	Scenario, Analysis, and Interpretation for Data

Learning Activities and Teaching Methods

Teaching Methods
Lecture
Discussion
Question & Answer
Drill - Practise
Problem Solving
Brain Storming
Report Preparation and/or Presentation
Proje Design/Management
Competences
Productive
True to core values
Rational
Questoning
Creative
Follow ethical and moral rules
Effective use of a foreign language
Eleştirel düşünebilme
Abstract analysis and synthesis
Problem solving
Applying theoretical knowledge into practice
Information Management
Organization and planning
Elementary computing skills
Decision making
Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
2.Midterm Exam	1	30
Homework	1	30
Final Exam	1	40
Toplam (%)		100