Ders Planı

ISTANBUL AYDIN UNIVERSITY

CLASS 1

CLASS 4

Lesson plan /

Lesson Information

Course Credit
Course ECTS Credit
Teaching Language of Instruction	İngilizce
Level of Course	Bachelor's Degree, TYYÇ: Level 6, EQF-LLL: Level 6, QF-EHEA: First Cycle
Type of Course
Mode of Delivery	Face-to-face
Does the course require compulsory or optional work experience?
Course Coordinator
Instructor (s)
Course Assistant

Purpose and Content

The aim of the course	Master fundamental concepts of data science and visualization and skills to preprocess, analyse, construct, train and test models, integrate with application programs
Course Content	difference between knowledge and data, methods of preprocessing, model theory and evaluation algorithms such as SVM, nearest neighbor, k-means, random forest, ensemble methods, regression algorithms

Learning Outcomes

Adequate knowledge in mathematics, physical sciences and the engineering subjects of individual branches; the ability to implement theoretical and practical knowledge in the respective areas to modeling and solving engineering problems.
Ability to identify, define, formulate and solve complex engineering problems; ability to select and implement the appropriate analysis and modeling methods in this respect.
Ability to design and conduct experiments for analyzing engineering problems or research subjects specific to the discipline, to collect data, to analyze and interpret the results.
Ability to use, choose and develop modern techniques and means required for engineering applications; ability to use information technologies effectively.
An ability to design and conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or discipline-specific research topics.
Awareness of the necessity of lifelong learning; ability to reach information, follow the latest developments in science and technology; ability to ensure self-renewal perpetually.
Acting in line with ethical principles, sense of professional and ethical responsibility; knowledge on the standards of engineering applications.
Ability to work effectively in interdisciplinary and multi-disciplinary teams and individual works.
Information about the effects of engineering applications on health, environment and safety in terms of universal and social aspects and information about the problems of the era; awareness about the legal results of engineering solutions.
Ability to communicate efficiently both in oral and written ways in Turkish; knowledge of at least one foreign language; ability to effectively prepare reports and understand written reports, prepare design and production reports, effective presentation, giving and receiving clear and understandable instructions.
Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development.

Weekly Course Subjects

1	Introduction
2	Project and data understanding
3	Visualization
4	Dimensionality reduction methods
5	Data preprocessing
6	Principles of modelling
7	Techniques of modelling
8	Midterm
9	Association rules
10	Clustering
11	Bayesian classifiers
12	Regression
13	Decision tree
14	Deep learning, neural networks

Resources

1-Introduction to Data Science:Guide to Intelligent data science, by Michael R. Berthold, Internet resources

EUA Kurumsal Öz değerlendirme Programı İzleme Raporu