Akademik Dersler - İçerik

0 : Does not support 1 : Low-level support 2 : Mid-level support 3 : Top level support

Learning Outcomes	1	2	3	4	5	6	7	8	9	10	11	12
Will be able to explain the basic components of database systems, data management approaches, and different data storage architectures.	-	-	-	-	-	-	-	-	-	-	-	-
Students can compare conceptual, logical, and physical data modeling techniques and design an appropriate data model for a given problem.	-	-	-	-	-	-	-	-	-	-	-	-
Within the framework of relational data models, students can create ER diagrams, define primary-foreign key relationships, and interpret fundamental concepts of distributed data management.	-	-	-	-	-	-	-	-	-	-	-	-
The student can explain the stages of the data lifecycle and evaluate normalization and denormalization techniques in terms of data quality and performance.	-	-	-	-	-	-	-	-	-	-	-	-
You can identify the core components of the big data ecosystem (Hadoop, Spark, HDFS) and explain methods for ensuring data integrity in big data environments.	-	-	-	-	-	-	-	-	-	-	-	-
It can design performance-oriented databases; and apply indexing, table design, and query optimization principles.	-	-	-	-	-	-	-	-	-	-	-	-
By using data warehouse architecture, ETL processes, and OLAP cubes, we can perform multidimensional analyses and interpret decision support scenarios.	-	-	-	-	-	-	-	-	-	-	-	-
Using SQL, you can write complex SELECT, JOIN, GROUP BY, and HAVING queries, and develop queries for data analysis purposes.	-	-	-	-	-	-	-	-	-	-	-	-
They can compare NoSQL database types and explain the fundamental principles of big data analytics and data mining approaches.	-	-	-	-	-	-	-	-	-	-	-	-
By selecting appropriate visualization techniques, you can present data effectively and develop basic visualization applications with tools like Power BI, Tableau, or FineBI.	-	-	-	-	-	-	-	-	-	-	-	-
It can classify supervised and unsupervised machine learning methods and perform basic modeling applications on real datasets.	-	-	-	-	-	-	-	-	-	-	-	-
They can explain natural language processing processes and build basic models for text classification problems using word embedding methods.	-	-	-	-	-	-	-	-	-	-	-	-
Students will be able to identify the core components of search engine and recommendation systems, and compare content-based and collaborative filtering approaches.	-	-	-	-	-	-	-	-	-	-	-	-
They will be able to explain the working principles of large language models, apply effective prompt design techniques, and evaluate the reliability and limitations of model outputs.	-	-	-	-	-	-	-	-	-	-	-	-