Akademik Dersler - İçerik

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.