VERİ ANALİTİĞİ COURSE IDENTIFICATION AND APPLICATION INFORMATION

The student gains proficiency in the fundamental components of data science and analytics, and is able to practically apply methods related to statistical analysis, data mining, and machine learning.

The student is capable of analyzing both structured and unstructured data types and can effectively utilize analytical methods to derive meaningful insights from large datasets.

The student can utilize programming languages such as Python, R, and SQL in data analysis and modeling processes and is able to effectively manage data processing and automation tasks.

The student can express analytical findings clearly and effectively by using data visualization and result reporting techniques, contributing meaningfully to decision-making processes.

The student can analyze complex data-driven problems, develop appropriate solutions, and make creative, data-based decisions through the use of scientific research methods.

The student can analyze problems encountered in the field of data science and analytics, develop solutions by selecting appropriate data analysis techniques, and critically evaluate statistical, algorithmic, and artificial intelligence-based methods.

The student can effectively use programming languages such as Python, R, and SQL in data science and analytics applications; they are capable of developing practical solutions using data mining, machine learning, big data processing, data visualization, and modeling tools, and can work with real-world datasets.

The student is able to take responsibility in individual or team-based projects related to data science and analytics, independently plan and execute complex data-driven tasks, and play an active role in decision-making processes by developing analytical and creative solutions to encountered problems.

The student possesses the competence for continuous self-improvement with an awareness of lifelong learning by following current developments, technologies, and methods in the field of data science and analytics; they can rapidly acquire new knowledge and skills and apply them effectively.

The student can communicate their work in data science and analytics clearly and effectively through written, oral, and visual means; they are capable of working efficiently in multidisciplinary teams, engaging in effective communication, and developing collaborative solutions.

The student has a strong command of the concepts, methods, algorithms, and tools specific to the field of data science and analytics; they can carry out data collection, processing, analysis, and interpretation processes in accordance with ethical principles, and act with a sense of responsibility regarding data privacy and security.