Major: Data Analysis Engineering (Veri Analizi Mühendisliği)
Program Overview
A technical, interdisciplinary program that trains professionals to collect, process, analyze, and interpret large datasets to drive decision-making in industries like finance, healthcare, marketing, and technology. Combines computer science, statistics, mathematics, and domain-specific knowledge to prepare graduates for careers as data analysts, data scientists, and business intelligence specialists in Turkey and globally.
Key Learning Objectives
- Master programming for data analysis (Python, R, SQL).
- Learn statistical methods and hypothesis testing.
- Gain expertise in data visualization and storytelling.
- Develop skills in machine learning and predictive modeling.
- Understand big data technologies and cloud computing.
- Explore domain-specific applications (e.g., finance, healthcare, marketing).
- Apply ethical and responsible data practices.
Core Courses
- Introduction to Data Analysis
- Overview of data types, sources, and the data analysis pipeline.
- Programming for Data Analysis
- Python (Pandas, NumPy, Scikit-learn), R, and SQL for data manipulation.
- Statistics for Data Science
- Descriptive/inferential statistics, probability, and regression analysis.
- Data Wrangling and Cleaning
- Handling missing data, outliers, and data transformation.
- Data Visualization
- Tools: Matplotlib, Seaborn, Tableau, Power BI, and Plotly.
- Machine Learning Fundamentals
- Supervised/unsupervised learning, model evaluation, and feature engineering.
- Big Data Technologies
- Hadoop, Spark, NoSQL databases (MongoDB, Cassandra), and cloud platforms (AWS, GCP).
- Database Management
- SQL, relational/non-relational databases, and data warehousing.
- Business Intelligence and Analytics
- Dashboards, KPIs, and decision support systems.
- Domain-Specific Applications
- Case studies in finance, healthcare, marketing, or social sciences.
- Ethics and Data Privacy
- GDPR, ethical data collection, and bias in AI.
- Practicum in Data Analysis
- Internships or capstone projects with industry partners.
- Capstone Project
- Solve a real-world data problem (e.g., predictive modeling, business analytics, or data-driven research).
Assessment Methods
- Coding assignments (Python, R, SQL)
- Data analysis and visualization projects
- Machine learning model development and evaluation
- Case studies on domain-specific applications
- Capstone project presentations
Tools & Resources
- Programming Languages: Python, R, SQL
- Libraries/Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Tableau, Power BI
- Big Data Tools: Hadoop, Spark, Kafka
- Cloud Platforms: AWS, Google Cloud, Azure
- Databases: MySQL, PostgreSQL, MongoDB
- Books:
- Python for Data Analysis by Wes McKinney
- R for Data Science by Hadley Wickham
- Data Science from Scratch by Joel Grus
Prerequisites
- Strong foundation in mathematics (algebra, calculus, statistics).
- Basic programming knowledge (helpful but not always required).
- Interest in problem-solving and data-driven decision-making.
Program Duration
- 4 years (bachelor’s) or 1–2 years (master’s), including internships or projects.
Certifications (Optional/Required)
- Google Data Analytics Professional Certificate
- Microsoft Certified: Data Analyst Associate
- AWS Certified Data Analytics
- Cloudera Certified Data Analyst
Career Paths
- Data Analyst (tech companies, finance, healthcare)
- Data Scientist (AI/ML startups, research labs)
- Business Intelligence Analyst (corporations, consulting firms)
- Data Engineer (big data pipelines, ETL processes)
- Machine Learning Engineer (AI/ML teams)
- Quantitative Analyst (finance, hedge funds)
- Marketing Analyst (digital marketing, e-commerce)
- Healthcare Data Analyst (hospitals, pharma)
- Research Analyst (think tanks, academia)
- Data Visualization Specialist (media, consulting)
Why This Major?
Turkey’s growing digital economy, data-driven industries, and demand for skilled analysts create high demand for data analysis engineers. This program provides hands-on training in programming, statistics, and machine learning, preparing graduates for high-impact careers in technology, finance, healthcare, and beyond. Ideal for those passionate about turning data into insights, solving complex problems, and driving innovation.
