Technology
Onur Baltaci
Python and data-science tutorials anchor a practical catalogue spanning data cleaning, exploratory analysis, machine-learning projects, AI agents, RAG applications, and FastAPI deployment. Recent uploads pair longer step-by-step builds with occasional short explainers, while project-led titles make the intended outcome clear. It is a useful fit for beginners and early-career practitioners building portfolio projects or expanding applied AI skills. Viewers should treat reported model metrics as project-specific results rather than universal benchmarks.
Hidden GemTechnology
Editorially reviewed:
Viewer engagementModerate engagement
Based on 20 recent videos
Assessed 25 October 2026
Editorial focusAI & Machine Learning Creator Tools
Recent videos
Latest from the source
Insurance Cost Prediction with Machine Learning | Python Data Science Project (85% R² Score!)
07 October 2026
Exploratory Data Analysis with Python | Data Analyst Portfolio Project (E-Commerce Dataset)
30 September 2026
Data Cleaning with Python: Watch Me Clean a Retail Sales Dataset in 20 Minutes (5+ Pro Tips)
22 September 2026
Deep Dive
Onur Baltaci: Hands-On Paths Through Data Science
Main focus
Onur Baltaci teaches data-science skills across Python, SQL, data analysis, machine learning, and AI. Tutorials, courses, and practical projects create a broad study library for newcomers as well as learners extending existing technical knowledge.
Why it matters
Building a data-science learning path is easier when core tools and applied projects sit in one place. Onur Baltaci supports progression from foundational programming and querying skills toward analysis, machine learning, and AI-focused work.
Style
Hands-on projects sit alongside tutorials and course-style material, keeping the emphasis on usable technical skills. The range of topics allows viewers to move between focused tool lessons and broader data-science workflows.
Consistency
Across 403 videos, the library covers a wide span of data-science subjects and learning formats. Its beginner-to-intermediate scope makes it useful for skill-building, though technical depth may vary between topics.
- Python programming
- SQL queries and databases
- Data analysis
- Machine learning
- Artificial intelligence
- Hands-on data projects
Editorial note
Check current documentation for evolving AI frameworks and libraries, and adapt code examples to your own data, security requirements, and production environment.
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