IBM-Data-Science-Professional-Certificate and ibm-ai-engineering

These are ecosystem siblings within IBM's professional certification curriculum—one provides foundational data science skills while the other builds specialized expertise in machine learning and deep learning, allowing learners to progress sequentially through related but distinct technical domains.

IBM-Data-Science-Professional-Certificate
40
Emerging
ibm-ai-engineering
23
Experimental
Maintenance 0/25
Adoption 9/25
Maturity 8/25
Community 23/25
Maintenance 0/25
Adoption 2/25
Maturity 9/25
Community 12/25
Stars: 82
Forks: 88
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: —
Stars: 2
Forks: 1
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
No License Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About IBM-Data-Science-Professional-Certificate

DanielBarnes18/IBM-Data-Science-Professional-Certificate

IBM Data Science Professional Certificate

Comprehensive learning resource documenting the 10-course IBM certification curriculum spanning Python, SQL, statistical analysis, and machine learning. Includes hands-on Jupyter notebooks covering practical projects such as stock data extraction, predictive modeling with scikit-learn, data visualization dashboards, and SQL-based analytics on real datasets. Leverages the IBM Watson Studio and Cloud Pak ecosystem alongside pandas, NumPy, scikit-learn, and Matplotlib for end-to-end data science workflows.

About ibm-ai-engineering

david-palma/ibm-ai-engineering

This IBM Professional Certificate covers machine and deep learning with Python, using SciPy, Scikit-Learn, Keras, PyTorch, and TensorFlow to solve real-world problems through labs and projects.

Related comparisons

IBM-Data-Science-Professional-Certificate and data-science-notes

Scores updated daily from GitHub, PyPI, and npm data. How scores work