IBM Data Science Professional Certificate (V3)
Issued by
IBM
The badge earner demonstrated career readiness in data science. They showed proficiency in Python app development with common data science libraries such as SciPy, Pandas, and NumPy while managing data with SQL and relational databases. They adeptly apply data and statistical analysis techniques by constructing machine learning models and present their findings using Jupyter notebooks. They can competently train, test, evaluate, and refine machine learning models using Generative AI techniques.
Additional DetailsSkills
- AI
- Artificial Intelligence
- Classification
- Clustering
- Data Analysis
- Database
- Data Science
- Data Visualization
- Db2
- Generative AI
- Jupyter Notebooks
- Machine Learning
- Matplotlib
- Methodology
- ML
- Model Selection
- Notebook
- NumPy
- Pandas
- PWID-B0969500
- Python
- Recommender Systems
- Regression
- RStudio
- SciKitLearn
- SciPy
- Seaborne
- SQL
- Studio
- Watson
- Zeppelin
Earning Criteria
Standards
The learning outcomes and skills acquired can be recognized as modules in subsequent educational courses, with a recommendation for recognition at EQF levels 5 and 6 for their designated ECTS credits. This certificate includes a workload of approximately 165 learning hours, providing a comprehensive learning experience.
Higher Education Institutions within the European Higher Education Area are obligated to recognize prior learning and non-formal learning experience, accepting up to a certain amount from non-university modules, provided there are no major differences in learning outcomes. Specific acceptance and applicability may vary by institution.
Endorsements
-
American Council on Education
This credential has been successfully evaluated by the American Council on Education for college credit. It is recommended for a total of 12 college credits. For more information about ACE Learning Evaluations, visit www.acenet.edu. -
American Council on Education
3 semester hours in Advanced Topics in Data Science in the upper-division undergraduate category -
American Council on Education
3 semester hours in Introduction to Machine Learning in the lower-division undergraduate category -
American Council on Education
3 semester hours in Introduction to Data Visualization in the lower-division undergraduate category -
American Council on Education
3 semester hours in Introduction to Data Science in the lower-division undergraduate category -
FIBAA
This credential has been successfully certified by the Foundation for International Business Administration Accreditation and is recommended for recognition of up to 6 ECTS credits. For more information about FIBAA, visit www.fibaa.org. -
FIBAA
6 semester hours