The badge earner is ready for a career in data science with demonstrated ability to solve for real-world problems. They can apply Data Science methodology - work with Jupyter notebooks - create Python apps - access relational databases using SQL & Python - use Python libraries to generate data visualizations - perform data analysis using Pandas - construct & evaluate Machine Learning (ML) models using Scikit-learn & SciPy and apply data science & ML techniques to real data sets.
Additional DetailsSkills
- AI
- Artificial Intelligence
- Bokeh
- Classification
- Clustering
- Data Analysis
- Database
- Data Science
- Data Visualization
- Db2
- Folium
- Foursquare
- IBM Cloud
- Jupyter
- Location
- Machine Learning
- Matplotlib
- Methodology
- ML
- Notebook
- NumPy
- Pandas
- PWID-B0380100
- Python
- Recommender Systems
- Regression
- RStudio
- Scikit-learn
- SciPy
- Seaborn
- SQL
- Studio
- Watson
- Zeppelin
Earning Criteria
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Receive the Data Science Professional Certificate from Coursera with a minimum passing grade of 70%.
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
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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 -
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 introduction to data science in the lower-division undergraduate category -
American Council on Education
3 semester hours in introduction to SQL programming in the lower-division undergraduate category -
American Council on Education
3 semester hours in introduction to Python programming in the lower-division undergraduate category -
American Council on Education
3 semester hours in advanced topics in data science in the upper-division undergraduate category