BI Foundations with SQL, ETL and Data Warehousing Specialization
Prepare yourself for your initial position in business intelligence. Develop the essential competencies required to initiate a career in business intelligence (BI) within two months. There is no prerequisite for a degree or prior experience.
Description for BI Foundations with SQL, ETL and Data Warehousing Specialization
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by IBM
Duration: 2 months at 10 hours a week
Schedule: Flexible
Pricing for BI Foundations with SQL, ETL and Data Warehousing Specialization
Use Cases for BI Foundations with SQL, ETL and Data Warehousing Specialization
FAQs for BI Foundations with SQL, ETL and Data Warehousing Specialization
Reviews for BI Foundations with SQL, ETL and Data Warehousing Specialization
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for BI Foundations with SQL, ETL and Data Warehousing Specialization
A data analysis course covering practical skills, data visualization in Excel and BI tools, Python for data analysis, and portfolio development through hands-on projects.
Learn to write efficient queries, perform correlations, create visualizations, and leverage sub-searches and lookups to become a Search Expert.
Prepare for data analytics career. In less than three months, gain high-demand skills and experience. No prior experience required.
Zenith offers AI-powered business analysis that delivers actionable insights in just 20 minutes to optimize strategies and enhance decision-making.
Featured Tools
Learn to understand and implement Deep Neural Networks, Residual Nets, and Convolutional Neural Networks (CNNs) using Keras with TensorFlow 2.0, and evaluate their performance and generalization.
Learn to explain Azure Machine Learning Studio's no-code capabilities, fundamental machine learning principles, key development tasks, and common ML categories.
Machine learning mathematics. Find out about the mathematical prerequisites for applications in machine learning and data science.
Integrate AI-assisted code into a Python project, optimize and debug with AI, and create detailed documentation using ChatGPT.
The course's topics including the distinction between deep learning, machine learning, and artificial intelligence, the process of developing machine learning models, the difference between supervised and unsupervised learning, and the use of metrics for evaluating classification models.