Business Intelligence on Oracle Analytics Cloud (ED1)

1. Data Visualization: Provides intuitive and interactive data visualization capabilities to create compelling charts, graphs, and dashboards from various data sources.

2. Self-Service Analytics: Empowers business users to explore data, create ad-hoc reports, and gain insights without extensive technical expertise.

3. Data Preparation: Offers data preparation tools to cleanse, transform, and enrich data for analysis, ensuring data quality and consistency.

4. Advanced Analytics: Supports advanced analytics capabilities such as predictive analytics, machine learning, and spatial analytics to uncover hidden patterns and trends in the data.

5. Enterprise-Grade Security: Ensures data security and governance with robust access controls, encryption, and compliance features to protect sensitive information.

Before learning Business Intelligence on Oracle Analytics Cloud (ED1), it's beneficial to have the following skills:

1. Data Analysis: Proficiency in analyzing and interpreting data to derive meaningful insights and make informed decisions.

2. SQL and Data Modeling: Understanding of SQL queries and relational database concepts, as well as data modeling principles for structuring data effectively.

3. Business Intelligence Concepts: Familiarity with business intelligence concepts such as data visualization, dashboard design, and report generation.

4. Cloud Computing: Knowledge of cloud computing concepts and familiarity with cloud platforms, as Oracle Analytics Cloud is a cloud-based solution.

5. Data Integration: Understanding of data integration techniques and tools for extracting, transforming, and loading (ETL) data from various sources into the analytics platform.

By learning Business Intelligence on Oracle Analytics Cloud (ED1), you gain the following skills:

1. Data Visualization: Ability to create compelling visualizations and dashboards to convey insights effectively.

2. Data Analysis: Proficiency in analyzing and interpreting data to identify trends, patterns, and correlations.

3. Data Preparation: Skills to cleanse, transform, and enrich data for analysis, ensuring data quality and accuracy.

4. Self-Service Analytics: Capability to empower business users to explore data and create ad-hoc reports without extensive technical assistance.

5. Advanced Analytics: Knowledge of advanced analytics techniques such as predictive modeling and machine learning to uncover deeper insights from data.