Oracle Analytics Cloud Corporate Training | Oracle Analytics Corporate Training Agenda

Oracle Analytics corporate training agenda

Oracle Analytics is a powerful platform that can be used to deliver enterprise-grade analytics to your organization. It enables users to perform self-service analytics, data discovery, data modeling and enterprise reporting.

Our Oracle Analytics corporate training gives attendees a complete overview of the Oracle Analytics platform. It is designed for anyone who is new to Oracle Analytics or who wants to understand the full capabilities of the platform. The course covers everything from the architecture and security, to data modeling, to building reports and dashboards, to data discovery and basic machine learning.

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Pythian has been an Oracle premier partner for decades

Pythian has cultivated a relationship with Oracle built on deep technical excellence and a commitment to customer success, establishing ourselves as a premier partner in the Oracle ecosystem. Our proven history working with the world's most complex Oracle environments, backed by a team of industry-leading Oracle ACEs, guarantees that your Oracle applications and data systems deliver maximum value and performance.

How is our Oracle Analytics corporate training delivered?

The course will be divided into lessons and labs. During lessons, the instructor will present a series of slides to explain the topic. During labs, delegates will be provided with exercises going over the functionality the instructor has just covered in the lessons. The delegates will be asked to complete the labs in the allocated time. The instructor will be available to answer any questions and provide assistance. Each delegate will be provided with a live Oracle Analytics instance to complete the labs.

Day 1: Metadata Repository

Introduction
- What is the Metadata Repository
- Overview of features and key processes
- Customer scenario
Metadata Repository basics
- Physical layer
- Business model and mapping layer
- Presentation layer
Advanced features
- Ragged and skip-level hierarchies
- Parent-child hierarchies
- Horizontal and vertical federation
Lab session
- Creating a basic repository
- Creating an advanced repository
- Server variables
- Modeling over 3NF sources
- Repository merging and patching

Day 2: Metadata Repository

Metadata Repository variables
- Repository vs. session variables
- Initialization blocks
- Row-level security
Modeling transactional (3NF) data
sources
- Horizontal and vertical federation
- Fragmentation
Software development lifecycle
- Online vs. offline development
- Merging and patching Metadata
  Repositories
- Multi-user development
- Version control and release
  management

Day 3: Analytics and dashboards

Introduction
- Where does Analytics (classic) fit in?
- Overview of features and key
  processes
Creating analyses
- Calculated columns, filters and
  formatting
- Title and table views
- Charts and graphs
Choosing the right visualization
- Data visualization concepts and
  principles
- Graphs and color usage
Creating dashboards
- Adding reports to dashboards
- Prompts and presentation variables
- Interactions between reports
  conditions
Additional views and features
- View and column selectors
- Master-detail linking of views
- Conditional formatting
Advanced analytics
- Time series calculations
- Filters based on another analysis
- Combining subject areas
Actionable intelligence
- Navigation actions
- Agents (scheduling reports)
Lab session
- Creating a basic analysis
- Choosing the right visualization
- Making analysis more interactive
- Advanced analytics
- Creating a basic dashboard
- Making dashboards more interactive
- Creating and scheduling an agent

Day 4: Data visualization

Introduction
- Where does data visualization (DV) fit in?
- Overview of features and key
  processes
Data acquisition
- Connections
- Datasets
Data preparation
- Data profiles and semantic recommendations
- Preparation script
Data exploration and visualization
- Prepare
- Visualize
- Narrate
Data flows
- Adding data sources
- Transformations
- Scheduling data flows
Machine learning
- Concepts and principals
- Advanced analytics functions
- Explain attributes and
  measures
- Machine learning models
Lab session
- Creating a dataset from a file
- Creating a dataset with multiple tables
- Creating a workbook
- Blending / presenting data in workbooks
- Creating a data flow
- Augmented analytics
- Creating a machine learning model

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