On successful Completion of the course you will get a sound understanding of:

• Define big data and the business drivers for advanced, big data analytics
• Describe why and how Data Science is different to traditional Business Intelligence
• Describe the roles and skills required in a big data analytics team
• Explain the phases and activities of the data analytics lifecycle and identify the main activities and deliverables
• Explore and make an initial analysis of the data, using R
• Select and execute appropriate advanced analytic methods for candidate selection, categorization, and predictive modeling
• Describe the challenges and tools for analyzing text and other unstructured data
• Describe the importance and benefits of advanced techniques such as in-database analytics and how extensions and other advanced functions add value

The Data Science and Big Data Analytics course educates students to a foundation level on big data and the state of the practice of analytics. The course provides an introduction to big data and a Data Analytics Lifecycle to address business challenges that leverage big data.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe the CRISP-DM process
• Understand Open Source Data Science tools and Platforms
• Learn to Install and Configure KNIME Platform
• Prepare data for predictive modelling
• Learn how to model, transform and prepare data frames and visualize them
• Learn about table transformation (merging data, table information, transpose, group by, pivoting etc
)
• Learn about row operations (eg
filter)
• Learn about column operations (filtering, spiting, adding, date information, missing values, adding binners, change data types, do basic math operations etc
)
• Visualize and explore data (column chart, line plot, pie chart, scatter plot, box plot)
• Create supervised and unsupervised machine learning models

This course is designed for anyone looking for a user-friendly, easily understandable tool for data analyses and machine learning tasks without necessarily having programming skills.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe the CRISP-DM process
• Understand Open Source Data Science tools and Platforms including Spark, R and Python
• Understand Commercial Data Science tools including Microsoft Azure ML and Oracle Advanced Analytics
• Learn to Install and Configure KNIME Platform
• Learn to Configure KNIME Platform for R Development
• Prepare data for predictive modelling
• Visualize and explore data
• Create supervised and unsupervised machine learning models
• Integrate KNIME with R and Python
• Develop Data Science solutions using R on KNIME Platform

This course is designed to help you understand the core principles of data science, and to learn techniques and technologies that you can use to explore and visualize data.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Explain what Predictive Analytics is
• Describe the main categories of machine learning Algorithms
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Install KNIME Analytics Platform
• Work with the KNIME Workbench
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Explain and Discuss Evaluation of Machine Learning Models
• Explain Regression, Cluster Analysis, and Association Analysis
• Define what the regression task is
• Explain the difference between regression and classification
• Construct models that learn from data using widely available open source tools
• Analyze big data problems using scalable machine learning algorithms on KNIME Analytics Platform

This course provides an overview of Predictive Analytics techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Define big data and the business drivers for advanced, big data analytics
• Describe why and how Data Science is different to traditional Business Intelligence
• Describe the roles and skills required in a big data analytics team
• Explain the phases and activities of the data analytics lifecycle and identify the main activities and deliverables
• Explore and make an initial analysis of the data, using KNIME Analytics Platform
• Select and execute appropriate advanced analytic methods for candidate selection, categorization, and predictive modeling
• Describe the challenges and tools for analyzing text and other unstructured data
• Describe the importance and benefits of advanced techniques such as in-database analytics and how extensions and other advanced functions add value

This course focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with KNIME Analytics Platform, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe key concepts of data science and machine learning
• Understand Open Source Data Science tools and Platforms including KNIME, R and Python
• Understand Open Source Machine learning Tools and Platforms
• Understand Commercial Data Science tools and Platforms
• Understand Data Science and Machine Learning Frameworks
• Learn to Install and Configure R and Python
• Prepare data for predictive modelling
• Visualize and explore data
• Create supervised and unsupervised machine learning models
• Develop Data Science solutions using R on Azure ML Platform
• Develop Machine Learning solutions using Microsoft Azure Notebooks

This course introduces you to the most commonly used Machine Learning algorithms used in Data Science applications

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Learn to Plan and Scope your Data Wrangling and Projects
• Understand the Data Wrangling Development Lifecycle
• Describe Data Integration techniques such as ETL,ELT, EAI and EII
• Understand the differences between Data Integration and Data Wrangling
• Understand Open source data integration tools and platforms
• Understand commercial data integration tools such as Microsoft SSIS, Oracle Data Integrator and Informatica
• Retrieve data from example database and big data management systems
• Learn how to model, transform and prepare data frames and visualize them
• Learn about table transformation (merging data, table information, transpose, group by, pivoting etc
)
• Describe the connections between data management operations and the big data processing patterns
• Describe the patterns needed to utilize them in large-scale analytical applications
• Identify when a big data problem needs data integration

This course is designed for those who are just getting started on their data wrangler journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Explain what Predictive Analytics is
• Describe the main categories of machine learning Algorithms
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Installing RapidMiner Studio
• Install RapidMiner Server Platform
• Work with the RapidMiner Studio Workbench
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Explain and Discuss Evaluation of Machine Learning Models
• Explain Regression, Cluster Analysis, and Association Analysis
• Define what the regression task is
• Explain the difference between regression and classification
• Construct models that learn from data using widely available open source tools
• Analyze big data problems using scalable machine learning algorithms on RapidMiner Analytics Platform

This course provides an overview of Predictive Analytics techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Understanding the Tableau Project Planning and Scoping Activities
• Capture Requirements for your Tableau Environment
• Understand the creation of Analytics Applications and Technical Design Specification documents
• Understand Foundational Elements of Data Visualization, and Apply Them Using Tableau
• Create Professional Dashboards and Master Tableau for Data Visualization and Analytics
• Build Professional Business Dashboards to Effectively Convey Patterns and Trends in Your Data
• Master Level of Detail (LOD) Expressions to Solve Complex Tasks in Data Visualization
• Master the Usage of Basic Calculations to Solve Common Tasks in Data Visualization
• Learn and Master Table Calculations to Apply Further Transformations to Your Visualization
• Navigate the Dashboard Workspace, and Customize Your Dashboards to Different Devices
• Master Tableau’s Mapping Functionality to Answer Spatial Questions
• Learn to Style and Format Maps with Mapbox, WMS, and Dual Axis capabilities
• Find, and Make of Use of Various Spatial Files in Tableau
• Master the Usage of Context Filters, Dimension Filters, Top N Filters, and Measure Filters
• Use Heat Maps to Find Hot Spots in Overlapping Data
• Add Interactivity to your Plots using Set actions and Parameter Control
• Learn about Different Join types to Combine your Data into a Single Rich Data Source
• Use Story Points to Tell Stories With Your Data
• Apply Pivots to your Data for Effective Data Processing
• Implement Unions to Combine Multiple Data Sources into One
• Learn About Theoretical Concepts Such as Data Types and Pre-attentive Processing

This course will teach you everything you need to know to create amazing data visualizations and dashboards with Tableau.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe the CRISP-DM process
• Understand Open Source Data Science tools and Platforms including Spark, R and Python
• Understand Commercial Data Science tools including Microsoft Azure ML and Oracle Advanced Analytics
• Learn to Install and Configure RapidMiner Platform
• Perform all common data preparations
• Build strong analytical predictive models
• Evaluate model quality with respect to different performance criteria
• Deploy analytical predictive models

The Data Science and Machine Learning with RapidMiner course focuses on data science and machine learning with RapidMiner Studio. You will explore a simplified business use case and build a strong analytical model while becoming familiar with the graphical interface and the main product features functionality.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe key concepts of Big Data
• Gain a high-level understanding of big data and Hadoop
• Understand Hadoop Components: MapReduce and HDFS
• Understand Hadoop Components: YARN and Apache Tez
• Understanding of big data infrastructure with its possibilities and limitations
• Connecting a desktop computer with a Hadoop cluster
• Exploration of large volumes of data
• Extracting and loading data
• Producing big data analyses with RapidMiner
• Knowledge of methods to efficiently process large volumes of data

After the course, participants will have an in-depth knowledge about the pros and cons of big data technologies and will know how large amounts of data can be processed using RapidMiner on Hadoop. In the course, the participants use their personal laptops, meaning they can take home the knowledge and example solutions from the course and use these as a basis for their own big data challenges.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe the CRISP-DM process
• Understand Open Source Data Science tools and Platforms
• Learn to Install and Configure RapidMiner Platform
• Prepare data for predictive modelling
• Learn how to model, transform and prepare data frames and visualize them
• Learn about table transformation (merging data, table information, transpose, group by, pivoting etc
• )
• Learn about row operations (eg. filter)
• Learn about column operations (filtering, spiting, adding, date information, missing values, adding binners, change data types, do basic math operations etc.)
• Visualize and explore data (column chart, line plot, pie chart, scatter plot, box plot)
• Create supervised and unsupervised machine learning models

This course is designed for anyone looking for a user-friendly, easily understandable tool for data analyses and machine learning tasks without necessarily having programming skills.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe Data Integration techniques such as ETL,ELT, EAI and EII
• Understand the differences between Data Integration and Data Wrangling
• Understand Open source data integration tools and platforms
• Understand commercial data integration tools such as Microsoft SSIS, Oracle Data Integrator and Informatica
• Retrieve data from example database and big data management systems
• Learn how to model, transform and prepare data frames and visualize them
• Learn about table transformation (merging data, table information, transpose, group by, pivoting etc
)
• Describe the connections between data management operations and the big data processing patterns
• Describe the patterns needed to utilize them in large-scale analytical applications
• Identify when a big data problem needs data integration

This course is designed for those who are just getting started on their data wrangler journey with RapidMiner. It starts with a detailed introduction of RapidMiner - from downloading it through to navigating the workbench.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Define big data and the business drivers for advanced, big data analytics
• Describe why and how Data Science is different to traditional Business Intelligence
• Describe the roles and skills required in a big data analytics team
• Explain the phases and activities of the data analytics lifecycle and identify the main activities and deliverables
• Explore and make an initial analysis of the data, using RapidMiner
• Select and execute appropriate advanced analytic methods for candidate selection, categorization, and predictive modeling
• Describe the challenges and tools for analyzing text and other unstructured data
• Describe the importance and benefits of advanced techniques such as in-database analytics and how extensions and other advanced functions add value

This course focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with RapidMiner, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Explain what Predictive Analytics is
• Describe the main categories of machine learning Algorithms
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Install RapidMiner
• Work with the RapidMiner Workbench
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Explain and Discuss Evaluation of Machine Learning Models
• Explain Regression, Cluster Analysis, and Association Analysis
• Define what the regression task is
• Explain the difference between regression and classification
• Construct models that learn from data using widely available open source tools
• Analyze big data problems using scalable machine learning algorithms on RapidMiner

This course provides an overview of Predictive Analytics techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Learn to create interactive reporting
• Create basic and advanced analyses and dashboards
• Build analyses and use views and graphs in analyses
• Explore data using Visual Analyzer
• Learn to create actionable intelligence and fixed-format published reporting
• Learn data exploration and visualisation techniques
• Learn to create self-service reporting
• Learn how to create augmented Analytics with machine learning

The Data Analysis and Visualization with Oracle Analytics Server course provides step-by-step instructions on how to create analyses, dashboards and perform a variety of Data Analysis and Visualization tasks using the Oracle Analytics Server platform.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Understanding the OAC and OAS Project Planning and Scoping Activities
• Capture Requirements for your Analytics Environment
• Understand the creation of Analytics Application and Technical Design Specification documents
• Understand Oracle Analytics Cloud (OAC) and (OAS) Architectures
• Understand the Oracle Analytics Reference Architecture
• Understand the data and repository layer
• Create and publish data models using Data Modeler
• Explore data using Visual Analyzer
• Build analyses and use views and graphs in analyses
• Administer objects in the catalog
• Troubleshoot issues with data loading, data modeling, analyses and dashboards
• Understanding the OAS Developer Client Tool
• Understanding Analytics Desktop Tool

The Oracle Analytics Server (On premise version of Analytics cloud - OAC) Bootcamp course covers everything you need to know in order to manage your Oracle Analytics Server (On premise version of Analytics cloud) and BI platform. It assumes no prior knowledge up-front.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Understanding the OAC and OAS Project Planning and Scoping Activities
• Capture Requirements for your Analytics Environment
• Understand the creation of Analytics Application and Technical Design Specification documents
• Upload data from external sources
• Explore data through data wrangling and visualizations
• Load data using Data Loader and SQL Developer
• Create and publish data models using Data Modeler
• Explore data using Visual Analyzer
• Build analyses and use views and graphs in analyses
• Administer objects in the catalog
• Troubleshoot issues with data loading, data modeling, analyses and dashboards
• Configure Oracle Analytics Server on Mobile
• Understanding the OAS Developer Client Tool
• Understanding Analytics Desktop Tool

The Business Intelligence on Oracle Analytics Server 5.5 course provides step-by-step instructions for creating analyses and dashboards using Oracle Analytics Server Platform. You’ll learn how to load data, model data, build and modify analyses and dashboards, while configuring Oracle Analytics Server on mobile.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe the key features of Oracle Machine Learning
• Create Projects and workspaces, and manage users in Oracle Machine Learning
• Crete and run Oracle Machine Learning Notebooks
• Develop SQL Scripts that can be used in Notebooks
• Create Notebooks for data analysis and Data Visualization
• Collaborate and share Notebooks with other Oracle Machine Learning users
• Schedule Jobs to run Notebooks
• Use Analytics Cloud to create data visualizations
• Extract data for analytics from Autonomous Data Warehouse and Autonomous Transaction Processing Cloud service instances
• Use Machine Learning feature in Oracle Analytics Cloud

This is a great starting point for Data Scientists, Developers, Architects, Business Analysts and anyone who wants to know how Oracle Machine Learning and Oracle Autonomous Cloud Platform can add value and transform their business.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Understand the features and Benefits of Oracle Analytics Cloud
• Learn to upload data from external sources
• Learn to blend and manage data
• Explore data on mobile devices
• Learn to manage users and roles
• Learn to troubleshoot issues in projects

In this Enhanced Visual Analysis with Data Visualization course, you will learn to use Data Visualization to easily create visualizations and explore your data through intuitive drag and drop gestures. Learn to quickly upload data from a variety of sources, like spreadsheets, CSV files, Fusion Applications, and database, to your system and model it in a few easy steps. You will learn to use the Oracle Analytics Cloud Home page to perform analyses and dashboards tasks.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Create and Configure the Oracle Analytics Server
• Configure the Prerequisites for Provisioning the OAS
• Understand Data Models and its components
• reate the Data Model in OAS
• Learn to use data modeler
• Learn to create source views
• Learn to create a Time Dimension
• Learn to define Hierarchies and Levels to Drill and Aggregate
• Learn to secure your data model

In this Data Modelling for BI Administrators course you will learn how to use Data Modeler to create data models from various source types, such as star and snowflakes.A data model is a design that presents business data for analysis in a manner that reflects the structure of the business. Data models enable analysts to structure queries in the same intuitive fashion as they ask business questions.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Create Reports
• Administration tasks
• Create reports from different data sources
• Create and Modify Data Models
• Create RTF Templates by Using Template Builder
• Create Layouts by Using the Layout Editor
• Schedule Reports
• Perform Translations

This Oracle Analytics Server: BI Publisher Reporting course will help you build a foundation of understanding how to best leverage BI Publisher in Oracle Analytics Cloud. You'll learn how to create layouts for reports, build data models, work with template builder, schedule reports, perform translations as well as perform some common administration tasks.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Define big data and the business drivers for advanced big data analytics
• Describe why and how Data Science is different to traditional Business Intelligence
• Describe the roles and skills required in a big data analytics team
• Explain the phases and activities of the data analytics lifecycle
• Describe what CRISP-DM is and identify the main activities and deliverables
• Describe and Understand Oracle Advanced Analytics Tools
• Describe the Oracle Data Miner Workflow GUI; a SQL Developer extension
• Explore and make an initial analysis of the data, using R
• Understand R Technologies in Oracle
• Describe Oracle R Enterprise Server
• Describe and Understand Data Science Algorithms supported by Oracle
• Select and execute appropriate advanced analytic methods for candidate selection, categorization, and predictive modeling
• Describe the challenges and tools for analyzing text and other unstructured data
• Describe the importance and benefits of advanced techniques such as in-database analytics and how extensions and other advanced functions add value
• Describe the SQL and R Support in Oracle Advanced Analytics
• Describe the In-Database Processing with Oracle Advanced Analytics
• Describe the Oracle R Enterprise—Integrating Open Source R with the Oracle Database
• Describe the Hadoop, Oracle Big Data Appliance and Big Data SQL

The Big Data Analytics with Oracle Advanced Analytics course educates students to a foundation level on big data and the state of the practice of analytics. The course provides an introduction to big data and a Data Analytics Lifecycle to address business challenges that leverage big data.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe Oracle Advanced Analytics (OAA) Option
• Describe the main categories of machine learning techniques
• Describe and Understand the AI and Machine Learning Process
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Install Oracle R Enterprise
• Start up R load ORE, and connect to Oracle Database
• Apply R Language Basics
• Use the ORE Transparency Layer
• Use ORE for embedded R execution
• Use ORE predictive analytics packages
• Interact directly with Oracle Database objects using ROracle

The Oracle R Enterprise Essentials course will teach you how to leverage the Oracle Database as a high performance computing platform from the powerful R statistical programming language and environment. Overcome the memory limitations of the open source client R engine. Prepare data, perform statistical analysis, and build predictive models on Big Data data sets that are generally impossible with open source R. Generate graphics and invoke R scripts from SQL for integration with the Oracle stack.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Explain basic Machine Learning concepts and describe the benefits of predictive analysis
• Understand primary Machine Learning tasks, and describe the key steps of a Machine Learning process
• Use the Oracle Data Miner to build,evaluate, and apply multiple Machine Learning models
• Use Oracle Machine Learning's predictions and insights to address many kinds of business problems, including: Predict individual behavior, Predict values, Find co-occurring events
• Learn how to deploy Machine Learning results for real-time access by end-users

In this course, students review the basic concepts of Machine Learning and learn how leverage the predictive analytical power of the Oracle Database Machine Learning option by using Oracle Data Miner 19c. The Oracle Data Miner GUI is an extension to Oracle SQL Developer 18.0 that enables data analysts to work directly with data inside the database.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Understand Predictive Analytics and Data Mining Concepts
• Explain data mining concepts and describe the benefits of predictive analysis
• Understand the Oracle Advanced Analytics (OAA) Option
• Understand Oracle R Enterprise (ORE)
• Understand Predictive Analytics and Machine Learning and their role within Information Warehouse Architecture
• Understand primary data mining tasks
• Describe the key steps of a Machine Learning process
• Deploy various Predictive Analytics Methods and Techniques
• Explore Predictive Analytics Case studies
• Use the Oracle Data Miner to build, evaluate, apply, and deploy multiple data mining models
• Use Oracle Data Mining's predictions and insights to address various kinds of business problems

The Predictive Analytics using Oracle Advanced Analytics course provides hands-on step-by-step instructions on how to apply a variety of Predictive Analytics and mining techniques using Oracle 12c and 18c.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Describe key concepts of data science and machine learning
• Understand Open Source Data Science tools and Platforms including Spark, R and Python
• Understand Open Source Machine learning Tools and Platforms
• Understand Commercial Data Science tools including Oracle SQL Developer and Microsoft Azure ML Studio
• Learn to Install and Configure R and Python
• Prepare data for predictive modelling
• Visualize and explore data
• Create supervised and unsupervised machine learning models
• Develop Data Science solutions using Oracle R (ORE) and Machine Learning Platform
• Develop Machine Learning solutions using Jupyter Notebooks
• Develop Data Science solutions using Oracle Data Miner

The Predictive Analytics using Oracle Machine Learning course provides step-by-step instructions on how to use a combination of data science and Machine Learning techniques to address your business challenges. You will perform basic to advanced Data Science and Machine Learning tasks using open source tools and Oracle SQL Developer for activities based on real-life work scenarios.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Understand Predictive Analytics and Machine Learning Concepts
• Explain data mining concepts and describe the benefits of predictive analysis
• Understand the Oracle Advanced Analytics (OAA) Option
• Understand Oracle Machine Learning for R (OML4R)
• Start up R, load OML4R, and connect to Oracle Database
• Apply R Language Basics
• Use the OML4R Transparency Layer
• Use OML4R for embedded R execution
• Use OML4R predictive analytics packages
• Interact directly with Oracle Database objects using ROracle
• Create Projects and Workspaces in Oracle Machine Learning
• Create and Running Notebooks in Oracle Machine Learning
• Collaborate in Oracle Machine Learning
• Creating a SQL Script in Oracle Machine Learning
• Running SQL Statements in Oracle Machine Learning

The Predictive Analytics with Oracle Machine Learning for R (OML4R) course will teach you how to leverage the Oracle Database as a high performance computing platform from the powerful R statistical programming language and environment. Overcome the memory limitations of the open source client R engine. Prepare data, perform statistical analysis, and build predictive models on Big Data data sets that are generally impossible with open source R. Generate graphics and invoke R scripts from SQL for integration with the Oracle stack.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will get a sound understanding of:

• Understand Predictive Analytics and Machine Learning Concepts
• Explain data mining concepts and describe the benefits of predictive analysis
• Understand the Oracle Advanced Analytics (OAA) Option
• Understand Oracle R Enterprise (ORE)
• Understand Oracle Machine Learning for R (OML4R)
• Understand Predictive Analytics and R and their role within Information Warehouse Architecture
• Understand primary data mining and Machine Learning tasks
• Describe the key steps of a data mining and Machine Learning process
• Understand primary data mining tasks, and describe the key steps of a data mining process
• Use the Oracle Data Miner to build, evaluate, apply, and deploy multiple data mining models
• Deploy various Predictive Analytics Methods and Techniques
• Explore Predictive Analytics Case studies
• Use the Oracle R Enterprise to build, evaluate, apply, and deploy predictive analytics models
• Use Oracle R Enterprise's predictions and insights to address various kinds of business problems

This Oracle Database 19c: Advanced Analytics Fundamentals course will teach you how to leverage the Oracle Database Advanced Analytics option as a high performance computing platform for Predictive Analytics and Data Science projects.

Product code: WLN-IDM-PBF<

On successful Completion of the course you will be able to accomplish the following:

• Describe key concepts of Data Science
• Understand Open Source Data Science tools including R and Python
• Understand Open Source Data Science Tools and Platforms
• Understand Commercial Data Science tools including Microsoft Azure Machine Learning
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Data Science solutions using R
• Develop R based Data Science solutions using Microsoft Azure ML
• Develop Data Science solutions using Microsoft Azure ML Studio
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FND-DSCR-CBT

On successful Completion of the course you will be able to accomplish the following:

• Describe key concepts of Data Science
• Understand Open Source Data Science tools including R and Python
• Understand Open Source Data Science Tools and Platforms
• Understand Commercial Data Science tools including Microsoft Azure Machine Learning
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Data Science solutions using Python
• Develop Python based Data Science solutions using Microsoft Azure ML
• Develop Data Science solutions using Microsoft Azure ML Studio
• Create supervised and unsupervised Data Science models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDN-DSPYTH-CBT

On successful Completion of the Foundations of Data Science with Microsoft Azure ML Self-Study CBT course you will be able to:

• Describe key concepts of Data Science
• Understand Open Source Data Science tools including R and Python
• Understand Open Source Data Science Tools and Platforms
• Understand Commercial Data Science tools including Microsoft Azure Machine Learning
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Data Science solutions using Python
• Develop R based Data Science solutions using Microsoft Azure ML
• Develop Machine learning solutions using Microsoft Azure ML Studio
• Create supervised and unsupervised Data Science models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDN-DSMSAZ-CBT

On successful Completion of the Foundations of Machine Learning with Azure ML course you will be able to:

• Describe key concepts of machine learning
• Understand Open Source Machine learning tools including Python
• Understand Open Source Machine learning Tools and Platforms
• Understand Commercial Machine learning tools including Microsoft Azure Machine Learning
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Machine learning solutions using Python
• Develop R and Python Machine learning solutions using Microsoft Azure ML
• Develop Machine learning solutions using Microsoft AzureML Studio
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDN-MLMSAZ-CBT

On successful Completion of the Foundations of Machine Learning with Python course you will be able to:

• Describe key concepts of machine learning
• Understand Open Source Machine learning tools including Python
• Understand Open Source Machine learning Tools and Platforms
• Understand Commercial Machine learning tools including Microsoft Azure Machine Learning
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Machine learning solutions using Python
• Develop Python Machine learning solutions using Microsoft Azure ML
• Develop Machine learning solutions using Microsoft Azure ML Studio
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDN-MLPYTH-CBT

On successful Completion of the Foundations of Machine Learning with R course you will be able to:

• Describe key concepts of machine learning
• Understand Open Source Machine learning tools including R
• Understand Open Source Machine learning Tools and Platforms
• Understand Commercial Machine learning tools including Microsoft Azure Machine Learning
• Develop Machine learning solutions using R
• Develop R Machine learning solutions using Microsoft Azure ML
• Develop Machine learning solutions using Microsoft Azure ML Studio
• Prepare data for predictive modelling
• Visualize and explore data
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDN-MLPYTH-CBT

On successful Completion of the Foundations of Data Science and Machine Learning course you will be able to:

• Describe key concepts of data science and machine learning
• Describe key concepts of machine learning
• Understand Open Source Machine learning tools including R and Python
• Understand Open Source Machine learning Platforms
• Understand Commercial Machine learning tools including Microsoft Azure Machine Learning
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Machine learning solutions using R and Python
• Develop R Machine learning solutions using Microsoft AzureML
• Develop Machine learning solutions using Microsoft AzureML Studio
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDN-DSML-CBT

On successful Completion of the Fundamentals of Data Science and Machine Learning with Azure ML course you will be able to:

• Describe key concepts of data science and machine learning
• Understand Open Source Machine learning tools including KNIME, R and Python
• Understand Open Source Machine learning Platforms
• Understand Commercial Machine learning tools including Oracle Advanced Analytics and Microsoft Azure Machine Learning
• Develop Machine learning solutions using R and Python
• Develop R Machine learning solutions using Microsoft Azure ML
• Develop Machine learning solutions using Microsoft Azure ML Studio
• Prepare data for predictive modelling
• Visualize and explore data
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDM-DSML-CBT

On successful Completion of the Machine Learning Fundamentals course you will be able to:

• Describe key concepts of machine learning
• Understand Open Source Machine learning tools including R, KNIME and Python
• Understand Open Source Machine learning Platforms
• Understand Commercial Machine learning tools including IBM SPSS, Oracle Analytics, Microsoft Azure ML
• Visualize and explore data
• Create supervised and unsupervised machine learning models
• Develop Machine learning solutions using R and Python
• Develop R Machine learning solutions using Microsoft Azure ML
• Develop Machine learning solutions using Microsoft Azure ML Studio
• Prepare data for predictive modelling

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: FDM-ML-CBT

On successful Completion of the Data Science and Machine Learning Bootcamp course you will be able to:

• Describe key concepts of data science and machine learning
• Describe key concepts of machine learning
• Understand Open Source Data Science tools and Platforms including Spark, R and Python
• Understand Open Source Machine learning Tools and Platforms
• Understand Commercial Data Science tools including Microsoft Azure ML Studio and Oracle Advanced Analytics
• Learn to Install and Configure R and Python
• Learn to Configure Microsoft Azure ML for R and Python Development
• Prepare data for predictive modelling
• Visualize and explore data
• Create supervised and unsupervised machine learning models
• Develop Data Science solutions using R on Azure ML Platform
• Develop Machine learning solutions using R and Python
• Develop Machine learning solutions Microsoft Azure Notebooks
• Develop Data Science solutions using Microsoft Azure ML Studio

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: DSML-BCAMP-CBT

On successful Completion of the Data Science and Machine Learning with Azure ML course you will be able to:

• Describe key concepts of data science and machine learning
• Understand Open Source Machine learning tools including R and Python
• Understand Open Source Machine learning Platforms
• Understand Commercial Machine learning tools including Microsoft Azure Machine Learning
• Set Up a R Environment for Development
• Set Up a Python Environment for Development
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Data Science solutions using R
• Develop R solutions using Microsoft Azure ML
• Develop Data Science solutions using Microsoft Azure ML Studio
• Develop Machine learning solutions using Microsoft Azure ML Studio
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: DSML-AZU-CBT

On successful Completion of the Career Enabler R Programming Disc-3mm -May2013 course you will be able to:

• Learn to Install RStudio and work on R interface
• Learn the basics of R programming including objects, classes, vectors, attributes etc.
• Write functions including generic functions using various methods and loops
• Install various packages and work effectively in the R environment
• Select and modify values as required
• Learn to use Vectorized Coding and Use Cases
• Cover the concepts of R Notation, S3 System and Closures
• Become proficient in writing a fundamental program and perform analytics with R

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: DSML-RPRG-CBT

On successful Completion of the Career Enabler: Fundamentals of R for Data Science course you will be able to:

• Understand Data Science
• Understand Data Science Tools and Technology
• Understand R
• Data Modeling in R
• Visualize and explore data
• Data Exploration in R
• Data Visualisation in R
• Profiling and Analyzing Datasets

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: DS-RDS-CBT

On successful Completion of the Predictive Analytics with Spark course you will be able to:

• Using Spark to work with data
• Preprocessing data for machine learning in Spark
• Building machine learning models in Spark
• Using R at scale with R Server on Spark

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: WLN-PA-SPARK

On successful Completion of the Career Enabler: R Programming and Predictive Modelling course you will be able to:

• Basics of R Programming
• Naive Bayes
• k-nearest neighbor algorithm
• Support vector machines
• Random forests
• Sentiment Analysis using R

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: DSML-RPRG-CBT

On successful Completion of the AI & Machine Learning course you will be able to:

• General Concepts on Artificial Intelligence
• General Concepts on Machine Learning
• Linear Regression
• Logistic Regression
• Association Rules- Market Basket Analysis
• Recommendation system
• Item Based collaborative
• User Based Collaborative

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSAIML-CBT

On successful Completion of the Career Enabler: Mastering Apache Hadoop course you will be able to:

• Comprehend key Hadoop distributors
• Comprehend Hadoop Architecture
• Comprehend HDFS
• Comprehend MapReduce
• Comprehend Backup and Recovery
• Comprehend Monitoring the Cluster
• Comprehend Hadoop Resource Management
• Comprehend Cluster Troubleshooting
• Comprehend Installation, Configuration and Implementation
• Comprehend Hadoop debugging, development, and execution of workflows and algorithms
• leverage Hive, Oozie, Pig, Flume, Sqoop, and other Hadoop ecosystem projects
• Create custom components such as InputFormats and WritableComparables to administer complex data types
• Write and execute joins to link data sets in MapReduce

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-HADO-CBT

On successful Completion of the Foundations of Mathematics & Statistics course you will be able to:

• Mean
• Mode
• Median
• Standard deviation
• Probability
• Combination
• R Studio and R Installation
• R for Statistics and mathematics (Basic Mathematics Operations)

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-FND-MATS-CBT

On successful Completion of the Fundamentals: Hadoop YARN & Ambari course you will be able to:

• Hadoop fundamentals
• Big Data Approach
• RDBMS Vs. Hadoop
• Introduction to Hadoop
• Hadoop Architecture
• HDFS, YARN, Hadoop setup
• Cloudera Manager
• Ambari
• Job scheduling and monitoring

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-HYAMB-CBT

On successful Completion of the Fundamentals: Sqoop, Flume & Kafka course you will be able to:

• Ingesting Data using Sqoop
• Sqoop properties and commands
• Integrating Sqoop with RDBMS
• Sqoop features and example scenarios
• Ingesting Data using Flume
• Flume properties and setup
• Flume and streaming
• Flume Vs. Kafka

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SQFK-CBT

On successful Completion of the Fundamentals: Pig, Hive & Impala course you will be able to:

• Introduction to Pig
• Pig programming and commands
• Pig interactive shell
• Analyzing complex data using Pig
• Using Pig functions
• Introduction to Hive
• Hive Metastore
• Writing queries using Hive
• Joins and grouping
• Impala architecture
• Impala Vs. Hive
• Queries using Impala

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-PHI-CBT

On successful Completion of the Career Enabler Spark Machine Learning course you will be able to:

• Spark Basics
• Spark MlLib Library
• Install and configure intelliJ for Spark development
• Logistic Regression
• Linear Regression
• ALS
• KMeans

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-SPAML-CBT

On successful Completion of the Career Enabler: Foundations of Data Science and Machine Learning course you will be able to:

• Describe key concepts of data science and machine learning
• Describe key concepts of machine learning
• Understand Open Source Machine learning tools including R and Python
• Understand Open Source Machine learning Platforms
• Understand Commercial Machine learning tools including Microsoft Azure Machine Learning
• Prepare data for predictive modelling
• Visualize and explore data
• Develop Machine learning solutions using R and Python
• Develop R Machine learning solutions using Microsoft AzureML
• Develop Machine learning solutions using Microsoft AzureML Studio
• Create supervised and unsupervised machine learning models

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-FND-DSML-CBT

On successful Completion of the Introduction to Data Science, AI & Machine Learning course you will be able to:

• Translate business questions into Machine Learning problems to understand what your data is telling you
• Explore and analyze data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business
• Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviors as well as other business critical events
• Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
• Discover hidden customer behaviors from Association Rules and Build Recommendation Engines based on behavioral patterns
• Use biologically-inspired Neural Networks to learn from observational data as humans do
• Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSAIML-RPRG-CBT

On successful Completion of the Career Enabler: Introduction to Big Data course you will be able to:

• Store, manage, and analyze unstructured data
• Select the correct big data stores for disparate data sets
• Process large data sets using Hadoop to extract value
• Query large data sets in near real time with Pig and Hive
• Plan and implement a big data strategy for your organization

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SQFK-CBT

On successful Completion of the Career Enabler: Building Enterprise Solutions with MongoDB course you will be able to:

• Develop highly scalable and cost-efficient applications with MongoDB
• Build efficient MongoDB data models for enterprise-scale applications
• Enhance performance with indexing and sharding
• Manage consistency with write concern and read preference
• Process data and compute results with aggregation pipelines and MapReduce

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SQFK-CBT

On successful Completion of the Career Enabler: Business Intelligence: Preparing for a BI Initiative course you will be able to:

• Apply the BI Meta Model (BIMM) to turn desired outcomes into actions
• Choose between a strategic and a tactical application of BI
• Map operational data to a data warehouse
• Design a BI solution for a business problem

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BII-CBT

On successful Completion of the Career Enabler: Introduction to Modelling for Business Analysis course you will be able to:

• Perform a functional decomposition of your organisation
• Diagram and document business processes as use cases
• Capture workflows in Business Process Model and Notation (BPMN)
• Apply UML diagrams to document your business objects and their states
• Use a CASE tool to refine business analysis models and documentation

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-MFBA-CBT

On successful Completion of the Career Enabler: Data Science and Machine Learning with Spark ML course you will be able to:

• Data Science and Machine Learning Overview
• Understand Data Science and Big Data Processing
• Understand Spark
• Making a Case for Spark
• Understand Major components of Spark programming
• Understand Major components of Spark programming
• Understand Major components of Spark programming
• Describe Spark MLlib and Data Types
• Describe Spark ML on Azure
• Describe Azure HDInsight Spark Clusters
• Spark MLlib Basic Statistics
• Understand Clustering on Spark
• Understand Regression on Spark
• Applying Regression and Classification on Spark

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SQFK-CBT

On successful Completion of the Career Enabler: Machine Learning with Big Data course you will be able to:

• Explain what machine learning is
• Describe the main categories of machine learning techniques
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Explain and Discuss Evaluation of Machine Learning Models
• Explain Regression, Cluster Analysis, and Association Analysis
• Define what the regression task is
• Explain the difference between regression and classification
• Construct models that learn from data using widely available open source tools
• Analyze big data problems using scalable machine learning algorithms on Spark

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SQFK-CBT

On successful Completion of the Data Science for Oracle Developers course you will be able to:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Describe and Understand Oracle Advanced Analytics Tools
• Describe Oracle Data Miner
• Understand R Technologies in Oracle
• Describe Oracle R Enterprise Server
• Describe and Understand Data Science Algorithms supported by Oracle
• Describe and Understand AI and Machine Learning Techniques supported by Oracle
• Install and Configure Oracle Advanced Analytics
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Understand and Perform Clustering Methods
• Understand and apply Association Rules
• Understand and apply Regression Analysis
• Understand and apply Classification Methods
• Understand and apply Neural Networks
• Understand and apply Solutions Deployment

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSORA-CBT

On successful Completion of the Data Science and Machine Learning for Oracle Developers course you will be able to:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Describe and Understand Oracle Advanced Analytics Tools
• Describe Oracle Data Miner
• Understand R Technologies in Oracle
• Describe Oracle R Enterprise Server
• Describe and Understand Data Science Algorithms supported by Oracle
• Describe and Understand AI and Machine Learning Techniques supported by Oracle
• Install and Configure Oracle Advanced Analytics
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Understand and Perform Clustering Methods
• Understand and apply Association Rules
• Understand and apply Regression Analysis
• Understand and apply Classification Methods
• Understand and apply Neural Networks
• Understand and apply Solutions Deployment
• Identify the typical workflow for using Autonomous Database Cloud Services
• Provision an Oracle Autonomous Data Warehouse Cloud Service instance
• Use Oracle Machine Learning with Autonomous Data Warehouse instances
• Connect to the cloud service instance using SQL Developer

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSORA-CBT

On successful Completion of the AI and Machine Learning for Oracle Developers course you will be able to:

• Describe and Understand AI and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the AI and Machine Learning Process
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Describe Oracle Database architecture
• Explain Oracle Database Cloud Service (DBCS) architecture and features
• Understand Oracle Autonomous Database Cloud services
• Describe and Understand Oracle Advanced Analytics Tools
• Describe Oracle Data Miner
• Understand R Technologies in Oracle
• Describe Oracle R Enterprise Server
• Identify the typical workflow for using Autonomous Database Cloud Services
• Provision an Oracle Autonomous Data Warehouse Cloud Service instance
• Provision an Oracle Autonomous Transaction Processing Cloud Service instance
• Use Oracle Machine Learning with Autonomous Data Warehouse instances
• Use Oracle Machine Learning with Transaction Processing instances
• Connect to the cloud service instance using SQL Developer
• Describe Data Exploration and Preparation
• Describe and Understand AI and Machine Learning Algorithms supported by Oracle
• Describe classification and how classification can be supervised or unsupervised
• Understand and Perform Clustering Methods
• Understand and apply Association Rules and Regression Analysis

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSORA-CBT

On successful Completion of the Data Science for SQL Server Developers course you will be able to:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Describe and Understand Microsoft Analytics Tools
• Describe Oracle Data Miner
• Understand R Technologies in Microsoft
• Describe Microsoft R Enterprise Server
• Describe and Understand Data Science Algorithms supported by Microsoft
• Describe and Understand AI and Machine Learning Techniques supported by Microsoft
• Install and Configure Microsoft Advanced Analytics
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Understand and Perform Clustering Methods
• Understand and apply Association Rules
• Understand and apply Regression Analysis
• Understand and apply Classification Methods
• Understand and apply Neural Networks
• Understand and apply Solutions Deployment

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSORA-CBT

On successful Completion of the Machine Learning for SQL Server Developers course you will be able to:

• Explain Artificial Intelligence and machine learning
• Describe the open source and commercial Tools landscape: KNIME, R vs. Microsoft R, Python
• Understand Algorithms and Algorithm classes in R, Python, ML Server, Azure ML
• Describe the main categories of machine learning techniques
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Explain and Discuss Evaluation of Machine Learning Models
• Explain Regression, Cluster Analysis, and Association Analysis
• Define what the regression task is
• Explain the difference between regression and classification
• Construct models that learn from data using widely available open source tools
• Analyze big data problems using scalable machine learning algorithms on open source R, Microsoft R, Azure ML

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSORA-CBT

On successful Completion of the Data Science and Machine Learning for SQL Server Developers course you will be able to:

• Describe and Understand Data Science and Machine Learning
• Describe the main categories of machine learning techniques
• Describe and Understand the Data Science Process
• Identify the steps in the machine learning process
• Describe what CRISP-DM is
• Describe the open source and commercial Tools landscape: KNIME, R vs. Microsoft R, Python
• Describe and Understand Microsoft Analytics Tools
• Understand R Technologies in Microsoft
• Describe Microsoft R Enterprise Server
• Describe and Understand Data Science Algorithms supported by Microsoft
• Describe and Understand AI and Machine Learning Techniques supported by Microsoft
• Install and Configure Microsoft Advanced Analytics
• Describe Data Exploration and Preparation
• Describe classification and how classification can be supervised or unsupervised
• Understand and Perform Clustering Methods
• Understand and apply Association Rules
• Understand and apply Regression Analysis
• Understand and apply Classification Methods
• Understand and apply Neural Networks
• Understand and apply Solutions Deployment

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-DSORA-CBT

On successful Completion of the Career Enabler: Foundations of Apache Spark course you will be able to:

• Gain a high-level understanding of Apache Spark
• Understand Resilient Distributed Datasets (RDDs) in Apache Spark
• Understand notebooks on Apache Spark
• Understand Spark ML
• Understand Spark SQL
• Understand Hive in Apache Spark
• Understand R on Apache Spark
• Understand Spark Streaming
• Understand machine learning with Spark MLlib

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SPK-CBT

On successful Completion of the Career Enabler: Microsoft Big Data Analytics: Hadoop and Spark on HDInsight course you will be able to:

• Gain a high-level understanding of big data and Hadoop on HDInsight
• Understand Hadoop Components: MapReduce and HDFS
• Understand Hadoop Components: YARN and Apache Tez
• Understand HDInsight
• Understand HCatalog
• Understand development with Hive
• Understand development with Pig
• Understand development with HBase
• Understand Apache Storm on HDInsight
• Understand HDInsight clusters, cluster type, and configuration
• Understand extensibility and customizing HDInsight clusters using script actions
• Understand cluster security and operations
• Understand monitoring and managing Hadoop with Ambari web UI
• Understand Hadoop Clusters in HDInsight
• Gain a high-level understanding of Apache Spark
• Understand Resilient Distributed Datasets (RDDs) in Apache Spark
• Understand notebooks on Apache Spark
• Understand Spark with Azure HDInsight
• Understand Spark ML
• Understand Spark SQL
• Understand Hive and Power BI in Apache Spark
• Understand Spark Streaming
• Understand machine learning with Spark MLlib
• Understand R Server on HDInsight with Apache Spark

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SQFK-CBT

On successful Completion of the Career Enabler: Microsoft Big Data Analytics: Hadoop on HDInsight course you will be able to:

• Gain a high-level understanding of big data and Hadoop on HDInsight
• Understand Hadoop Components: MapReduce and HDFS
• Understand Hadoop Components: YARN and Apache Tez
• Understand HDInsight
• Understand HCatalog
• Understand development with Hive
• Understand development with Pig
• Understand development with HBase
• Understand Apache Storm on HDInsight
• Understand HDInsight clusters, cluster type, and configuration
• Understand extensibility and customizing HDInsight clusters using script actions
• Understand cluster security and operations
• Understand monitoring and managing Hadoop with Ambari web UI
• Understand Hadoop Clusters in HDInsight

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SQFK-CBT

On successful Completion of the Career Enabler: Microsoft Big Data Analytics: Spark on HDInsight course you will be able to:

• Gain a high-level understanding of Apache Spark
• Understand Resilient Distributed Datasets (RDDs) in Apache Spark
• Understand notebooks on Apache Spark
• Understand Spark with Azure HDInsight
• Understand Spark ML
• Understand Spark SQL
• Understand Hive and Power BI in Apache Spark
• Understand Spark Streaming
• Understand machine learning with Spark MLlib
• Understand R Server on HDInsight with Apache Spark

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-SPK-CBT

On successful Completion of the Career Enabler: Microsoft Big Data Analytics: Hadoop on Azure course you will be able to:

• Microsoft Big Data Fundamentals
• How to implement Hadoop on HDInsight
• Hive and Tez: Querying Big Data Essentials
• Pig, Sqoop, Oozie, and Mahout Essentials
• HBase Essentials
• Storm Essentials

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-HADAZU-CBT

On successful Completion of the Career Enabler: Foundations of Microsoft AI, Machine Learning and Big Data Platform course you will be able to:

• Gain a high-level understanding of SQL Server 2016/2017
• Understand the mission-critical and hybrid capabilities of SQL Server
• Gain a high-level understanding of Azure Data Services
• Gain a high-level understanding of Modern Data Warehouse
• Gain a high-level understanding of SQL Server BI
• Gain a high-level understanding of SQL Server Enterprise Information Management
• Gain a high-level understanding of Power BI
• Gain a high-level understanding of Azure Machine Learning
• Gain a high-level understanding of Azure Data Factory
• Gain a high-level understanding of SQL Server R Services
• Gain a high-level understanding of Hadoop on Azure
• Gain a high-level understanding of Azure Data Lake
• Gain a high-level understanding of Apache Spark

Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.

Product code: CEN-BIG-MSPLT-CBT

On successful Completion of the Fundamentals of Microsoft Azure Data Warehousing course you will be able to:

• Understand Data Warehousing Concepts
• Understand key SQL Data Warehouse concepts
• Understand the Data Warehouse and ETL Development Lifecycle
• Understand SQL Data Warehouse topology and architecture
• Understand Azure Data Warehouse database design
• Understand ETL (data access, data export) in SQL Data Warehouse
• Learn how to Extract, Transform, and Load (ETL) Data in Azure SQL Data Warehouse
• Understand data movement and distribution theory in SQL Data Warehouse
• Understand big data and SQL Server integration
• Understanding Azure Data Warehouse to BI integration, including with Power BI

Certification: This course will assist in preparation for the Microsoft Business Intelligence Certification exams.

Product code: WLN-MSFT-AZUDW

On successful Completion of the Fundamentals of SQL Server 2016 Platform course you will be able to:

• Gain a high-level understanding of SQL Server
• Understand the new features in SQL Server 2016
• Understand SQL Server In-Memory Optimization (OLTP)
• Understand SQL Server In-Memory Data Warehouse capabilities
• Understand SQL Server 2016 high-availability features
• Understand SQL Server 2016 SQL Server security features
• Understand SQL Server 2016 Query Store and Live Query Statistics
• Understand SQL Server 2016 Temporal Tables
• Understand the integration of SQL Server 2016 with big data sources with PolyBase
• Understand SQL Server 2016 Stretch Database
• Gain a high-level understanding of SQL Server R Services Analytics
• Understand SQL Server 2016 R Services integration

Certification: This course will assist in preparation for the Fundamentals of SQL Server 2016 Platform Certification exams.

Product code: WLN-MSFT-BIDW

On successful Completion of the Fundamentals of SQL Server 2017 Platform course you will be able to:

• Gain a high-level understanding of SQL Server
• Understand the new features in SQL Server 2017
• Understand SQL Server In-Memory Optimization (OLTP)
• Understand SQL Server In-Memory Data Warehouse capabilities
• Understand SQL Server 2017 high-availability features
• Understand SQL Server 2017 SQL Server security features
• Understand SQL Server 2017 Query Store and Live Query Statistics
• Understand SQL Server 2017 Temporal Tables
• Understand the integration of SQL Server 2017 with big data sources with PolyBase
• Understand SQL Server 2017 Stretch Database
• Gain a high-level understanding of SQL Server R Services Analytics
• Understand SQL Server 2017 R Services integration

Certification: This course will assist in preparation for the Fundamentals of SQL Server 2017 Platform Certification exams.

Product code: WLN-MSFT-BIDW

On successful Completion of the Fundamentals of SQL Server On-Premise and Cloud Data Warehousing course you will be able to:

• Learn to Plan and Scope your SQL Server Data Warehouse Projects
• Understand BI and Warehouse Architecture Frameworks
• Understand the BI and Warehouse Development Process
• Learn to Manage SQL Server Warehouse Projects
• Understand the Data Warehouse and ETL Development Lifecycle
• Understand Data Warehousing Concepts
• Understand Dimension Modelling
• Understand key SQL Server Data Warehouse concepts
• Understand SQL Data Warehouse topology and architecture
• Understand Azure Data Warehouse database design
• Understand ETL (data access, data export) in SQL Data Warehouse
• Understand data movement and distribution theory in SQL Data Warehouse
• Understand big data and SQL Server integration
• Understanding Azure Data Warehouse to BI integration, including with Power BI

Certification: This course will assist in preparation for the Microsoft Business Intelligence Certification exams.

Product code: WLN-MSFT-AZUDW

On successful Completion of the Introduction to Data Modeling course you will be able to:

• Fundamentals of normalization and why it's important to transactional databases
• How to develop logical and physical data models
• How to model data security, privacy and protection requirements
• When and where to model database-specific performance requirements
• How model-driven development fits in a DevOps or agile environment
• How to avoid schema drift and other data anti-patterns

Certification: This course will assist in preparation for the Microsoft MCSE & MCSA Certification exams.

Product code: WLN-MSFT-BIDW

On successful Completion of the Career Enabler: Decision Modeling course you will be able to:

• Decision Modeling
• Decision Tree
• Supervised learning
• Unsupervised learning
• Artificial Neural Networks
• Twitter Data Processing with R

Certification: This course will assist in preparation for the Decision Modeling Certification exams.

Product code: WLN-MSFT-BIDW

This course introduces students to the fundamentals of SQL using Oracle Database 11g database technology. In this course students learn the concepts of relational databases and the powerful SQL programming language. This course provides the essential SQL skills that allow developers to write queries against single and multiple tables, manipulate data in tables, and create database objects.
On successful Completion of the course you will be able to accomplish the following:

• Understand Relational Database Modelling
• Identify Oracle Database Development Tools
• Install your Oracle Software
• Understand the Oracle Database 11g Enterprise Edition Options
• Understand the Oracle Database Architecture
• Identify the major structural components of the Oracle Database 11g
• Manage objects with data dictionary views
• Manage schema objects
• Run data definition language (DDL) statements to create and manage schema objects
• Retrieve row and column data from tables with the SELECT statement
• Create reports of sorted and restricted data
• Display data from multiple tables using the ANSI SQL 99 JOIN syntax
• Create reports of aggregated data
• Use the SET operators to create subsets of data
• Run data manipulation statements (DML) to update data
• Employ SQL & PL/SQL functions to generate and retrieve customized data
• Create PL/SQL Triggers
• Create PL/SQL Stored Procedures and Functions
• Create PL/SQL Packages

Oracle Database 11g Essentials Self-Study CBT: Certified Implementation Specialist..

Product code: 367DDF5p

The Oracle 11g training course will teach students how to install and maintain an Oracle database, how to create an operational database and properly manage the various structures in an effective and efficient manner including performance monitoring, database security, user management, and backup/recovery techniques.
On successful Completion of the course you will be able to accomplish the following:

• Basics of the Oracle® Database Architecture
• Shared Memory Areas
• Installing Oracle Software
• Managing an Oracle® Instance
• Oracle® Managed Files
• Server Side Parameter File
• Oracle® Data Dictionary
• Control Files and Redo Log Files
• Managing the Physical Database Structure
• Managing Tablespaces and Datafiles
• Temporary Segments
• Storage Structures and Undo Data
• Block Utilization
• Undo Space Management
• Managing Database Objects
• Indexes
• Integrity Constraints
• Managing Database Users
• System Privileges
• Object Privileges
• Roles
• Backup and Recovery Concepts
• Perform Database Backups

This course will assist in preparation for the Oracle Database 11g Administration OCA Certification - 1Z0-052 exams.

Product code: 367DDF5p

On successful Completion of the Oracle Database 11g SQL Essentials course you will be able to:

• Run data definition language (DDL) statements
• Identify the major structural components of the Oracle Database
• Retrieve row and column data from tables with the SELECT statement
• Create reports of sorted and restricted data
• Employ SQL functions to generate and retrieve customized data
• Display data from multiple tables using the ANSI SQL 99 JOIN syntax
• Create reports of aggregated data
• Use the SET operators to create subsets of data
• Run data manipulation statements (DML) to update data

This course will assist in preparation for the Oracle Database 11g: OCA 1Z0-051 Certification exams.

Product code: 367DDF5p

This course introduces students to the fundamentals of PL/SQL using Oracle Database 11g database technology. In this course students learn the concepts of relational databases and the powerful PL/SQL programming language.
On successful Completion of the course you will be able to accomplish the following:

• Write Executable Statements
• Declaring PL/SQL Variables
• Working with Composite Data Types
• Conditionally control code flow (loops, control structures)
• Explicit cursors
• Handle runtime errors
• Describe stored procedures and functions

This course will assist in preparation for the Oracle Database 11g: OCA 1Z0-144 Certification exams.

Product code: 367DDF5p

On successful Completion of the course you will be able to accomplish the following:

• Design, build, and use a relational database using proven methods
• Ensure database designs accurately model business requirements
• Normalize and denormalize your data to optimize performance
• Work with integrity constraints
• Automatically generate database documentation using CASE tools

Certification: This course will assist in preparation for the Oracle Database 11g: 1Z0-051 and 1Z0-144 certification exams.

Product code: WLN-ORA-SQPLSQL

On successful Completion of the course you will be able to accomplish the following:

• Run data definition language (DDL) statements
• Identify the major structural components of the Oracle Database
• Retrieve row and column data from tables with the SELECT statement
• Create reports of sorted and restricted data
• Employ SQL functions to generate and retrieve customized data
• Display data from multiple tables using the ANSI SQL 99 JOIN syntax
• Create reports of aggregated data
• Use the SET operators to create subsets of data
• Run data manipulation statements (DML) to update data
• Employ SQL & PL/SQL functions to generate and retrieve customized data
• Create PL/SQL Triggers
• Create PL/SQL Stored Procedures and Functions

Certification: This course will assist in preparation for the Oracle Database 11g: 1Z0-051 and 1Z0-144 certification exams.

Product code: WLN-ORA-SQPLSQL

On successful Completion of the course you will be able to accomplish the following:

• Run data definition language (DDL) statements
• Identify the major structural components of the Oracle Database
• Retrieve row and column data from tables with the SELECT statement
• Create reports of sorted and restricted data
• Employ SQL functions to generate and retrieve customized data
• Display data from multiple tables using the ANSI SQL 99 JOIN syntax
• Create reports of aggregated data
• Use the SET operators to create subsets of data
• Run data manipulation statements (DML) to update data
• Employ SQL & PL/SQL functions to generate and retrieve customized data
• Create PL/SQL Triggers
• Create PL/SQL Stored Procedures and Functions

Certification: This course will assist in preparation for the Oracle Database 12c: 1Z0-061 and 1Z0-144 certification exams.

Product code: WLN-ORA-DB12C-SQPLSQL

This course introduces students to the fundamentals of PL/SQL using Oracle Database 12c database technology. In this course students learn the concepts of relational databases and the powerful PL/SQL programming language.
On successful Completion of the course you will be able to accomplish the following:

• Introduction to PL/SQL Programming
• PL/SQL Blocks
• Creating an Anonymous Block
• Write Executable Statements
• Declaring PL/SQL Variables
• Working with Composite Data Types
• Conditionally control code flow (loops, control structures)
• Explicit cursors
• Handle runtime errors
• Describe stored procedures and functions

This course will assist in preparation for the Oracle Database 12c: OCA 1Z0-144 Certification exams.

Product code: WLN-ORA-DB12C-PLSQL

On successful Completion of the Oracle 11g Data Warehousing Fundamentals Self-Study CBT course you will be able to:

• Define the terminology and explain the basic concepts of data warehousing
• Understand the Oracle Data Warehouse Technology Stack
• Understand the Oracle Data Warehouse Reference Architecture
• Understand the Oracle Business Intelligence and Analytics Technology Stack
• Describe methods and tools for extracting, transforming, and loading data
• Use Oracle Data Integrator 11g ETL for extracting, transforming, and loading data
• Identify some of the tools for accessing and analysing warehouse data
• Identify the technology and some of the tools from Oracle to implement a successful data warehouse
• Define the decision support purpose and end goal of a data warehouse
• Describe the benefits of partitioning, parallel operations, materialized views, and query rewrite in a data warehouse
• Understand the implementation and organisational issues surrounding a data warehouse project
• Use materialised views and query rewrite to improve the data warehouse performance
• Develop familiarity with some of the technologies required to implement a data warehouse

Certification: This course will assist in preparation for the Oracle BI Certified Implementation Specialist 1Z0-591 Certification exam.

Product code: WLN-ORA-DWFNDM

On successful Completion of the Oracle 12c Data Warehousing Fundamentals Self-Study CBT course you will be able to:

• Define the terminology and explain the basic concepts of data warehousing
• Understand the Oracle Data Warehouse Technology Stack
• Understand the Oracle Data Warehouse Reference Architecture
• Understand the Oracle Business Intelligence and Analytics Technology Stack
• Describe methods and tools for extracting, transforming, and loading data
• Use Oracle Data Integrator 11g ETL for extracting, transforming, and loading data
• Identify some of the tools for accessing and analysing warehouse data
• Identify the technology and some of the tools from Oracle to implement a successful data warehouse
• Define the decision support purpose and end goal of a data warehouse
• Describe the benefits of partitioning, parallel operations, materialized views, and query rewrite in a data warehouse
• Understand the implementation and organisational issues surrounding a data warehouse project
• Use materialised views and query rewrite to improve the data warehouse performance
• Develop familiarity with some of the technologies required to implement a data warehouse

Certification: This course will assist in preparation for the Oracle BI Certified Implementation Specialist 1Z0-591 Certification exam.

Product code: WLN-ORA-DWFNDM

On successful Completion of the Oracle 18c Data Warehousing Fundamentals Self-Study CBT course you will be able to:

• Define the terminology and explain the basic concepts of data warehousing
• Define the terminology and explain the basics of Oracle Big data warehousing
• Understand the Oracle 18c Data Warehouse Technology Stack
• Understand the Oracle 18c Multitenant Architecture for Data Warehousing
• Understand the Oracle 18c Data Warehouse Reference Architecture
• Discuss the different Oracle tools to implement data warehousing on-premise
• Discuss Autonomous Data Warehouse Cloud (ADWC) and Data Integration Platform Cloud (DIPC)
• Understand the Oracle Business Intelligence and Analytics Technology Stack
• Describe methods and tools for extracting, transforming, and loading data
• Use Oracle Data Integrator ETL for extracting, transforming, and loading data
• Identify some of the tools for accessing and analysing warehouse data
• Understand the Oracle 18c AI & Machine Learning Tools for data warehousing
• Identify the technology and some of the tools from Oracle to implement a successful data warehouse
• Define the decision support purpose and end goal of a data warehouse
• Describe the benefits of partitioning, parallel operations, materialized views, and query rewrite in a data warehouse
• Understand the implementation and organisational issues surrounding a data warehouse project
• Use materialised views and query rewrite to improve the data warehouse performance
• Develop familiarity with some of the technologies required to implement a data warehouse

Certification: This course will assist in preparation for the Oracle BI Certified Implementation Specialist 1Z0-591 Certification exam.

Product code: WLN-ORA-DW18cFNDM

On successful Completion of the Oracle Data Integrator 11g Fundamentals course you will be able to:

• Understand the ETL Development Lifecycle
• Understand basic Data warehousing Concepts
• Understand basic Dimension Modelling
• Understand Data Quality Issues in ETL Process
• Understand the architecture of the Oracle Data Integrator system
• Define Data Sources
• Define Physical and Logical Architecture
• Create and reverse-engineer models
• Manage Projects
• Define Integration Processes
• Work with projects in Oracle Data Integrator
• Release your ODI work for deployment
• Understand models, reverse-engineering model metadata from your database
• Manage metadata in Oracle Data Integrator: models and data quality
• Create simple interfaces and add multiple sources to an interface
• Launch a session to run an interface
• Enforce data quality constraints in an interface
• Manage packages

Certification: This course will assist in the preparation for the Oracle Data Integrator 11g Certified Implementation Essentials - 1Z0-482 exam.

Product code: WLN-ORA-ODIFNDM

On successful Completion of the Oracle Data Integrator 11g Certified Implementation Essentials - 1Z0-482 course you will be able to:

• Learn to Plan and Scope your ETL/ELT Projects
• Understand the ETL Development Lifecycle
• Describe Oracle Fusion Middleware (FMW)
• Describe the Oracle Data Integrator (ODI) system logical architecture
• Identify and describe Oracle DI Java components
• Identify the system administration tools that are available for managing Oracle DI
• Define the Oracle Data Integrator 11g (ODI) Information System architecture
• Describe scenarios in which FMW Control is available
• Start and stop instances of WebLogic Server by using FMW Control
• Configure ODI11g Java EE Agent Components
• Monitor a WebLogic Server (WLS) domain with FMW Control
• Centrally manage Oracle DI components using Fusion Middleware Control
• Centrally manage Oracle DI Java components using the Oracle WebLogic Server Console
• Stop and start Oracle DI components
• Understand Data Quality Issues in ETL Process
• Understand the architecture of the complete Oracle Data Integrator system
• Create Data Servers and Sources
• Define Physical and Logical Architecture
• Create and reverse-engineer models
• Manage Projects
• Define Integration Processes
• Work with projects in Oracle Data Integrator
• Release your ODI work for deployment
• Understand models, reverse-engineering model metadata from your database
• Manage metadata in Oracle Data Integrator: models and data quality
• Create simple interfaces and add multiple sources to an interface
• Launch a session to run an interface
• Enforce data quality constraints in an interface
• Manage packages
• Manage and Monitor ETL processes in the ODI Console

Certification: This course will assist in the preparation for the Oracle Data Integrator 11g Certified Implementation Essentials - 1Z0-482 exam.

Product code: WLN-ORA-ODIBC