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.
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.
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.
Predictive Analytics using KNIME Analytics Platform CBT
Earn 50K with Analytics Skills
£129.99
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.
Data Science and Big Data Analytics with KNIME Analytics Platform CBT
Earn 50K with Analytics Skills
£129.99
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.
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.
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.
Data Science and Machine Learning with RapidMiner CBT
Earn 50K with Analytics Skills
£129.99
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.
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.
Data Analysis and Machine Learning with RapidMiner CBT
Earn 50K with Analytics Skills
£129.99
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.
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.
Data Science and Big Data Analytics with RapidMiner CBT
Earn 50K with Analytics Skills
£119.99
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.
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.
Data Analysis and Visualization with Oracle Analytics Cloud/Server 5.5 CBT
Earn 50K with Analytics Skills
£139.99
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.
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.
Business Intelligence on Oracle Analytics Cloud/Server 5.x CBT
Earn 50K with Analytics Skills
£139.99
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.
Machine Learning with Oracle Autonomous Database CBT
Earn 50K with Analytics Skills
£139.99
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.
Data Analysis and Visualization with Oracle Analytics Desktop CBT
Earn 50K with Analytics Skills
£99.99
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.
Oracle Analytics Server: Data Modelling for BI Administrators CBT
Earn 50K with Analytics Skills
£179.99
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.
Oracle Analytics Server: BI Publisher Reporting CBT
Earn 50K with Analytics Skills
£119.99
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.
Big Data Analytics with Oracle Advanced Analytics CBT
Earn 50K with Analytics Skills
£159.99
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.
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.
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.
Predictive Analytics using Oracle Advanced Analytics CBT
Earn 50K with Analytics Skills
£149.99
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.
Predictive Analytics with Oracle Machine Learning CBT
Earn 50K with Analytics Skills
£119.99
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.
Predictive Analytics with Oracle Machine Learning for R (OML4R) CBT
Earn 50K with Analytics Skills
£129.99
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.
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.
Oracle Database 19c: Analytic SQL for Data Warehousing CBT
Earn 50K with Analytics Skills
£119.99
On successful Completion of the course you will get a sound understanding of:
Use SQL with aggregation operators, SQL for Analysis and Reporting functions
Group and aggregate data using the ROLLUP and CUBE operators
Analyze and report data using Ranking, LAG/LEAD, and FIRST/LAST functions
Analyze and report data using the PIVOT and UNPIVOT clauses
Use the MODEL clause to create a multidimensional array from query results
Use Analytic SQL to aggregation, Analyze and Reporting, and Model Data
Interpret the concept of a hierarchical query, create a tree-structured report
Format hierarchical data, and exclude branches from the tree structure
Use regular expressions to search for, match, and replace strings
Perform pattern matching using the MATCH_RECOGNIZE clause
This Oracle Database 19c: Analytic SQL for Data Warehousing training teaches you how to interpret the concept of a hierarchical query, create a tree-structured report, format hierarchical data and exclude branches from the tree structure.
Oracle Database 12c: Analytic SQL for Data Warehousing CBT
Earn 50K with Analytics Skills
£119.99
On successful Completion of the course you will get a sound understanding of:
Use SQL with aggregation operators, SQL for Analysis and Reporting functions
Group and aggregate data using the ROLLUP and CUBE operators
Analyze and report data using Ranking, LAG/LEAD, and FIRST/LAST functions
Analyze and report data using the PIVOT and UNPIVOT clauses
Use the MODEL clause to create a multidimensional array from query results
Use Analytic SQL to aggregation, Analyze and Reporting, and Model Data
Interpret the concept of a hierarchical query, create a tree-structured report
Format hierarchical data, and exclude branches from the tree structure
Use regular expressions to search for, match, and replace strings
Perform pattern matching using the MATCH_RECOGNIZE clause
This Oracle Database 19c: Analytic SQL for Data Warehousing training teaches you how to interpret the concept of a hierarchical query, create a tree-structured report, format hierarchical data and exclude branches from the tree structure.
Applied Predictive Analytics for Telecommunications: Concepts and Techniques CBT
BI and Analytics
£279.99
On successful Completion of the Applied Predictive Analytics for Telecommunications: Concepts and Techniques Self-Study CBT course you will be able to:
Mechanics of operating a data mining toolkit for the Telecommunications Industry
Appreciate role of Predictive Analytics in the business intelligence architecture
Data mining Techniques for Telecommunications CRM
Describe CRM
Explain the importance of CRM for users
Identify the common functional areas addressed by a CRM solution
Describe the technological structure of CRM applications
Understand mining Techniques for Customer segmentation
Data mining Techniques for predicting Credit Risk
Apply Data mining for predicting Customer Churn
Apply Data mining for predicting Customer Life-time Value (CLV)
Apply Data mining for predicting Fraudulent Activities
Identification of cross-sell or up-sell opportunities
Generic method as a guideline for the newcomer and the expert
Technical aspects of the mining algorithms
Necessary data preparation steps in a detailed manner
Proven mining applications in the field
Further steps for improvement of the predictive analytics results
Certification: This course will assist in preparation for the Oracle BI Certified Implementation Specialist 1Z0-591 Certification exam..
Business Intelligence and Data Warehousing Fundamentals CBT
BI Foundation Suite
£189.99
On successful Completion of the Business Intelligence and Data Warehousing Fundamentals Self-Study CBT course you will be able to:
Define the terminology and explain basic concepts of data warehousing
Identify the technology and some of the tools from Oracle and Microsoft to implement a successful data warehouse
Describe methods and tools for extracting, transforming and loading data
Identify some of the tools for accessing and analysing warehouse data
Describe the benefits of partitioning, parallel operations, materialized views and query rewrite in a data warehouse
Explain the implementation and organizational issues surrounding a data warehouse project
Improve performance or manageability in a data warehouse using various Oracle Database features
Understand Business, Logical, Dimensional, and Physical Modeling
Understand Oracle, SAP and Microsoft ETL Development Tools
Understand the Oracle, SAP, Microsoft and IBM Cognos Business Intelligence and Analytics Technology Stack
Understand the The BI and OLAP Development Life-Cycle
Understand the BI and Warehouse Architecture Framework
Understand Business Dimension Modelling
Build Data Analysis, Visualisation and Interactive Dashboards in Oracle BIEE
Build Data Analysis and Visualisation in Cognos Reports Studio
Certification: This course will assist in preparation for the IBM, Microsoft and Oracle BI Certified Implementation Specialist 1Z0-591 Certification exams.
On successful Completion of the Hyperion FM 11.1.2 Certified Implementation Specialist course you will be able to:
Use EPM Workspace
Manage Financial Management Applications
Create Classic Planning Applications
Create applications and setting up profiles
Load data to HFM and the various alternatives available for developers
Work with formulas, add charts, deploy reports with books and batches
Build reports using the Smart View client
Address management issues such as security, backing up Reports
Understand Performance Management Architecture and Infrastructure
Install and Configure Hyperion EPM Financial Management System 11.1.2
Install and Configure Hyperion Financial Management 11.1.2 Server and Clients
Understand EPM Foundation Modules including Essbase
Understand EPM System Applications
Understand EPM Architect Modules
Understand EPM System Integration with Oracle BIEE
Understand and use Shared Services
User Management, including: User provisioning, External authentication definition
Understand Lifecycle Management and for Migrations
Certification: This course will assist in preparation for the Hyperion Financial Management 11.1.2: Implementation Specialist - 1Z0-532 Certification exam.
Hyperion FM 11.1.2: Create & Manage Applications CBT
HYperion Financial Management
£169.99
On successful Completion of the Hyperion FM 11.1.2: Create & Manage Applications course you will be able to:
Navigate Workspace
Navigate Financial Management
Import and export data and metadata
Create and deploy an application
Enter data in data forms and data grids
Enter data in data grids
Enter data in data forms
Adjust data with journals
Process and report on journals
Eliminate intercompany balances
Manage intercompany transactions
Run consolidations
Manage the review cycle through Process Management
Synchronize data
Automate Tasks
Analyze data with Smart View
Enter data by using data forms in Smart View
Analyze data with Smart View functions
Certification: This course will assist in preparation for the Hyperion Financial Management 11.1.2: Implementation Specialist - 1Z0-532 Certification exam.
On successful Completion of the Hyperion EPM Planning 11.1.2 Administration: Installation & Configuration - 1Z0-533 CBT course you will be able to:
Identify the Planning installation requirements
Describe the architecture of WebLogic Server including domains, servers and machines
Install and configure WebLogic Server
Install and configure Foundation Services and Essbase
Perform backup, recovery, application migration, and task automation
Design and manage Essbase security
Install and configure Planning
Verify the Planning installation
Certification: This course will assist in preparation for the Hyperion EPM Planning 11.1.2 Administration: Installation & Configuration - 1Z0-533 Certification exam.
On successful Completion of the Hyperion EPM Financial Management 11.1.2 Administration: Installation & Configuration - 1Z0-533 CBT course you will be able to:
Identify the Planning installation requirements
Describe the architecture of WebLogic Server including domains, servers and machines
Install and configure WebLogic Server
Install and configure Foundation Services and Essbase
Perform backup, recovery, application migration, and task automation
Design and manage Essbase security
Install and configure Financial Management
Verify the Financial Management installation
Certification: This course will assist in preparation for the Hyperion EPM Financial Management 11.1.2 Administration: Installation & Configuration - 1Z0-533 Certification exam.
On successful Completion of the HYPERION EPM Applications 11.1.2 Administration Boot Camp - 1Z0 532 and 1Z0 533 CBT course you will be able to:
Understand EPM System Architecture and Infrastructure
Identify the installation requirements
Describe the architecture of WebLogic Server including domains, servers and machines
Install and configure WebLogic Server
Install and configure Foundation Services and Essbase
Design and manage Essbase security
Install and configure Planning
Install and configure Financial Management
Install and configure Reporting and Analysis
Install and configure Strategic Finance
Verify the installation
Perform backup, recovery, application migration, and task automation
Certification: This course will assist in preparation for the HYPERION EPM Applications 11.1.2 Administration Boot Camp - 1Z0 532 and 1Z0 533 Certification exam.
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
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