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API Testing using POSTMAN, SOAP UI Online Course Content

API Testing using POSTMAN, SOAP UI Online Course Content

API Introduction

Introduction to web application architecture
Introduction to APIs
Introduction to Web-Services
How does an API works
What is API testing?
API TESTING USING POSTMAN– FOUNDATION COURSE
Advantages of API

API vs Web-Services
Introduction to API architecture
REST API
SOAP API
Understanding how REST API architecture works
Understanding how SOAP API architecture works
Understanding the HTTP methods
GET
POST
PUT
DELETE
PATCH
OPTIONS
HEAD
Few more

API TESTING
What does API testing involve
Validation techniques used in API testing
API testing steps
Understanding URI, endpoints, Resources, HTTP verbs
Understanding GET request
Understanding POST request
Understanding PUT request
Understanding DELETE request
GUI tools available for API testing
Command-line tools available for API testing
Best Practices for API testing

INTRODUCTION TO POSTMAN API TESTING TOOL
What is the Postman tool
Installation of Native Postman tool
Installation of Postman tool as Chrome Add-on
Introduction to Postman landscape
Introduction to Postman Settings

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R Programming - Advanced Analytics In R For Data Science

R programming in data analytics Analytics Online Course

What is R programming in data analytics?

R is a programming language possessing an extensive catalogue consisting of graphical and statistical methods. It includes some major learnings like machine learning algorithms, time series, linear regressions, etc. While talking about the R programming language, computational tasks like C++ and Fortran codes are taken into consideration. Data analytics with R language is done using 4 simple steps:

  • Program: R programming is a clear and accessible programming tool
  • Transform: R helps in making a collection of libraries that is meant for data science
  • Discover: It helps in investigating the data and refining your hypothesis for analysis
  • Model: It provides with a wide array of tools for capturing of right model for the data
  • Communicate: R programming language is used for integrating codes, output, and graphs.

Why use this programming language?

  • Data analysis software: Anyone like data analysts, data scientists need to make sense of data then they can make use of R for analytics statistics and predictive modelling.
  • Programming language: Being an object-oriented language that is created by statisticians, it provides objects and functions for allowing users to explore, model, and visualize
  • Statistical analysis: All the standard statistical methods are easy to analyse using R, and in this cutting-edge world, predictive modelling is new in R and all development techniques are used in R first.
  • Community: R programming language has brought so many communities of scientists and statisticians together in this world for performing this language. Having over 2 million users, it has a vibrant online community.

R language is worth all its popularity and it is going upscale only. It allows the wide practice of graphical techniques and in the future, R programming language will be used even more. Whether it is automating tasks or designing algorithms, R programming language is used in all fields..

Data Analytics in R Certificate Online Course Content 

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Oracle Fusion Middle Ware Online Training

Oracle Fusion Middle Ware Online Training Content

FMW Oracle Fusion middle ware Online Training Content 

Introduction to IDAM Technology

  • Identity Management
  • Access Management
  • Fusion Middleware Concepts
  • LDAP Structure Directory
  • Web Logic Server

Installation

  • Installation of ODBMS
  • Installation of FMW
  • Creating different cluster of Managed Servers

Weblogic and FMW

  • Cluster environment
  • Data Sources
  • Server Tweak
  • Security Realm and Authentication Provide

Course Time: 10hrs

 Oracle Fusion Middleware Online Training Placement Service

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Business Intelligence, Business Analysts, Data Analysts or Business Technology Analysts

Business Intelligence

Definition:
Business Intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current, and predictive views of business operations.

Terms of Business Intelligence:
The terms of Business Intelligence (BI) refers to technologies , applications and practices for the collection , integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making.

Role of Business Intelligence:
Business Intelligence is a type of software that can harness the power of data within an organization. These insights can help companies make strategic business decision that increase productivity, improve revenues, and enhance growth.

What is Business Intelligence?

Business Intelligence system combine data gathering , data storage , and knowledge management with data analysis to evaluate and transform complex data into meaningful, actionable information, which can be used to support more effective strategic , tactical, and operational insights and decision making.
Business Intelligence environments consist of a variety of technologies, applications, processes, strategies, products, and technical architectures used to enable the collection, analysis, presentation, and dissemination of internal and external business information.

Benefits Of Business Intelligence

The important of business intelligence continues to grow as businesses face an ever-increasing flow of raw data and the challenges of gaining insight from enormous volumes of information (big data).
With the employment of business intelligence systems, businesses can gain a comprehensive view of their organization’s data and translate it into insights about their business processes, enabling improved and strategic business decisions.

Business Analysts

A business analysts is someone who analyzes who analyzes an organization or business domain and documents its business or processes or systems assessing the business model or its integration with technology.
Business Analysts helps in guiding businesses in improving processes,
products, services and software through data analysis.

Benefits Of Business Analysts

Opportunity to liaise and network.
A fast paced career .
A promising vocation with visible growth.
Opportunity to see the bigger picture.
High organizational visibility and respect.
Chance to be carries of change.
Gradual growth of communication and soft skills.
Exposure to multiple domains.

Roles Of Business Analysts

Business Analysts work with organizations to help them improve their processes and systems . They conduct research and analysis in order to come up with solutions to business problems and help to introduce these system to businesses and their clients.

Role for business – IT efficiency: Business Analyst help guide businesses in improving processes , products,
services and software through data analysis. These agile workers straddle the line between IT and the business to help bridge the gap and improve efficiency.

Data Analysts

Data analysis is a process of inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusions and supporting decision-marking.

Benefits Of Data Analysts

Proactivity & Anticipating Needs.
Mitigating Risks & Fraud.
Delivering Relevant Products.
Personalisation & Service.
Optimizing & Improving the Customer Experience.

Roles Of Data Analysts

A data analyst interprets data turns it into information which can offer ways to improve a business , thus affecting business decisions.
Data Analysts gather information from various sources and interprets patterns and trends – as such a Data analysts job description should highlight the analytical nature of the role.

Business Technology Analysts

Business Technology analysts work with a business to assist in technology integration. This includes overseeing a company’s current information technology(IT) systems to ensure that the company’s technology – related goals are met.
Their tasks may also include implementation of new IT systems. In these cases, business technology analysts must ensure that daily business is disrupted as little as possible.
They should be able to compile , analyse and aggregate data. Additionally, these analysts train users and run both users and system tests.

Role of technical analyst

Technical analysts improve and maintain an enterprise’s information technology system.
They are tasked with examining the function of computer systems, identifying problems and areas for improvement in execution, and designing solutions.

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Data science Online Training with SAS and R

    1. Critical SAS programming skills.
    2. Accessing, transforming, and manipulating data.
    3. Improving data quality for reporting and analytics.
    4. Fundamentals of statistics and analytics.
    5. Working with Hadoop, Hive, Pig, and SAS.
    6. Exploring and visualizing data.
    7. Essential communication skills.
    8. Machine learning and predictive modeling techniques.
    9. How to apply these techniques to distributed and in-memory big data sets.
    10. Pattern detection.
    11. Experimentation in business.
    12. Optimization techniques.
    13. Time series forecasting.
  • Course Content
  • Data Science with R
  •  Course IntroductioN
  • Introduction to Business Analytics
  • Introduction to R Programming
  •  Data Structures
  • Data Visualization
  • Statistics for Data Science-I
  • Statistics for Data Science-II
  • Regression Analysis
  • Classification
  • Clustering
  • Association
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live Abinitio Course adithyaelearning

Live Demo on Abinitio @ 7.30 pm IST today 27-08-2020

New Meeting
Please join my meeting from your computer, tablet or smartphone.
You can also dial in using your phone.
(For supported devices, tap a one-touch number below to join instantly.)
United States: +1 (312) 757-3121
– One-touch: tel:+13127573121,,377307789#
Access Code: 377-307-789
New to GoToMeeting? Get the app now and be ready when your first meeting starts: https://global.gotomeeting.com/install/377307789
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Tableau 10 Certification Course

Course Content for Tableau Certification and Jobs

TABLEAU
1) Introduction
ï‚· Business Intelligence Introduction
ï‚· Gartner Architecture
ï‚· Components Overview

2) Getting Started
ï‚· Getting Started
ï‚· The Tableau Interface
ï‚· Connection, Data Source, Visualization Area
ï‚· Joins & Cardinalities

3) Connecting to Data
ï‚· Getting Started with Data
ï‚· Connecting to Databases and Advanced Features
ï‚· Data Prep with Text and Excel Files
ï‚· Splitting & Custom Splitting
ï‚· Pivot & Data Cleansing
ï‚· Editing Data Connections and Data Sources
ï‚· Editing Metadata and Saving Data Sources
ï‚· Using and Refreshing Extracts
ï‚· Join Types
ï‚· Cardinalities
ï‚· Difference Between Live & Extract
ï‚· Data Blending

4) Visual Analytics
ï‚· Getting Started with Visual Analytics
ï‚· Dimension
ï‚· Measures
ï‚· Drill Down and Hierarchies
ï‚· Sorting
ï‚· Grouping
ï‚· Creating Sets
ï‚· Ways to Filter
ï‚· Using the Filter Shelf
ï‚· Quick Filters
ï‚· General
ï‚· Wildcard
ï‚· Condition
ï‚· Filtering for Top and Top N
ï‚· Parameters
ï‚· Color
ï‚· Size
ï‚· Detail
ï‚· Tool Tip
ï‚· Label
ï‚· Trend Lines
ï‚· Reference Lines
ï‚· Forecasting

5) Mapping
ï‚· Getting Started with Mapping
ï‚· Maps in Tableau
ï‚· Using Latitude & Longitude
ï‚· Editing Unrecognized Locations

6) Calculations
ï‚· Getting Started with Calculations
ï‚· Calculation Syntax
ï‚· Aggregate Calculations
ï‚· Date Calculations
ï‚· Logic Calculations
ï‚· Number Calculations
ï‚· String Calculations

7) Dashboards and Stories
ï‚· Getting Started with Dashboards and Stories
ï‚· Building a Dashboard
ï‚· Dashboard Layouts and Formatting
ï‚· Dashboard Interactivity using Actions
ï‚· Highlight Action
ï‚· URL Action
ï‚· Filter Action
ï‚· Dashboard Best Practices

ï‚· Story Points

8) Different Type Of Charts
ï‚· Column Chart
ï‚· Bar Chart
ï‚· Pie Chart
ï‚· Scatter Plot
ï‚· Bubble Chart
ï‚· Stacked Bars
ï‚· Tree Maps
ï‚· Heat Maps
ï‚· Line Charts
ï‚· Area maps

9) Tableau Online

10) Publish Dashboard Contents

Real-Time Scenarios / Tableau Marathon

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Power BI Course Content

Lesson 1 – Business Intelligence & Data Science Introduction
What is Business Intelligence?
The flow of Business Intelligence
Components of Business Intelligence

Lesson 2 – Power BI – First Step
Gartner Architecture
Why Power BI?
Gartner Magic Quadrant Review
Power BI History – When, Where & Why
Installing Power BI Desktop
Signing up for Power BI Service

Lesson 3 – Introduction to Different Interfaces

Getting Started with Power BI
The Power BI Interface
Report Panel
Data Panel
Model Panel
Your first Power BI Visualization

Lesson 4 – Transforming Data Using Power BI Desktop
Introduction to Power BI Desktop

Connecting to Data
Getting Started with Data
Connecting to Databases and Advanced Features
Analyzing the Project Data Warehouse
Changing Locale
Connecting to a Database
Basic Transformations
Managing Query Groups
Splitting Columns
Changing Data Types
Working with Dates
Removing and Reordering Columns
Conditional Columns
Connecting to Files in a Folder
Merge Queries
Query Dependency View
Transforming Less Structured Data
Enter Data
Query Parameters
Data Prep with Excel Files
Data Cleansing
Editing Data Connections and Data Sources
Editing Metadata and Saving Data Sources
Data Blending

Lesson 5 – Power BI – Visualizing Your Data
Pie and Treemap
Hierarchical Axis and Concatenating

Filter (Including TopN)
Bar Chart with Line
Analytics Pane
Slicer
Focus Mode and See Data
Date Slicer
Map and Filled Map
Table and Matrix
Table Styles
Scatter Chart
Waterfall
Gauge, Card, and KPI
Coloring Charts
Shapes, Textboxes, and Images

Lesson 6 – Visual Analytics – Working With Multiple Visualization
Page Layout and Formatting
Visual Relationship
Duplicate Page
Categories with No Data
Default Summarization and Categorization
Positioning, Aligning, and Sorting Visuals
Custom Hierarchies
Trend Lines
Forecasting

Lesson 7 – Playing With DAX

Introduction to DAX
DAX calculation types
DAX functions
Using variables in DAX expressions
Table relationships and DAX
DAX tables and filtering

Lesson 8 – Working with Power BI Service – Dashboard
Overview of Dashboards and Service
Uploading to Power BI Service
Quick Insights
Configuring a Dashboard
Adding Textbox, Image Widgets
Featured and Favorite Dashboard
Filtering Dashboard
Dashboard Settings
Featured Questions
Sharing a Dashboard
In-Focus Mode
Pinning a Live Page
Custom URL and Title
TV Mode and Collapse Navigation
Printing Dashboard and Exporting Data
Export to CSV and Excel
Publishing to Web

Lesson 9 – Viewing Power BI Dashboard
Viewing in Windows App / Android App / iPad / iPhone

Lesson 10 – Mobile App – Power BI
Report Gallery and Search
Mobile Dashboard Layout
Sharing and Annotating in Power BI Mobile
Platform-Specific Features
Search and Recent

Lesson 11 – Publishing & Projects
Publishing Contents
Interview Tips
Projects

Lesson 12 – Final Conclusion – Wrap Up
Submitting Interview Questions
Interview Tips
IT Companies Project Flow
Agile/Scrum & Waterfall

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Advanced Big Data Hadoop Online Training Course content

Introduction; Advanced Big Data Hadoop

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.
Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig, and Hive. By following along with the provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets.

1.Introduction to Hadoop and Big-data
• Introduction to Big-data?
• Introduction to Hadoop?
• Business problems / Challenges with Big data?
• Scenarios where Hadoop is used?
• Overview of Batch Processing and real-time data analytics using Hadoop?
• Hadoop vendors – Apache, Cloudera, Hortonworks?
• Hadoop versions – Hadoop 1.x and Hadoop 2.x?
• Hadoop services – HDFS, MapReduce, YARN?
• Introduction to Hadoop ecosystem components
(Hive, HBase, Pig, Sqoop, Flume, Zookeeper, Kafka, Spark)?

2.Cluster setup (Hadoop 1.x)

• Linux VM installation on the system for the Hadoop cluster using Oracle Virtual Box?
• Preparing nodes for Hadoop and VM settings?
• Install Java and configure passwordless SSH across nodes?
• Basic Linux commands?
• Hadoop 1.x Single node deployment?
• Hadoop Daemons – NameNode, JobTracker,

DataNode, TaskTracker, Secondary NameNode?
• Hadoop configuration files and running?
• Important Web URLs and Logs for Hadoop?
• Run HDFS and Linux commands?
• Hadoop 1.x multi-mode deployment?
• Run sample jobs in Hadoop single and multi-node clusters?
• HDFS Concepts
• HDFS Design Goals
• Understand Blocks and how to configure block size
• Block replication and replication factor
• Understand Hadoop Rack Awareness and configure racks in Hadoop
• File read and write anatomy in HDFS Health monitoring using FSCK command?
• Understand NameNode Safemode, File system Image, and Edits?
• Configure Secondary NameNode and use the checkpointing process to provide NameNode failover?
• HDFS DFSAdmin and File system shell Commands?
• Hadoop Namenode / Datanode directory Structure?

4. MapReduce Concepts
• Introduction to MapReduce?
• MapReduce Architecture?
• Understanding the concept of Mappers & Reducers?
• Anatomy of MapReduce Program?
• Phases of a MapReduce program?
• Data-types in Hadoop MapReduce?
• Driver, Mapper, and Reducer classes?
• InputSplit and RecordReader?
• InputFormat and OutputFormat in Hadoop?
• Concepts of Combiner and Partitioner?
• Running and Monitoring MapReduce jobs?
• Writing your own MapReduce job using MapReduce API?

5. Cluster setup (Hadoop 2.x)
• Hadoop 1.x Limitations?
• Design Goals for Hadoop 2.x?
• Introduction to Hadoop 2.x?
• Introduction to YARN?
• Components of YARN – ResourceManager, NodeManager, ApplicationMaster?
• Deprecated properties?
• Hadoop 2.x Single node deployment?
• Hadoop 2.x multi-mode deployment?

6.HDFS High Availability and Federation
• Introduction to HDFS Federation
• Understand Nameservice ID and block pools
• Introduction to HDFS High Availability
• Failover mechanisms in Hadoop 1.x
• Concept of Active and Standby NameNode
• Configuring Journal Nodes and avoiding a split-brain scenario
• HDFS HAadmin commands?

7. YARN – Yet Another Resource Negotiator

• YARN Architecture?
• YARN Components – ResourceManager, NodeManager, JobHistoryServer, Application TimelineServer, MRApplicationMaster?
• YARN Application execution flow?
• Running and Monitoring YARN Applications?

8.Apache Zookeeper
• Introduction to Apache Zookeeper?
• Zookeeper stand-alone installation?
• Zookeeper clustered installation?
• Understand Znode and Ephemeral nodes?
• Manage Znodes using Java API?
• Zookeeper four-letter word commands?

9. Apache Hive
• Introduction to Hive?
• Hive Architecture?
• Components – Metastore, HiveServer2, Beeline, HiveCli,

Hive WebInterface?
• Installation and configuration?
• Metastore service?
• DDLs and DMLs?
• SQL – Select, Filter, Join, Group By?
• Hive Partitions and buckets in Hive?
• Install and configure HCatalog services?

10. Apache Pig
• Introduction to Pig
• Pig installation
• Accessing Pig Grunt shell?
• Pig Data Types?
• Pig commands?
• Pig Relational Operators?

11. Apache Sqoop
• Introduction to Sqoop
• Sqoop Architecture and Installation
• Import data using Sqoop in HDFS
• Import all tables in Sqoop
• Import tables directly in Hive
• Export data from HDFS

12.Apache Flume
• Introduction to Flume
• Flume Architecture and Installation
• Define Flume agent – Sink, Source and Channel
• Flume Use Cases

13.Apache HBase
• Introduction to HBase
• HBase Architecture
• HBase components — HBase Master and RegionServers
• HBase installation and configurations
• Create sample tables and queries on HBase

14.Apache Spark / Storm / Kafka
• Real-time data Analytics?
• Introduction to Spark / Storm / Kafka

15.Cluster Monitoring and Management tools
• Cloudera Manager?
• HUE

Projects are:-
• Pokémon Data Analysis
• Flight data analysis
• Sales data analysis
• Stock Data analysis

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SAS Certified Platform Administrator Certification in India

SAS A00-250 Exam Summary:
Exam Name SAS Certified Platform Administrator for SAS 9
Exam Code A00-250
Exam Duration 3hrs
Exam Questions 70 multiple-choice and short-answer questions
Passing Score 70%
Exam Price $180 (USD)
Training Getting Started with the Platform for SAS Business
https://support.sas.com/edu/schedules.html?ctry=us&id=3087
Analytics
https://support.sas.com/edu/schedules.html?ctry=us&id=3087
SAS Platform Administration: Fast Track SAS Platform
https://support.sas.com/edu/schedules.html?ctry=us&id=2737
Administration: Metadata Administration SAS Platform
https://support.sas.com/edu/schedules.html?ctry=us&id=3055
Administration: System Administration
https://support.sas.com/edu/schedules.html?ctry=us&id=3056
Exam Registration Pearson VUE
Sample Questions SAS Platform Administrator Certification Sample Question
Practice Exam SAS Platform Administrator Certification Practice Exam
SAS A00-250 Exam Topics:
Objective Details
Securing the SAS configuration – Regularly manage and apply hot fixes. – Apply SAS maintenance packs. – Perform SAS license updates
Monitoring the Status and Operation of SAS Metadata Servers – Explain the functionality of the object spawner.
– Use the Server Manager plug-in to monitor SAS servers.
– Use Environment Manager to evaluate the resources available on the SAS servers.
Monitoring, Logging, and Troubleshooting SAS Servers – Change logging levels and locate logs.
– Enable trace logging.
– Explain the difference between default logging and customized logging.
– Utilize dynamic logging.
Backing Up the SAS Environment – View backup and recovery history.
– Run an immediate (ad hoc) backup.
– Customize backups.
– View information about backup and recovery sources.
Administering Users Determine when to store passwords in the metadata.

Manage internal SAS accounts.

Identify SAS server authentication mechanisms.

Administering Data Access Pre-assign a library.

Troubleshoot data access problems.

Use the metadata LIBNAME engine

Managing Metadata – Use the metadata folder structure to manage access to metadata.

Create and use Access Control Templates.

SAS Platform Administrator Course Content, Material and Course Online Training and  Videos Details are available Click here

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