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Table AU Corse Topics A)Introduction and Overview. •Why Tableau? Why Visualization? •Level Setting – Terminology •Getting Started – creating some powerful visualizations quickly •The Tableau Product Line •Things you should know about Tableau B)Getting Started •Connecting to Data and introduction to data source concepts •Working with data files versus database servers •Understanding the Tableau workspace •Dimensions and Measures •Using Show Me! •Tour of Shelves (How shelves and marks work) •Building Basic Views •Help Menu and Samples •Saving and Sharing your work C)Concepts and Options when Connecting to Data •Joining multiple tables •Data Blending •Copy and Paste •Data Extracts •Custom SQL •Publishing and Re-using Data Connections •Understand how to deal with data changes in your data source such as field addition, deletion or name change •Re-using and sharing data connections – the concept of meta data •Working with multiple connections in the same workbook D)Analysis •Creating Views Marks Size and Transparency •Highlighting Working with Dates Date aggregations and date parts •Discrete versus Continuous Dual Axis / Multiple Measures Combo Charts with different mark types Geographic Map •Page Trails •Heat Map •Density Chart •Scatter Plots •Pie Charts and Bar Charts •Small Multiples Working with aggregate versus disaggregate data E)Analyzing •Sorting & Grouping •Aliases •Filtering and Quick Filters •Cross-Tabs (Pivot Tables) •Totals and Subtotals •Drilling and Drill Through •Aggregation and Disaggregation •Percent of Total •Working with Statistics and Trendlines F)Getting Started with Calculated Fields •Working with String Functions •Basic Arithmetic Calculations •Date Math •Working with Totals •Custom Aggregations •Logic Statements G)Formatting •Options in Formatting your Visualizations •Working with Labels and Annotations •Effective Use of Titles and Captions •Introduction to Visual Best Practices H)Building Interactive Dashboards •Combining multiple visualizations into a dashboard •Making your worksheet interactive by using actions and filters •An Introduction to Best Practices in Visualization I)Sharing Workbooks •Publish to Reader •Packaged Workbooks •Publish to Office •Publish to PDF •Publish to Tableau Server and Sharing over the Web

Hi Friends, I am Amit, an expert BI professional. Having competencies of providing Tableau online and classroom training to Individuals and corporate companies both in Indian and oversees clients. Please get in touch with me to discuss details about the training. Tableau Course Content ~ Introduction and Overview •Why Tableau? Why Visualization? •Level Setting – Terminology •Getting Started – creating some powerful visualizations quickly •The Tableau Product Line •Things you should know about Tableau Getting Started •Connecting to Data and introduction to data source concepts •Working with data files versus database servers •Understanding the Tableau workspace •Dimensions and Measures •Using Show Me! •Tour of Shelves (How shelves and marks work) •Building Basic Views •Help Menu and Samples •Saving and Sharing your work Concepts and Options when Connecting to Data •Overview of other connection options o Joining multiple tables o Data Blending o Copy and Paste o Data Extracts o Custom SQL o Publishing and Re-using Data Connections •Understand how to deal with data changes in your data source such as field addition, deletion or name change •Re-using and sharing data connections – the concept of meta data •Working with multiple connections in the same workbook Analysis •Creating Views o Marks o Size and Transparency o Highlighting o Working with Dates  Date aggregations and date parts  Discrete versus Continuous o Dual Axis / Multiple Measures o Combo Charts with different mark types o Geographic Map o Page Trails o Heat Map o Density Chart o Scatter Plots o Pie Charts and Bar Charts o Small Multiples o Working with aggregate versus disaggregate data •Analyzing o Sorting & Grouping o Aliases o Filtering and Quick Filters o Cross-Tabs (Pivot Tables) o Totals and Subtotals o Drilling and Drill Through o Aggregation and Disaggregation o Percent of Total o Working with Statistics and Trendlines Getting Started with Calculated Fields •Working with String Functions •Basic Arithmetic Calculations •Date Math •Working with Totals •Custom Aggregations •Logic Statements Formatting •Options in Formatting your Visualization •Working with Labels and Annotations •Effective Use of Titles and Captions •Introduction to Visual Best Practices Building Interactive Dashboards •Combining multiple visualizations into a dashboard •Making your worksheet interactive by using actions and filters •An Introduction to Best Practices in Visualization Sharing Workbooks •Publish to Reader •Packaged Workbooks •Publish to Office •Publish to PDF •Publish to Tableau Server and Sharing over the Web Putting it all together •Scenario-based Review Exercises •Best Practices Where to get Further Assistance •The Help File / Product Manual •Knowledge base •Forums •Whitepapers & Books •Further Training Offerings & Professional Services •Technical Support

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HADOOP - Modules that we undertake: Introduction to Bigdata •Architecture of BD •3V Concept •Framework and Applications/Tools •Samples •What is Hadoop? •History of Hadoop •Building Blocks - Hadoop Eco - System •Who is behind Hadoop? •What Hadoop is good for and what it is not •HDFS Overview and Architecture •HDFS Installation •HDFS Use Cases •Hadoop FileSystem Shell •FileSystem Java API •Hadoop Configuration •Map/Reduce 2.0 •Pig •Hive •Map Reduce Workflows •Sqoop •HBase - The Hadoop DataBase •Big Data Analysis with R •Data Science •Application and Certification Introduction to Big Data and Hadoop •What is Big Data? •What are the challenges for processing big data? •What technologies support big data? •Distributed systems •What is Hadoop? •Why Hadoop? •History of Hadoop •Use Cases of Hadoop •Hadoop eco System •HDFS •Map Reduce •Statistics Understanding the Cluster •Typical workflow •Writing files to HDFS •Reading files from HDFS •Rack Awareness •5 daemons Best Practices for Cluster Setup •Best Practices •How to choose the right hadoop distribution •How to choose right hardware Cluster Setup •Install Pseudo cluster •Install Multi node cluster •Configuration •Setup cluster on Cloud - EC2 •Tools •Security •Benchmarking the cluster Routine Admin procedures •Metadata & Data Backups •Filesystem check (fsck) •File system Balancer •Commissioning and decommissioning nodes •Upgrading •Using DFSAdmin Monitoring the Cluster •Using the Web user interfaces •Hadoop Log files •Setting the log levels •Monitoring with Nagios Install,Configure and use •PIG •HIVE •HBASE •Flume and Sqoop •Zookeeper Contact Details Dotexe Technologies, #23, South Sivan Koil Street, Vadapalani Chennai–,Phone:+,

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Peridot systems is a best software training institute with 100%placement.Want to join.. just give a call (9952010141- selva) . We will provide good learning environment with real time certified training by IT professionals. Before joining you can attend a free demo classes. We will arrange 100% placement for you. Tableau Fundamentals Help MENU AND SAMPLES Connecting to data Dimensions and measures and more... Tableau Advanced Custom SQL Editing data connection Data menu Exporting data connection Size and transparency Highlighting Dual Axis multiple measures Dates Difference from previous and More... Visual Analytics Grouping Aliases Filtering Cross Tabs-(Pivot tables) Aggregation & Disaggregation Trend lines Page trial and More... Tableau Server Comprehensive Single Installer Worker Installer Authentication Processes Data Server Backgrounder SSL and More.... Administration Create Projects and Groups Assign Permissions to Projects Licensing Data Connections and More..... For Full Syllabus, Contact us at Ruban (9952010141)

Tableau Advanced Online Training In Hyderabad India Tableau Course Description This course is designed for the beginner to intermediate-level Tableau user. It is for anyone who works with data – regardless of technical or analytical background. This course is designed to help you understand the important concepts and techniques used in Tableau to move from simple to complex visualizations and learn how to combine them in interactive dashboards. D.Srinivas Contact Us' + Introduction and Overview •Why Tableau? Why Visualization? •Level Setting – Terminology •Getting Started – creating some powerful visualizations quickly •The Tableau Product Line •Things you should know about Tableau 2.Getting Started •Connecting to Data and introduction to data source concept •Working with data files versus database server • Understanding the Tableau workspace •Dimensions and Measures •Using Show Me! •Tour of Shelves (How shelves and marks work) •Building Basic Views •Help Menu and Samples •Saving and sharing your work 3.Analysis Creating Views •Marks •Size and Transparency •Highlighting •Working with Dates •Date aggregations and date parts •Discrete versus Continuous •Dual Axis / Multiple Measures •Combo Charts with different mark types •Geographic Map Page Trails •Heat Map •Density Chart •Scatter Plots •Pie Charts and Bar Charts •Small Multiples •Working with aggregate versus disaggregate data

DSP projects at matlab project centres in Chennai Quad Quad is the best project centres in Chennai for matlab based digital signal processing. Here is an idea about digital signal processing technology to develop your final year project work. Digital signal processing (DSP) is the mathematical manipulation of an information signal to modify or improve it in some way. It is characterized by the representation of discrete time, discrete frequency, or other discrete domain signals by a sequence of numbers or symbols and the processing of these signals. The goal of DSP is usually to measure, filter and/or compress continuous real-world analog signals. The first step is usually to convert the signal from an analog to a digital form, by sampling and then digitizing it using an analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers. However, often, the required output signal is another analog output signal, which requires a digital-to-analog converter (DAC). Even if this process is more complex than analog processing and has a discrete value range, the application of computational power to digital signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression. Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc. DSP algorithms have long been run on standard computers, as well as on specialized processors called digital signal processor and on purpose-built hardware such as application-specific integrated circuit (ASICs). Today there are additional technologies used for digital signal processing including more powerful general purpose microprocessors, field-programmable gate arrays(FPGAs), digital signal controllers mostly industrial application such as motor control and stream processors, among others. In DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information, that is the frequency spectrum. Autocorrelation is defined as the cross-correlation of the signal with itself over varying intervals of time or space. Application The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, RADAR, SONAR, digital communication. Seismology and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, hi-fi loudspeaker and equalization, and audio effects for use with electric guitar amplifiers. Visit Quad with your paper to be implemented in matlab. We assist you with our best of knowledge to make you score more marks. We also deal with DSP projects DIP Projects Power Systems Projects Power Electronics Projects RF Antenna Projects Instrumentation project Civil related matlab projects Control Systems Projects, etc,… Experts form technologies like Java, J2ee, Dot Net, Mat Lab, NS2, VLSI, Embedded, Robotics, Android, PHP, Oracle, and Civil will be developing your projects. Ganga Quad Softwares, Old No.46/1, New No.122, Pillayar Koil Street, Pandurangan Colony, Vadapalani, Chennai-. Landmark: Near Vadapalani Murugan Temple Mobile No: Phone No: 044-

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