Best Tableau Training in Melbourne with Tableau Certification

1. Tableau Desktop architecture
2. Working with Tableau graphs, maps and reports
3. Creating parameters and deploying Tableau generated field
4. Formatting data, building views and creating visuals
5. Deploying real-time analytics with Tableau
6. Integrating R with Tableau
7. Data aggregation and blending with Tableau

• Data Scientists, Business Intelligence Professionals and Testing Professionals
• Statisticians, Business Analysts and Project Managers
• Data Visualization Analysts and Functional Experts
• Graduates and Professionals looking to move into the analytics domain

There are no prerequisites for taking up this Tableau certification training course.

Melbourne is a major commercial hub of Australia that is home to a diverse range of business enterprises including manufacturing, IT, finance, tourism, transportation, logistics and media. Due to this, there is increased need for Business Intelligence professionals who can extract value from huge amounts of data. Thus, Tableau professionals can command high salaries in this city.

The Tableau market in Melbourne is really taking off, thanks to this city growing at a rapid rate. Due to this, Tableau professionals can take comfort in the fact that they can have a vast choice of organizations to work for at top salaries.

• Worldwide Business Intelligence and Analytics market to grow to US$22.8 billion in the next two years – Gartner
• Tableau is in the Leaders quadrant in the Gartner Magic Quadrant for BI for the fourth year – Gartner
• Average Tableau salaries are 77 percent higher than those for all other job profiles – Indeed

Tableau is the top Business Intelligence tool today, and this has been confirmed by the research firm Gartner. The Intellipaat training in Tableau will help you be up and running in Tableau Desktop for business intelligence and data visualization helping you grab some of the top-paying jobs in the Business Intelligence domain.

What is data visualization?, comparison and benefits against reading raw numbers, real use cases from various business domains, some quick and powerful examples using Tableau without going into the technical details of Tableau, installing Tableau, Tableau interface, connecting to DataSource, Tableau data types, and data preparation.

Installation of Tableau Desktop, architecture of Tableau, interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.) how to start with Tableau, and the ways to share and export the work done in Tableau.

Hands-on Exercise: Play with Tableau desktop, learn about the interface, and share and export existing works.

Connection to Excel, cubes and PDFs, management of metadata and extracts, data preparation, Joins (Left, Right, Inner, and Outer) and Union, dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract , etc.

Hands-on Exercise: Connect to Excel sheet to import data, use metadata and extracts, manage NULL values, clean up data before using, perform the join techniques, execute data blending from multiple sources , etc.

Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies), constant sets, computed sets, bins, etc.

Hands-on Exercise: Use marks to create and edit sets, highlight the desired items, make groups, apply sorting on results, and make hierarchies among the created sets.

Filters (addition and removal), filtering continuous dates, dimensions, and measures, interactive filters, marks card, hierarchies, how to create folders in Tableau, sorting in Tableau, types of sorting, filtering in Tableau, types of filters, filtering the order of operations, etc.

Hands-on Exercise: Use the data set by date/dimensions/measures to add filter, use interactive filter to view the data, customize/remove filters to view the result, etc.

Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste; formatting data using labels and tooltips, edit axes and annotations, k-means cluster analysis, trend and reference lines, visual analytics in Tableau, forecasting, confidence interval, reference lines, and bands.

Hands-on Exercise: Apply labels and tooltips to graphs, annotations, edit axes’ attributes, set the reference line, and perform k-means cluster analysis on the given dataset.

Working on coordinate points, plotting longitude and latitude, editing unrecognized locations, customizing geocoding, polygon maps, WMS: web mapping services, working on the background image, including add image, plotting points on images and generating coordinates from them; map visualization, custom territories, map box, WMS map; how to create map projects in Tableau, creating dual axes maps, and editing locations.

Hands-on Exercise: Plot longitude and latitude on a geo map, edit locations on the geo map, custom geocoding, use images of the map and plot points, find coordinates, create a polygon map, and use WMS.

Calculation syntax and functions in Tableau, various types of calculations, including Table, String, Date, Aggregate, Logic, and Number; LOD expressions, including concept and syntax; aggregation and replication with LOD expressions, nested LOD expressions; levels of details: fixed level, lower level, and higher level;  quick table calculations, the creation of calculated fields, predefined calculations, and how to validate.

Creating parameters, parameters in calculations, using parameters with filters, column selection parameters, chart selection parameters, how to use parameters in the filter session, how to use parameters in calculated fields, how to use parameters in reference line, etc.

Hands-on Exercise: Creating new parameters to apply on a filter, passing parameters to filters to select columns, passing parameters to filters to select charts, etc.

Dual axes graphs, histograms: single and dual axes; box plot; charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts; maps: tree and heat maps; market basket analysis (MBA), using Show me; and text table and highlighted table.

Hands-on Exercise: Plot a histogram, tree map, heat map, funnel chart, and more using the given dataset and also perform market basket analysis (MBA) on the same dataset.

Building and formatting a dashboard using size, objects, views, filters, and legends; best practices for making creative as well as interactive dashboards using the actions; creating stories, including the intro of story points; creating as well as updating the story points, adding catchy visuals in stories, adding annotations with descriptions; dashboards and stories: what is dashboard?, highlight actions, URL actions, and filter actions, selecting and clearing values, best practices to create dashboards, dashboard examples; using Tableau workspace and Tableau interface; learning about Tableau joins, types of joins; Tableau field types, saving as well as publishing data source, live vs extract connection, and various file types.

Hands-on Exercise: Create a Tableau dashboard view, include legends, objects, and filters, make the dashboard interactive, and use visual effects, annotations, and description s to create and edit a story.

Introduction to Tableau Prep, how Tableau Prep helps quickly combine join, shape, and clean data for analysis, creation of smart examples with Tableau Prep, getting deeper insights into the data with great visual experience, making data preparation simpler and accessible, integrating Tableau Prep with Tableau analytical workflow, and understanding the seamless process from data preparation to analysis with Tableau Prep.

Introduction to R language, applications and use cases of R, deploying R on the Tableau platform, learning R functions in Tableau, and the integration of Tableau with Hadoop.

Hands-on Exercise: Deploy R on Tableau, create a line graph using R interface, and also connect Tableau with Hadoop to extract data.

Project 1: Working with Tableau Interactive Dashboard

Domain: Sales

Problem Statement: How to make an interactive dashboard with Tableau?

Description:

Upon the completion of this project, you will understand how to create a single point of access for all your sales data, ways of dissecting and analyzing sales from multiple angles, coming up with a sales strategy for improved business revenues.

Project 2: Tableau for Crime Statistics Analysis

Domain: Crime Statistics (Public Domain)

Problem Statement: Showing the types of crimes and their frequency in the District of Columbia and providing the details of the crimes

Description: Police authorities are often called on to “put more feet on the street” to prevent crime and ensure order. However, due to limited resources, it is almost impossible to use them anytime, anywhere. In this project, you will work on the crime data, in the District of Columbia and analyze it using the Tableau tool. During visualization, you can use ‘crime categories’ and ‘days of the week’ as data types to see when and where crimes have occurred. In this project, you will also analyze the details of the crimes, such as, the area/location and the day of the week it has happened, etc. This project will help the local police get insightful information on where to put their crime prevention efforts on.

Highlights:

• A map should be plotted at the block site address level
• Shows the offense, the location, and the date of the crime
• Shows the frequency (in %) for each type of crimes
• Shows the crime details of every month by week/weekday/offense type (the dashboard should have various filters that will be applicable to all three sheets in the dashboard)
• An action from map should filter out the other two sheets, accordingly
• An action from the tree map and the bar chart should highlight the remaining two sheets according to the selection

Project 3: Analyzing Economic Data

Domain: Government

Problem Statement: How is unemployment affecting global malnutrition?

Description: In this Tableau project, you will be working on vast amounts of data and analyzing it to come up with trends, insights, and correlations. Datasets include the global unemployment figures for multiple years, world population statistics across several years, and the worldwide nutritional data. By analyzing this data, you will correlate the malnutrition problem with the unemployment rates using Tableau.

Highlights:

• Cleaning up Excel data and connecting with Tableau
• Using data blending and pivot tables
• Comparative analysis with the Tableau dashboard

Project 4: Analyzing Market Performance

Domain: Retail

Problem Statement: Using the Consumer Packaged Goods data to analyze which are the markets that are performing well for a particular retail enterprise using Tableau Desktop

Description: This Tableau Desktop project involves working with the complex Consumer Packaged Goods data to come up with the brand performance analysis, regions that are contributing good to the revenues, where there is a need to offer more discounts to spur sales, and making in-depth budget vs spend analysis for any particular year.

Highlights:

• Combining data sources, adding filters, and drilling down data
• Building an interactive dashboard and reports for detailed analysis
• Deriving real-time visualization of data for business insights