” Is Tableau For Data Scientists? “
“Tableau” is designed for “Data Science!” Move beyond the basics and dive deeper into the power of this Data visualization software. Learn how to deal with messy or badly formatted data, using Tableau to answer key data analytics questions and visualize your results with maps and dashboards.
Tableau-certified “Zen Master” Matt Francis will show you how to use parameters to enhance visualizations, create cross-source filters, use data extracts to optimize slow connections, and much more.
The training starts with one of the most important features in Tableau: the difference between the green and blue pills (discrete and continuous data) and how this affects every single action Tableau performs. Then find out how to add new maps and create more effective dashboards that maximize screen real estate.
Discover how actions can link together sheets and provide greater levels of interactivity, performance and how formatting can make an ordinary dashboard demand attention. Plus, get some bonus tips on performing date and time calculations in Tableau.
Tableau with below courses deep-dives into the practical, applicable and essential skills that anyone doing data visualization and analytics in a professional setting needs to have.
- Green vs. blue pills
- Using filters, colors, and dates
- Connecting to data
- Extracting data
- Cleaning and prepping data
- Pivoting data
- Merging and joining data
- Highlighting data
- Using the Analytics pane
- Creating new maps
- Creating calculations based on parameters
- Designing dashboards
Role of tableau in Data Science :
” Is Tableau For Data Scientists? ”
Tableau build a tool that non-programmers could use and which could pull data in from even the largest and most diverse sources. Tableau Data Extracts was designed to work out of the box with more than fifty different data sources.
The real value to the product wasn’t the connections, but the interactive visualizations. All the numbers coming out of those data sources were morphed into representations any user could understand and work with directly.
On the surface it might seem like Tableau had turned data science into something that required less in the way of specialized expertise. However, stripping useful information from ever more intricately interrelated sets of data would continue to challenge data scientists and Tableau would become another tool in their growing arsenal.
There is a strain of individual found in every profession who seems to insist that you’re not a real professional unless you do it the hard way for instance, generating all your bar charts by hand-crafting Python code with pandas, or hacking out an R program to put together a scatter plot.
But business executives don’t care how hard you work as long as the data is accurate and delivered quickly. And in many cases, Tableau solutions provide exactly that without all the sweat and late-night coding marathons. It’s easy to explore underlying data and get a feel for it, or whip together quick answers to basic questions in a format that will be readily understood by laypersons.
The VizQL language that powers the dashboards makes it easier to explore data on the fly versus designing a Python or R program for a specific goal. A quirk surfacing in the data can be immediately viewed and pursued with Tableau.
Like any specialized tool, it has limitations. Tableau excels at visualization, but not necessarily at the hardcore data analysis and statistical work that are the bread and butter of data scientists. For amateurs, Tableau may be their only option for looking at information.
For a true data scientist, it’s just one tool in a larger toolbox. But the key to being a real professional knows when to reach for the right tool for the job at hand you don’t take a chainsaw to a twig.
Most data scientists will appreciate the horsepower of Tableau Desktop paired with the Online or Server versions of the software. They’ll also be happy to find that, under the hood, Tableau can easily be integrated with raw R code, which can be used to power up Tableau’s interactive dashboard displays in ways that are difficult or impossible with that tool by itself
It also works seamlessly with Hadoop and other sources of large, unstructured data that otherwise present a significant challenge to data scientists. Progressive Insurance, for instance, uses Tableau’s Hadoop integration to quickly sample subsets of the larger data store to model data that may reveal new insights or lead to further analysis with other tools.