Category Archive: Tableau BI Certification

The Alliance between MongoDB and Tableau Makes Visual Analysis Easier

The Alliance between MongoDB and Tableau Makes Visual Analysis Easier
 

After a volley of speculations, in 2015 the BIG revelation was made – MongoDB, the database for mammoth ideas has partnered with Tableau, the master in visual analytics to make visual analysis of rich JSON-like data structures easier directly in MongoDB. This is a fascinating telltale about a leader in modern databases for robust application development teaming with a leader in rapid-fire visual analytics to serve users’ better.

 

 

Recently, the two global tech players are again in the news – Tableau certified MongoDB’s connector for BI as a “named” connector, which means users for the first time can visually analyze rich JSON-like data structures incorporated with modern applications directly in MongoDB Enterprise Advanced. “Data is a modern software team’s greatest asset, so it needs to be easy for them to both store and visualize it in performant, flexible and scalable ways,” said Eliot Horowitz, CTO, MongoDB. He further added, “With Tableau’s certification of the MongoDB Connector for BI, executives, business analysts and data scientists can benefit from both the engineering and operational advantages of MongoDB, and the insights that Tableau’s powerful and intuitive BI platform make possible.”

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Maps in Tableau: Key to Answer Data Questions

For creating brilliant data visualization, first you need to know which visual chart type would be ideal for the data story you want to tell. In this post, we will explore maps in Tableau, when and where they seem to be appropriate for particular data visualization, and how to make them more productive. If you want to use a map, make sure you know the reason why.

 
Maps in Tableau: Key to Answer Data Questions
 

Maps help you attain, authenticate, or communicate spatial patterns with data. With these maps, you should start your presentation with a spatial question. This spatial question ensures that your map will perfectly find you an answer in the best way possible.

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Let’s Make Visualizations Better In Python with Matplotlib

Learn the basics of effective graphic designing and create pretty-looking plots, using matplotlib. In fact, not only matplotlib, I will try to give meaningful insights about R/ggplot2, Matlab, Excel, and any other graphing tool you use, that will help you grasp the concepts of graphic designing better.

 
Let’s Make Visualizations Better In Python with Matplotlib

Simplicity is the ultimate sophistication

To begin with, make sure you remember– less is more, when it is about plotting. Neophyte graphic designers sometimes think that by adding a visually appealing semi-related picture on the background of data visualization, they will make the presentation look better but eventually they are wrong. If not this, then they may also fall prey to less-influential graphic designing flaws, like using a little more of chartjunk.

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Making Data Visualizations Smarter, Tableau Explains How

Making Data Visualizations Smarter, Tableau explains How

Appalling, bewildering and utterly nonsensical – data at times can look incomprehensible, especially in its raw forms. This accelerated the foundation of the data visualization company and our very own ‘business dashboard’ tool. Generally found locked within the so-called BI sphere, we can now consider these top notch graphical tools as a powerful medium of assimilating, categorizing, analyzing and then presenting data in a highly interactive and interesting form, using images and charts.

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What Makes Tableau 10 So Desirable Among the IT Nerds

Your data just got better, say thanks to Tableau 10. The big data geeks have worked on both the beauty and brains of Tableau, to make the analyses easier, faster and exceptionally delightful. Tableau 10 features an exciting new look and feel, loaded with cute fonts and beautiful colours to make your viz sparkle.

 
What Makes Tableau 10 So Desirable Among the IT Nerds
 

We are thrilled. We have been waiting for days to gauge how some of the Tableau 10 new features will synchronise with our conventional business interface for Hadoop. We have culled out some of the best features of Tableau 10, which had made us zealous and left us buzzing. And here they are ..

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Demystifying Tableau Jargons: Interact With Data like Never Before

Demystifying Tableau Jargons: Interact With Data like Never Before
 

Businesses are flourishing. Managerial data are in abundance. The need for efficient BI softwares is at the pinnacle. Structured BI softwares are nimble and up to the minute. Tableau is one such BI tool, which is not only simple and comprehensible, but also extremely purposeful, enough to fulfil high-end professional commitments. It works just the way you want it to, instruct it in a particular way and wait for the results, without compromising the security of various confidential data.  

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Power BI or Tableau? Which is Better and Why?

In the present data frenzy setting, data visualization is the new Talk of the Town. Various companies are developing and launching their own data visualization tools in the market. For quite some time now, Tableau has been the pioneering data visualization platform and till date the best to consider. Tableau’s data visualization tool is unbeatable to any other emerging product in the digital community. 

 

Power-BI-or-Tableau--Which-is-Better--and-Why

 

Apparently, Tableau has a remarkable competitor, recently. It is the Power BI, a decisive and dynamic BI tool, brought into by Microsoft. It is catching the trend with Tableau fast and appears to be on its way to become the number one BI tool in the digital market.

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ETL and ETL Testing: The Concepts Explained

In this New Age, businesses pay maximum attention in collecting customer and transactional data. Businesses with draconian financial reporting and persistent audit requirements look up to ETL, as it offer an organized and integrated solution instead of relying on other apparent solutions like Hadoop.

 

 

ETL and ETL Testing: A Detailed Evaluation

 From Visually.
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Battle the Blank Tableau Canvas Blues with These Nifty Tips

Battle the Blank Tableau Canvas Blues with These Nifty Tips
 

Do you experience vizzer’s block? Do you feel paralyzed by choices? Do you stare at the blank Tableau canvas, wondering from where to start your viz?

 

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Though brimming with stories to tell, you are stuck at the get-go. Fortunately, here are a few tricks to help you get over the blank-canvas woes and get yourself rolling.

Draw your mind out

Doodling does help! Draw, doodle or sketch, just kick-start your cognitive thinking abilities. The scribbles don’t need to be pretty or legible, but they have to spur the creative process. So, grab a paper and pen, and start brainstorming.

 

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And, you don’t have to go it alone. From academic researchers and lifestyle bloggers to professional visual consultants, the entire world is drawing.

Get inspired by ace visualizers

People inspire you, or they drain you – pick them wisely. Keep the right people by your side, they will lift you up and get the better out of you. Be in association with hotshot vizzes, follow maven data journalists and data vizzers, jot down notes and read data-viz pdfs.

 

 ALWAYS, keep your eyes open to stumble across fetching viz, whose idea might work out well for you!

For example, this visualization by Washington Post tells a gripping food-survey story.

 

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Develop a formidable structure to understand the data better

Frazzled about starting your viz? If affirmative, then this checklist can save your day. It is segregated into two parts – data preparation and data exploration.

Taking the first one first, i.e. Data Preparation:

 

As boring as it sounds, physical inspection of your data sometimes helps you comprehend the data set’s possibilities and challenges. To draw a clearer picture, here are few things to look into a data set:

 

  • The kind of data in each field
  • The pattern of data structure and format
  • Fields covered and not covered by the data set
  • Highest and lowest values in each field
  • Are there fields that contain null values

 

If you follow the above example, you will find there are multiple levels of data infused in the food-survey data set – where some food items boasts of four sub-categories, while others has only two. Situations like this make it hard to establish a comparison between two food items unless you know that they are at their minimum sub-category.

 

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Coming to the second one, Data Analysis:

 
Analyse a data set just like interviewing it. Whenever you feel like going blank by staring at a Tableau canvas, start grilling yourself about data. Do it in a traditional interview way and you are sorted.
 

  • What, how, who, why, when, and where – Evaluate each field and ponder how to apply these questions on each field.
  • Let your inner child smile, while you ask “Why? Why? Why?” to your data.

 

To pop colours on your blank-canvas, interviewing is indispensable.

 

Remember: Every end is a new beginning

What if my final viz fails to shed light upon the deepest cognizance? Or, how will I feel if my viz cannot do justice to my story. Don’t worry, pondering is common. Get up and hit the road. There are countless number of ways to address a viz and remember that once you finish a viz, it doesn’t mean an end. Remaking and telling stories in newer and innovative ways are something you can always look up to anytime.

 

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Turn the volume up and focus

Crank up the music, boost productivity and tune out distractions! Music helps in focusing on work, by diminishing outside noise (phone buzzing, colleagues chatting, TV blasting). Irrespective of the kind of tunes you like, plug in your headphones and say goodbye to the world!

 

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Recently, tableau bi training courses are gaining a lot of attention. If you are seeking comprehensive tableau certification Pune, scroll through DexLab Analytics.

 



 

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Can We Fight Discrimination With Better Machine Learning?

With the increase in use of machine learning, for taking important corporate as well as national operational decisions, it is important to set across some core social domains. They will work to make sure that these decisions are not biased with discrimination against certain categories whatever they may be applied into.

 
Can We Fight Discrimination With Better Machine Learning?
 

In this post, we will discuss the crucial matters of “threshold classifiers”, a part of some machine learning operations that is critical to the issues of discrimination. With a threshold classifier one can essentially make a yes/no decision, which in turn helps to put things in perspective with one category or the other. Here we will take a look at how these classifiers work, the ways in which they can potentially be biased and how one may be able to turn an unfair classifier into a much fairer one.

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