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.
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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?
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.
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.
And, you don’t have to go it alone. From academic researchers and lifestyle bloggers to professional visual consultants, the entire world is drawing.
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.
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:
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.
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.
To pop colours on your blank-canvas, interviewing is indispensable.
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.
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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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.
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.
By opting for a course on Machine Learning Using Python, you will be able to grasp the subject matter of this topic better.
In order to provide an illustrative example, we will concentrate on loan granting scenarios where the bank may approve or deny a loan based on one single, number computed automatically like a Credit score.
In the above-mentioned diagram, the dark dots represent people who do pay off their loans and debts, while the lighter dots show those who would not. In an ideal scenario, we may get to work with statistics that cleanly distinguish the classes as in the left example. However, sadly this is far more common to see a situation wherein at the right where the group overlaps.
A standalone statistic can stand in for several different variables, and boiling them down to just one number. In case of the credit score, which is evaluated by looking at several numbers of factors, that include income, promptness in debt repayment and much more. The number might even correctly represent the likelihood that a person may pay off a debt or also default, or might not. This relationship is actually pretty blurred and it is rare to find a statistic that correlates perfectly with real-world outcomes.
And that is exactly where the idea of a “threshold classifier” comes in: the bank selects a particular cut-off or threshold, and the people who have their credit scores are mentioned below it, will be denied of loans and people above it are usually granted the lending. However, real banks have several more additional complexities, but this simple model is often useful for studying some of the fundamental issues. Also to be clear, Google does not use credit scores for their products!
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The above-mentioned diagram makes use of synthetic data to show how a threshold classifier works. For further simplification of the explanation, we will be staying away from realistic credit scores or the data what you see shows just the simulated data with a score based on the range of 0 to 100.
As can be well understood, selecting a threshold needs some tradeoffs. Too low and the bank wil l end up giving loans to many people who default; if too high many people who actually do deserve a loan will not get them.
So, how to determine the right threshold? That is subjective. One important goal may be to maximize the number of appropriate decisions. (Can you tell us what threshold will do that in this example scenario?)
Another financial situational goal may be to, maximize profit. At the bottom of the above mentioned diagram, is a readout hypothetical “profit” which is based on the model wherein a successful loan will make USD 300, but a default will cost a bank USD 700. So what will be the most profitable threshold? And does it match the threshold with the maximum correct decisions?
The aspect of how to make a correct decision is defined, and with sensitivities to which factors will become particularly thorny, when a statistic like a credit score ends up distributed separately in between the two teams.
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Drag the black threshold bars either left or right to alter the cut-offs for loans. Click on the varying preset loan strategies:
In the above mentioned case, the distributions of the two groups are slightly varying. While the blue and the orange people are equivalently likely to pay off a debt. But if you take look for a pair of thresholds that maximize total profit (or click on max profit button), then you will be able to see that the blue group is held in a slightly higher standard than the orange one.
An important outcome of the paper by Hardt, Price, and Srebro depicted that – when mentioned essentially in any scoring system, it will be possible to efficiently to find the thresholds that meet any of the above mentioned criteria. Put in other words, even if you do not posses control over the underlying scoring system (which is quite a common case) it will still be possible to attack the issue of discrimination.
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Business Intelligence combined with Big Data analytics is stimulating the progress of Enterprises across the globe, along with pulling whooping amount of investment within the Big Data community. The infographic given below on big data in shaping everyday lives elucidates people about how Big Data is making our lives better.
When Moore’s Law meets with modern day Business Intelligence, what happens? Disruption and then wider adoption!
With costs of implementing BI tools lowering, more and more enterprises are keen on jumping on-board the homebrewed variety of custom BI solution to help drive their business. The result of these efforts is that these days several organizations are pursuing data driven intelligent decision-making, at a cost, which is almost fractional compared to yesteryear’s Business Intelligence budgets.
A proper Big Data certification allows individuals to make the best of available smart BI solutions available out there!
But the question remains, as to are all these companies actually making better decisions?
Surely, most enterprises are now reaping the benefits of having a larger range of BI solutions available to them. Nevertheless, there is still a bigger room for error in the picture, which many firms tend to ignore.
If done right, BI solutions can deliver an ROI of USD 10.66 for the cost of every dollar spent on implementing them. But, as per a survey conducted by Gartner, the results are not so glorious for most firms. More than 70 percent of all BI implementations do not stand up to meet the business goals that were anticipated of them.
Due to the evolution and lowering BI solution prices, the demand for data analytics certification courses have grown by several manifolds.
Is there a secret formula to BI solution driven success? Well, starting with asking the right questions is always a good place to begin:
Do you know what the lifeblood is for BI? Why, data of course, data is what Business Intelligence strives upon. All firms do have a rudimentary strategy to collect and analyze data, however, they tend to overlook the data sources. The key here to note is – truly reliable data sources are the main difference between the success and failure of your Business Intelligence efforts.
These data sources do exist; all you have to do is choose right. In addition, the best thing about them is a lot of them are almost free of charge. Using the good ones will transform the way you look at your market, the business pipeline and the way you perceive your audience.
These are your firm’s single source data repositories. Warehouses store all the data you collect from various sources, and provide the same for when needed, on prompt for reporting and analysis. However, self-service BI tools can be a bit of hit-or-miss at times, where consistently handling data is a worry.
The key is to discover a data warehouse solution, which can efficiently store, curate and retrieve data for analysis on prompt.
Companies that are looking to use their own Business Intelligence infrastructures must identify the analytics architecture that best suits their necessities. However, unwieldy datasets in combination with a lack of processing maturity can dull the effort even before one decides to start!
For incorporating enterprise-scale Business Intelligence solutions, it is necessary to have it work effortlessly with the different other information formats, processes and systems, which have already been established previously in the internal work pipeline.
So, the key here is to ask the question – will the necessary integration cost more in terms of resources and effort that you can afford?
The most persistent challenge in BI is to wrangle data, majority of users cannot understand any of it beyond a simplified visualization. Decision-makers may be fooled with the help of powerful visualization tools. However, the truth is that making it pretty alone will not get the job done right.
So, forget pretty, and ask the all important question of whether the reporting mechanism is useful in interpreting otherwise unintelligible data or not.
If your BI solutions, directly impinges on relevant regulations (and so it will, when the time comes). Then the solutions should aid the compliance and not hinder it. A good BI solution should provide a means to trace and audit data and its sources wherever, needed.
The field of data science is evolving in expertise. And even professionals involved in the field tend to vary in their capabilities and opinions about the same. So, the important thing is to consider the importance of data in your company, and that one has all the appropriate responses to the posed questions above.
You can learn to ask the right questions with comprehensive tableau BI training courses. For more information on tableau course details feel free to contact the experts at DexLab Analytics.
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Let us not waste any time on reading long-worded introductions, and get right down to our 5 top tips for creating different map styles in Tableau.
You will be able to find out about these tips and a lot more on Tableau with our Tableau training courses.
A simple way to create a clean map for best visualization is by removing the map layers. In order to do this, all you need to do is Select Map on the toolbar menu and “map layer”. Then simply click on the uncheck box and everything in the map layer window, this will leave behind only the outline of the map data.
Image Source: dataplusscience.com
Tableau course details has all these steps and much more to learn about the software, you can know the details by visiting our Tableau training institute website.
When you use map, you can change the border colours of the map under the option ‘Color’. Simply select the colour and choose the border colour that you desire.
Image Source: dataplusscience.com
Combining the above mentioned step 1 and 2 and by clicking to remove the fill colour, we can create an outline of a map. To do so, first remove the map layer shown in step 1 and then set the map fill colour to match with the background colour. And voila! After selecting the desirable colour for the borders (as shown in step 2) we now have created just a simple outline of the map of USA!
Image Source: dataplusscience.com
To do this in Tableau, you must remove the base layer which is similar as shown in the first step, we can then alter the Pane colour to change the colour of the water on the map to any colour we want. Once we get the base layer to be unchecked, all we have to do is simply right-click on the map and select ‘Format’ then select “Shading” and then alter the “Pane” colour to any shade of our choice.
Just like the previously discussed step (step 3) we can start by making the borders of the countries. In this case we have also combined the step 4 where the pane colour has been changed to match. This has given rise to a red coloured outline of the countries of the world on a pitch-black background.
Image Source: dataplusscience.com
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Let us assume you have got sales data in your hands, wouldn’t it be absolutely grand to be able to recognize certain customer groups to be able to develop targeted marketing programs? It could also be that you are in healthcare and you’d love to identify patients who have had similar symptoms to determine the effectiveness of treatments.
With complex data these questions can be very difficult to answer, even when people have distinct and well-separated groups. Additionally, sometimes data is distributed in such a way that it contains no obvious gaps which can help us distinguish between groups with simple inspection. Continue reading “Now Easily Uncover Patterns in Your Data With Tableau 10’s Clustering Feature”
If you are looking to be hired as Tableau expert then you must be acquainted with these common interview questions and answers. These questions have been collected by the experts at DexLab Analytics who offer Tableau BI Certification training at the institute. These questions are to give you an idea of the types of questions you may be asked at an interview. Happy job hunting!
Data visualization is a much advanced, precise and ordered way of viewing large volumes of data. It is the way one visually represents data into graphs, charts and other illustrative aids, especially when you cannot define them textually. Through the various software applications like Tableau one can show various trends, patterns, correlations etc.
In Tableau desktop one can create workbooks for data visualizations but Tableau Servers are used to distribute the interactive workbooks or/and reports to the target audience. Users of Tableau servers can also edit and update workbooks and dashboards online on the Server but they cannot create new workbooks.
But there are limited options for editing in server as compared to desktop.
The differences in these two features actually lie in their applications. With parameters one can allow users to insert their values, which can be integers, dates, floats, string these can be used for calculation purposes. But in filters one can only receive values which the user chooses to ‘filter by’ in the list, this cannot be used to perform calculations.
In them users can change the measures and dimensions in case of parameters but for filters this feature is not approved.
Are you interested in a Tableau training course we can help you get a head start in this much coveted career. Simply view our course details at Dexlab Analytics.
Are You Trying to Ace Your Tableau Interview? from Infographics
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Tableau helps data analysts to visually analyze your big data and is a great tool that helps to solve complex problems associated with data analytics. By data visualization we mean, describing information through visual rendering to gather immediate insight with the mind’s powerful and quick visual processing system.
Earlier Tableau used to offer certification for version 8 both Desktop and Server. But now Tableau has released version 9 certification exams. Thus, now you can no longer register for the version 8. If you are already certified in version 8 of Tableau and would now like to upgrade yourself, you can sit for the 9th version delta examination with the new features only. Continue reading “Analysts’ Guide to Tableau Certification”
