Common birthdays

US-Birthdays

This visualisation shows the popularity of birthdays in the US between 1973 – 1999. The darkness of the colour shows the rank of how popular that birthday is. Dark colours are more popular (i.e. better ranked) birthdays.

  • Most people are born in August & September (and therefore were conceived around November & December, during the holidays, perhaps?)
  • However, very few people are actually born during holidays – New year, Independence day, Halloween, Thanksgiving and Christmas. (People don’t like to spoil their holidays?)
  • Few people are born on the 1st of April. (You don’t want your kid born on Fool’s Day)
  • Few people are born on the 13th of any month. (Unlucky?)
  • Plenty are born on Valentine’s Day and St Patrick’s day

We tried to see what this looked like in India.

Based on school registration data for ~700,000 students born between 1992 – 1995, here’s what it looks like. (Click for a larger version.)

IN-Birthdays

This shows a number of bizarre patterns:

  • Almost everyone’s born between May and June – just before the school opens.
  • Almost no one is born in August – after school opens.
  • An unusual number of people have round-numbered days as birthdays – 5th, 10th, 15th, 20th, 25, and 30th. (This round-numbered pattern was also seen when we analysed utility fraud).
  • January 1st is fairly popular. Other than that, none of the holidays seem to have an effect.

In fact, these results are so striking that we are tempted to believe that the popularly accepted proof for a person’s age – their Class 10 certificate – generally bears a convenient fiction created for the purposes of school admission several years ago.

Data science news

Big Data Is An Issue Of Corporate Survival

“It is imperative from the business standpoint that you need to get ahead of this new wave of interacting with customers. You need to know who that customer is, what they represent to the business now, what they should represent to the business and how to move them along the trajectory to be that great customer they should be.”

Annika Jiminez, senior director for analytics solutions at Greenplum, said big data is happening in nearly every sector of business and government, from health care where it is used in medical records and treatment pathways to car manufacturers using it to capture data on how vehicles are used and transmitting it to a data center.

‘Big Data’ Could Remake Science — And Government

The research firm Gartner predicted in December 2011 that 85 percent of Fortune 500 firms will be unprepared to leverage big data for a competitive advantage by 2015.
Big-data analytics also has the potential to improve government efficiency, panelists at the TechAmerica event said.

The Centers for Medicare and Medicaid Services, for example, could pull data from insurance reports and hospital forms and anonymized data from electronic medical records to get a much better understanding of which medications and procedures are most effective, said Caron Kogan, a strategic planning director at Lockheed Martin Corp.

Visualization Broadens Business Intelligence’s Appeal

Some 400 IT and business unit managers responding to a survey found advanced analytics, which Dresner Advisory Services founder Howard Dresner defines as “extensive use of color, size, shape, 3D, texture, motion, etc. to convey meaning,” more compelling than Big Data, the cloud, social media analytics and other trendy business intelligence technologies.

On a rising scale of importance, from one to five, respondents gave advanced visualization a 3.8. Dashboards, respondents’ top priority, rated only slightly higher at 4.15

Predictive Analytics Goes Deep, Catches Pass From Tech Giant IBM

Beyond the simple data analysis of standard business intelligence (BI) software, predictive analytics solutions give midsize IT the ability to not just crunch numbers but get a glimpse of what the future may hold–this is an invaluable asset in the quickly changing tech market. Adoption of predictive software services has been slow in the world of IT, but it is now getting noticed both at the enterprise investor level and on the gridiron.

Where Big Data Shows Huge ROI

Big data projects can far surpass the hype by nurturing context and connections, according to an analysis of numerous case studies by Nucleus Research.
Examples of those returns included: a 942 percent ROI for a manufacturer that was able to scour large, disparate data sets from vendors for purchasing and cost information; 1,822 percent ROI from reduced labor costs by a resort that integrated shift scheduling processes with data from the National Weather Service; and an 863 percent ROI by a metropolitan police force that was able to combine various crime databases alongside predictive analytics and its department assets.

How visualisation uncovers the big picture of ‘Big Data’

According to Gartner, Big Data is “…the volume, variety and velocity of structured and unstructured data pouring through networks into processors and storage devices, along with the conversion of such data into business advice for enterprises.” A recent report from the Center for Economics and Business Research (CEBR) 1, suggests that improved use of this Big Data could add £216 billion to the UK economy and create 58,000 jobs. Data visualisation can be a key tool in helping users explore and communicate data through graphic representations – enabling collaborating, inferring connections and drawing conclusions that benefit business’ bottom line.


Data Visualization in Analytics

From http://analyticstraining.com/2012/data-visualization-in-analytics/

Data Visualization in Analytics

Gaurav Vohra

Data visualization  is one of the most under-rated aspects of analytics. No doubt there is some awareness of the importance of visualization in the field of analytics (Proponents of R rave about its data visualization capabilities and it is seen as a big reason why many analysts prefer it over some other tools). Yet the emphasis is nowhere near as much as it should be.

Visualization is the best way to summarize, understand and analyse data. Good visualization will help you easily identify patterns, problems, and outliers in the data. For most businesses, this is the most logical first step towards leveraging the available data. You don’t need expensive IT systems or complex predictive models at the start. What you need is to be able to summarize and understand what is happening with your business.

This is where Gramener, an exciting start-up with a unique approach towards analytics, comes in. They create great visualizations that will give you an intuitive understanding of your business and the problems and opportunities hidden deep inside your data.

Gramener’s visualization-driven approach to analytics is different from that of most other analytics service providers. It has a lot of advantages over the traditional statistical modelling approach.

For starters, it is a lot easier to explain to a client. Analysts have always struggled with this part. Building a neural network model or even a complex regression model is usually a lot easier than explaining the results of the same model to a client. And it becomes even harder when your insights vary from the client’s beliefs.

As an analyst, I often think of data as a living being. A living being that sees everything, knows everything. The only problem is that this being talks in a different language. It talks in the language of mathematics. The analyst is essentially an interpreter. Her job is to listen to what the data is saying, understand it and then interpret it for the client. The problem here is that the client has no way to verify how accurately the analyst is interpreting what the data is saying. He just has to take the analyst’s word for it.

Gramener’s visualization-based approach will effectively provide a translator to the client – he can directly hear and understand what the data is saying. He no longer needs to just take the analyst’s word for it. It is all right there, in front of him, in the form of visualizations.

Indian businesses are a sceptical lot. It is very hard to convince them to trust a statistical model that they have no understanding of, over their gut and intuition. Gramener’s approach will work well in this market. It will convert sceptics into believers.

Here is an example of some of the great visualizations Gramener has created.

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