Fresh and Frozen Fruit consumption – U.S. Bureau of Labor Statistics

The south and the West consume highest amount of Fruits.

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Here is more individual breakdown by Quarterly expenditure on Fruits (figures in 100 million)

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Visualization on How the undergraduate tuition has increased over the years

Average undergraduate tuition and fees and room and board rates

Source: http://nces.ed.gov/

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These figures are inflation adjusted and look how just the tuition fees have increased compared to the Dorm and Board rates

Now comparing the rate increase for 2-year program

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So for the 2 year program, the board rates have remained at the same level compared to the dorm rates.

Now check out the interesting graph for 4 year program below

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Comparing the slope of 2 year Board rates to the 4 year Board rates, the 4 year has significant increase

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If price of meals is same for both programs then both 4 year and 2 year programs should have the same slope. So why is the 4 year slope different than 2 year?

Now, let see about the Dorm rates

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And finally the 4 year vs 2 year Tuition rates

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Here is the data table for the above visualization

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50 years of killing Deer – Data visualization and analysis

Virginia maintains the summary of Deer kills way back from 1947

The stack bar gives a total view of the killings and how it has grown over the years

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By comparing the killings on a line chart we see that the female Deer killings has an uptick from 2008 onwards

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USA War Casualties

iCasualties.org maintains documented list of all fatalities for Iraq and Afghanistan wars.

Analysing the dataset for Afghanistan, we summarize the results by the year

NOTE: This contains only Afghanistan metrics. We will later update the visuals to reflect Iraq war.

 

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USA war fatalities by year

We are approaching the levels of 2002 and hope for the best that we don’t have to suffer another wars.

Here is another view by year and month

 

 

The dataset contains the age of each person died in the war so summarizing by Age

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War Deaths by Age

Checking it against the year

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Why so many young deaths between age 20 and 30 for the year 2014?

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Where did most of the deaths occur?

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Where were the soldiers from?

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Deaths by Rank

 

 

Cause of Death

Attack Types

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Helicopter Crash is the one of the top death cause in Non Hostile situations

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github DMCA takedown notices in the rise!

Categories: Other

GitHub maintains a list of all DMCA takedown notices along with counteractions and retractions if any.

Analysing all the notices from 2011, it seems that the takedown notices are on the rise.

Year View : Notice the sharp increase in 2014

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Quarterly view : Now looking at the quarterly breakup, seems like the takedowns are cooling off in the later quarters.

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So who is issuing these DMCA takedowns?

Here is the complete list of all companies who issued DMCA takedowns

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NOTE: The names were extracted from the description text

And here are the counteractions and retractions

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See the full list of companies with notice type

So the important question is “Why the DMCA takedown notices have increased?”

One important thing to note is sites like Stackoverflow encourage to replicate the content of the web page from where the original idea/algorithm or source code is copied from. To be honest it is a good thing because lot of times these referring sites become zombies and you don’t want to lose this knowledge. But could it be the case that such non-referenceable source codes end up in GitHub and hence causing the increase in the takedown notices as companies start discovering them?

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Fastest growing and rapidly declining job industry

Data source : http://www.bls.gov/emp/tables.htm#occtables

 

Fastest growing job industry

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Original Visualization

Most rapidly declining job industry

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Rapidly declining jobs link

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Top 100 analytics companies ranked and scored by Mattermark

Let us move on from Grass Eating Sauropods and talk about who’s who in the analytic space.

For every dime there are dozen analytic companies. Everybody who provides a freaking dashboard is an analytic company. Anybody that merely mentions Google, Facebook, Hadoop etc in the same sentence is somehow into BigData. Haven’t you stumbled across company pages where they claim to be expert in analytics and big data but they want you to schedule a call with them. They don’t have any products or solutions to show case yet they are Big Data/analytics folks.

So to make things easy, Mattermark released this highly curated list of 100 analytic companies. No offense to BigData, but small datasets like these are always juicy.

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Mattermakr ranks each company using their own algorithm and calls it “Mattermark Score”. After loading it up, we came up with these visualizations

 

 

 

For each funding stage, it shows the listing of companies by Mattermark score.

Some interesting questions

1. How many companies by funding stage?

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2. What is the funding by location and stage?

 

 

Another interesting visual by plotting the score against the total funding.

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We thought the above visual would tell us what kind of logic did Mattermark used to rank the companies. As suspected, apparently we cannot reverse engineer it without some additional information about the companies.

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Y Combinator companies has more funding than the sum total of all remaining accelerators

After finishing our call with Bed Bugs , we decided to check out what the startup scene looks like. We used the data from seed-db to let our analytical juices flowing.

First we asked what is the top most program (duh!!) but by how much and who are next in the list and so on.

Like most Data scientists who believe in the power of simple bar graphs we used our first “chart weapon” of choice and here it is what it rendered.

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Y Combinator is freaking huge like a dinasaur, infact very much resembles the grass eating Sauropods. In fact we had to create a chart that was 3000 pixels wide just to accommodate all.

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See the resemblance between the chart and the Sauropod?

To get better perspective we rendered it in a Treemap as shown

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Looking at the treemap, Y Combinator occupies more than the sum total of all the remaining accelerators. That is super amazing but the problem our charts were not coming up beautiful. YC is clearly the outlier and was causing us difficulty to understand the remainder startup ecosystem.

We said, lets cut off the head to dig deeper.

The moment we filtered out YC from our analysis, all of the regions became colorful and that was certainly a visual treat.

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Now we could clearly see what are the other accelerators/programs that are roughly the same size.

For example,

TechStars Boulder and AngelPad are roughly the same

TechStars NYC, TechStars Boston and 500Startups are in the same club

Similarly DreamIT, fbFund and Mucker Lab share the same color.

Now let us try to see from the location angle

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So we re-established that YC is freaking huge and having them on a chart with other accelerators does not create beautiful visualizations.

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Bed bugs in Boston – Analysis of Boston 311 public dataset

Digging into the Boston public Dataset can reveal interesting and juicy facts.

Even though there is nothing juicy about Bed bugs but the data about Boston open cases for Bed bugs is quite interesting and worth looking at.

We uploaded the entire 50 mb data dump which is around 500K rows into the Data Visualizer and filtered the category for Bed Bugs. Splitting the date into its date hierarchy components we then plotted the month on the Y axis.

It seems that the City of Boston started collecting this data around 2011 and has only partial data for that year.

Interestingly, the number of Bed bug cases seem to rise during the summer months.

Now if we break the lines into Quarters (we just add the quarter hierarchy to the mix)

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