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on May 11

The Man Behind the Scenes: An Interview with Nate Watson

on April 20

In the next few years, CAN is predicted to be among the nation’s leaders in data science. We have an impressive resume to back this up. We’ve worked with multiple Fortune 500 hundred companies, and many more Fortune 1000 companies all over the globe and have built a solid reputation among local Omahans for producing experts in data science and IT.

A year ago we joined up with Interface Web School and created the Omaha Data Science Academy (Oma-DSA). Our dedication to educating our community — from recent college graduates to single moms looking for a new career — has bolstered our reputation as not only an upstanding professional service but also a down-to-earth, human-to-human, educational source.

We also have an impressive network of CAN alums (Oma-DSA grads and former CAN employees) to prove this educational dedication. When you work for CAN, we don’t just want you to improve our company. We want you to improve yourself. That’s why so many of our graduates and employees have moved on to work for businesses all over the country, and to start their own businesses.

We have AIM and The Start-Up Collaborative to thank for our continued success. As CAN turns a year older, a lot of people are stopping us to ask this question:

 

Will CAN stay in Omaha?

Of course we will.

Does that shock you? To some it might seem like the next logical step for CAN is to move to a bigger city where there are more businesses, take on investment, and find more talent. But’s that not how we see it. We think Omaha is perfect.

Let’s examine the two mottos of the area: Omaha and Council Bluffs (Omaha’s sister city across the river from Omaha).

Omaha’s motto is “We Don’t Coast”. This has two meanings. This first is literal. Obviously, we don’t have coasts. And to add to that, we don’t mountain either. But the metaphorical meaning is even more true: we’re a city of do-ers. We don’t slack. We don’t waste time. We act on our ideas. We collaborate. We make waves.

Council Bluff’s motto is “Unlike Anywhere Else. On Purpose.”. Our friendly, work-hard, be-nice attitude, does not come naturally; rather, it’s something we strive to be. We know it puts us ahead. We don’t have to try to be hip and trendy, our values are more genuine. We’re happy with our identity.

What is Omaha’s identity? Let’s dive a little deeper.

 

Omaha’s bragging rights

Every town in the country has that one special thing that draws tourists, like a giant ball of yarn or a natural history museum. Omaha has more than gimmicks, however. We have the whole package.

We have the people.

We spoke a little about our personality in reference to our mottos. One phrase that CAN likes to use to describe the good people Omaha is “homesteader mentality.” We’re not afraid to put in hours and work. The 9-5 workday is not always our mode of operation when a job needs to be done. We work overtime to accomplish goals, and we approach problems with the end goal of figuring out a solution, not just putting in hours. 

Anecdote: Our original Founder used to tell people we only worked half days…7am to 7pm…

We also have a different kind of people. Because Omaha doesn’t have the density of other big cities, we have had to make our actions “on purpose”. In Omaha, people are willing to give advice and collaborate. Since everyone is connected in someway, with the right idea anyone could have lunch with 1 of the 5 billionaires that live in Nebraska. On the coasts, these people would be way to pre-occupied to give just any idea the time of day.

Most importantly, the people of Omaha understand good enough. We’re not caught up in the fame game. You could say we’re slow and steady, because we strive for improvement, not perfection. We don’t cut corners, we work hard to build ourselves up. Just like CAN has done in the past 9 years.

We are the place.

We’re not just small town in the middle of the country. Omaha has a global reach that surpasses many of the other bigger cities in the MidWest. Omaha is a business community. 

For instance, not so long ago Omaha was the equivalent of Silicon Valley for telecommunication. A lot of these big corporations are still headquartered in Omaha. Omaha currently has 4 Fortune 500 company headquarters.

Our agricultural companies ship grain all over the world, and we manage money for people across all six inhabited continents.

This spirit of success continues at every level. You can see it in the halls of the start-up collaborative. People young and old come to work on tech-related ideas and products that they believe will boost up Omaha’s name even more. 

Omaha is also a major transportation hub, with the largest N/S and E/W interstates running through the city and the nation’s largest railroad located downtown. In four hours you can go just about anywhere in the country from our airport, making Omaha a major crossroads of tech and transport.

Not to mention our cost of living is among the lowest in the country. Less personal expenses mean you can invest more in your business. More investments equals more momentum for success.

Still, starting a business in Nebraska has its challenges. Constraints, however, often produce creative solutions. With a state population of 1.8 million, isolation has been CAN’s biggest constraint. Isolation has forced CAN to learn to build a national client base using blogging, social networks, and virtual meetings.

We knew that this results-oriented culture of Omaha would help us create a business that provides real value for our customers and help to keep our business focused on the long-term instead of quick wins.

We have some cool things.

You probably don’t need a whole lot more convincing, but here’s a few websites that list awesome social and cultural aspects of Omaha:

  1. “8 Reasons to Move to Omaha, NE”
  2. “7 Reason why Omaha is the Best City in the US to Live In”
  3. “30 Things You Need to Know About Omaha Before You Move There”

Food, fashion, furry things. When we at CAN aren’t busy working on projects, we have unique opportunities to explore the MidWest culture.  

 

CAN Loves Omaha

Between the travel opportunities, the global reach, the hard-working mentality, and the room for growth, CAN couldn’t choose a better city for their headquarters. Omaha is producing more tech talent every year at half the price of those on the coast. CAN wants to hire this talent, give them real data analytics experience, and send them wherever they need to go. Without the resources and mentality of Omaha, this would not be possible.

In the end, CAN is going anywhere. We believe in our community, we believe in improvement, and we believe in sticking with our roots. 


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Why will CAN stay in Omaha?

on May 23

The Man Behind the Scenes: An Interview with Nate Watson

on April 20

Open up any computer at the CAN headquarters and you’ll see our favorite data science website as our homepage: datasciencecentral.com. Articles, webinars, resources, ideas, tools, you name it. If it’s related to data science, it’s there.

Today, we’d like to share some knowledge from Rick Riddle. Check out his article “How can organizations successfully convert big data into real-world decisions?” by clicking on the link.

It’s all about how to apply the stacks and stacks of data your business has on file to real-life decisions. Which is what CAN is all about: using your data to help you. It may seem an overwhelming task, but CAN’s data scientists love challenges. Read over this article and let us know if you think we can help you with your data needs.

Contact Nate Watson at support@canworksmart.com.


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11

Last month, TechBus interviewed our very own President, Nate Watson. TechBus is an Omaha-based group that posts bi-weekly interviews about local businesses and new technology.

This video is everything you’ve ever wanted to know about CAN: who we are, what we do, how we’re related to the Data Science Academy, and how our staff augmentation model works. It’s a great way to get a glimpse into CAN for those who may be interested in contracting us, being employed by us, or being taught by us at the Oma-DSA.

To schedule a phone call with our very own Nate Watson, send us an e-mail at nate@canworksmart.com.


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11

Here at CAN our free-time is spent researching the latest trends in and facts about data science. In a skim of Tableau Public, we found this fascinating visualization about women in tech. Tableau Public is a platform to post data visualizations made with Tableau. You don’t have to be a data expert to share a visualization, you just have to be excited about data.

With this particular visualization, you can see how many fewer women receive tech-related degrees than men. As women are quickly overtaking men in educational status, it’s more important than ever to attract their attention to the opportunities of the tech world. We at CAN believe in giving a real-life data science education to all who want to pursue it. That’s why we created the Omaha Data Science Academy with Interface Web School. Sound like something you’d like to know more about? Check out more information here.


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11
Matt Hoover Dynamo

Check out the video below of one of our data scientists and Director of Data Visualization Matt Hoover giving a tour of the The Startup Collaborative. Matt was interviewed by Omaha tech company Dynamo. Dynamo is a new kind of IT consulting and recruiting agency that is based on an understanding of who companies actually need — valuing people and culture fit over transactions and placement fees.

Dynamo + Matt Hoover from Brody Deren on Vimeo.

CAN HQ @ The Exchange Building

In the video you’ll watch Matt as he shows off the Omaha Startup Collaborative’s coworking space at the Exchange Building, learn a little about the Omaha Data Science Academy, and see up close footage of CAN’s headquarters. Matt also mentions his newest project involving March Madness, Creighton basketball, Tableau, and statistics. Sound intriguing? Find out more here.


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11

After the first weekend of basketball, our Machine Learning Prediction tool has good results.

We had two measures of success: We wanted to win at least 46% of our picks and we wanted to “win” using virtual money bet on the money lines. By both measures, we had success: We correctly picked 6 upsets out of the 13 games we chose (46%) and we won $1,359 off the 6 correctly picked upsets (profit of $59 on $1300 laid ($100 per game) or 5% ROI).

The details:

Overall there were 10 instances where the lower seed won in the first two rounds. This year is on track for fewer lower seeds winning (22%) than the historic rate (26%). So even with “tough headwinds” we still came close to our expectations.

“But CAN, there were multiple lower seed winning that you didn’t pick. Why didn’t the model see Middle Tennessee upsetting Minnesota?” The answer is simple, MT winning was a result of variables that we weren’t measuring. Our picks were based on games that matched our criteria were based on variables found in most (not all) of the games in which the lower seed won in past years. Lower seeds can and will still win, our model was built to predict the highest number of upsets without over picking. This is actually the perfect example of a model, even great ones, will not predict all. However, most, even some, in business, can mean huge revenue increases or monies saved.

Besides we had some really, really close calls that would have put us way, way ahead. There were several games where we had that the lower seed having a good chance of winning and they simply lost (Both Wichita State and Rhode Island had the games tied with under a minute to go). We picked multiple games where the money lines showed Vegas gave no chance of the upset, yet the teams came very close. Our play was to choose games that match the criteria and spread the risk over several probable winners. This wasn’t about picking the only upsets or all of the upsets, this was about picking a set of games that had

Our goal was to not choose games in a vacuum (which is how you bet), but instead to choose games that match the criteria and spread the risk over several probable winners. This wasn’t about picking the only upsets or all of the upsets, this was about picking a set of games that had the highest probability of the lower seed winning. And by our measures of success, we achieved our goal.

We aren’t done quite yet either.

For the next round, we have 5 games that match our criteria:

Wisconsin over Florida
South Carolina over Baylor
Xavier over Arizona
Purdue over Kansas
Butler over North Carolina

**If any games match our predictive criteria in the next round, we’ll post them Saturday before tip-off.

The results of the first rounds:

The Machine Learning algorithm performed as advertised: It identified a set of characteristics from historic data that was predictive of future results. The implications for any business is clear: if you have historic data and you leverage this type of expertise, you can predict the future.

For more information about how we created the Machine Learning algorithm and how we are keeping score, you may read the Machine Learning article here:

http://canworksmart.com/machine-learning-basketball-methodology

If you would like to see how Machine Learning could improve your business, please feel free to reach out to either of us: this can relate to your business contact Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com) or Nate Watson of CAN (nate@canworksmart.com).

 


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11

The Cabri Group / CAN Machine Learning Lower Seed Win Prediction tool has made its first round forecast! Without further ado:

East Tennessee St. (13) over Florida (4)
Xavier (11) over Maryland (6)
Vermont (13) over Purdue (4)
Florida Gulf Coast (14) over Florida St. (3)
Nevada (12) over Iowa St. (5)
Rhode Island (11) over Creighton (6)
Wichita St. (10) over Dayton (7)

 

* If the last play in games add another predicted upset, we’ll update that prior to the game starting.

Update: USC (11) over SMU (6)

One of the obvious observations on the predictions is: “Wait, no 8/9 upsets????” Remember these games show the most similar characteristics of the largest historic collection of upsets. This doesn’t mean that there will be no upsets as 8/9 nor that all of the predictions above will hit (remember we are going for 47% upsets) nor that all games not listed will have the favorites win. The games on the list are there because they share the most characteristics with historic times when the lower seed won.

Also, one of the key team members on this project, Matt, is a big Creighton fan (and grad). He was not happy to see Creighton on the list. I’ll speak to that one specifically. In the technical notes, I indicated that one of the many criteria that is being used is was Defensive Efficiency (DE). Machine Learning algorithm (Evolutionary Analysis) doesn’t like it when the lower seed has a large gap of DE between the lower seed and the higher seed. Creighton actually has a lower Defensive Efficiency than Rhode Island. Sorry Matt. Again, it doesn’t mean Creighton won’t win, it only means that the Rhode Island v. Creighton game shares more criteria with a the largest collection of historic upsets than the other games in the tournament.

As we indicated, we will use the odds as well as a count of upsets to determine how well we do as the tournament goes on. We’ll have a new set of predictions on Saturday for the next round of the tournament and a recap coming on Monday.

For more information about how we created the Machine Learning algorithm and how we are keeping score, you may read the Machine Learning article here:

http://canworksmart.com/machine-learning-basketball-methodology


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11
machine learning prediction

Machine Learning and the NCAA Men’s Basketball Tournament Methodology

 <<This article is meant to be the technical document following the above article. Please read the following article before continuing.>>

“The past may not be the best predictor of the future, but it is really the only tool we have”

 

Before we delve into the “how” of the methodology, it is important to understand “what” we were going for: A set of characteristics that would indicate that a lower seed would win. We use machine learning to look through a large collection of characteristics and it finds a result set of characteristics that maximizes the number of lower seed wins while simultaneously minimizing lower seed losses. We then apply the result set as a filter to new games. The new games that make it through the filter are predicted as more likely to have the lower seed win. What we have achieved is a set of criteria that are most predictive of a lower seed winning.

 

This result set is fundamentally different than an approach trying to determine the results of all new games whereby an attempt is made to find result set that would apply to all new games. There is a level of complexity and ambiguity with a universal model which is another discussion entirely. By focusing in on one result set (lower seed win) we can get a result that is more predictive than attempting to predict all games.

 

This type of predictive result set has great applications in business. What is the combination of characteristics that best predict a repeat customer? What is the combination of characteristics that best predict a more profitable customer? What is the combination of characteristics that best predict an on time delivery? This is different from just trying to forecast a demand by using a demand signal combined with additional data to help forecast. Think of it as the difference between a stock picker that picks stocks most likely to rise vs. forecasting how far up or down a specific stock will go. The former is key for choosing stocks the later for rating stocks you already own.

 

One of the reasons we chose “lower seed wins” is that there is an opportunity in almost all games played in the NCAA tournament for there to be a data point. There are several games where identical seeds play. Most notably, the first four games do involve identical seeds and the final four can possibly have identical seeds. However, that still gives us roughly 60 or so games a year. The more data we have, the better predictions we get.

 

The second needed item is more characteristics. For our lower seed win we had >200 different characteristics for years 2012-2015. We used the difference between the characteristics of the two teams as the selection. We could have used the absolute characteristics for both teams as well. As the analysis is executed, if a characteristic is un-needed it is ignored. What the ML creates is a combination of characteristics. We call our tool, “Evolutionary Analysis”. It works by adjusting the combinations in an ever improving manner to get result. There is a little more in the logic that allows for other aspects of optimization, but the core of Evolutionary Analysis is finding a result set.

The result set was then used as a filter on 2016 to confirm that the result is predictive. It is possible that the result set from 2012-2015 doesn’t actually predict 2016 results. Our current result set as a filter on 2016 data had 47% underdog wins vs. the overall population. The historic average is 26% lower seed wins and randomly, the 47% underdog win result could happen about 3.4% of the time. Our current result is therefore highly probable as a predictive filter.

 

The last step in the process is to look at those filter criteria that have been chosen and to check to see if they are believable. For example, one of the criteria that was Defensive Efficiency Rank. Evolutionary Analysis chose a lower limit of … well it set a lower limit, let’s just say that. This makes sense, if a lower seed has a defense that is ranked so far inferior to the higher seed, it is unlikely to prevail. A counter example is that the number of blocks per game was not a criteria that was chosen. In fact, most of the >200 criteria were not used, but that handful of around ten criteria set the filter that chooses a population of games that is more likely to contain a lower seed winning.

 

And that is one of the powerful aspects of this type of analysis, you don’t get the one key driver, or even two metrics that have a correlation. You get a whole set of filters that points to a collection of results that deviates from the “normal”.

 

Please join us as we test our result set this year. We’ll see if we get around 47%. Should be interesting!

 

If you have questions on this type of analysis or machine learning in general, please don’t hesitate to contact Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com) or Nate Watson at CAN (nate@canworksmart.com).

**Disclaimer: Any handicapping sports odds information contained herein is for entertainment purposes only. Neither CAN nor Cabri Group condone using this information to contravene any law or statute; it’s up to you to determine whether gambling is legal in your jurisdiction. This information is not associated with nor is it endorsed by any professional or collegiate league, association or team. Machine Learning can be done by anyone, but is done best with professional guidance.

 

 

 


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11

The first run of the Omaha Data Science Academy proved successful. Already 4 of the 6 graduates have found jobs in a related field. Silicon Prairie News found this noteworthy and published an article about it here.

Silicon Prairie News is a newsroom and community forum focused on start-ups in the Great Plains/MidWest region. Silicon Prairie is a venture of AIM, a non-profit organization centered around building community through technology.

For some information on the Oma-DSA, contact nate@canworksmart.com.


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Why will CAN stay in Omaha?

on May 23

Check out Data Science Central

on May 11

Every week CAN will highlight a past or present CAN employee as part of a CAN alumni network series. This week we feature Grant Stanley.

Grant Stanley founded Contemporary Analysis in 2008. For 6 years he served as CEO and president before handing off the company to Nate Watson to pursue new ventures. In 2014, Stanley launched Bric. Bric is a managing software system designed specifically for creative agencies. Today we highlight a post on Bric’s blog about the art of time tracking and the importance of the data it collects:

https://getbric.com/time-tracking-needs-new-purpose/



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