November 6th: Machine Learning & Data Science

Back by special request, here is the half-day class from Infotec 2017 on How to Find Machine Learning Experts When None Exist. This is part of our new partnership with Bellevue University providing both online and onsite data science training. In this class you will learn: What is a machine learning data scientist and how do you find/hire one? Many companies struggle with the concept of what machine learning is and what it does. Since Machine Learning is quickly becoming a barrier to entry for a product, companies will soon not consider a piece of software or app that does not have machine learning built into it. However, Machine Learning skills are extremely hard to find and almost cost prohibitive. Many companies are trying to fill the need using a regular data scientist (also hard to find). This session will discuss the necessary skill sets of a Machine Learning data scientist, the parallel data scientist skills which can be cross trained, and how to manage those people once they get the right training. It will also cover the value proposition of Machine Learning and how it applies to your business.

Enrollment begins September 1st.

 

December 4th: Data Science Leadership

Built for those who are going to be tasked, or are currently tasked, with leading the philosophical change that is Data-Driven Decision Making. The key word is “leading”– getting ahead of the curve and staying there. Leaders first need to understand what has changed in business in a world where data science is building moats between business competitors. There will be training on what data science is, how data science works, and how data science can drive better decisions as well as how to implement ideas and how to hire, train, and manage talent. The seminar will then address some of the major barriers that leaders deal with: the bias of inactivity, the bias of decision avoidance, ROI for “science” projects, acquiring resources, choosing the right projects, and management’s ability to understand the results. There will also be discussions on job task automation, classification algorithms, effective charts/graphs, data ripeness/freshness, and model refresh frequency, etc. You will come away with a much better understanding of the people you are managing, the people who need their predictions, and how to stand in between both camps.

January 22nd: Cohort 6 begins

The ODSA begins its 6th Cohort in January 2019. Get trained from 6-9 pm on what CAN’s data scientists did from 8-5. In these classes, students will be introduced to some of the major concepts of Data Science (Python Programming, Database Management, Basic Modeling Building, Machine Learning, and Data Visualization) and some of the tools used in the profession (Python, Data Science Platforms, and Tableau). This is not meant to replace the degree from four year institutions, but instead, to enhance the person who is almost a data scientist but is missing a few critical skills to “round them out.” Graduates of the DSA will leave with the confidence and skills of an entry level data scientist and be able to have conversations with business units, build predictive analytical MVPs, and be able to know and continue to learn the skill sets needed for data science projects.

Register here.




Last Thursday, Data Science on the Plains hosted their first meetup event. The event started with peer to peer networking over pizza and beer followed by a presentation by Union Pacific’s Jason Hochwender who spoke about the multiple data analytic projects currently happening at UP and his own pursuit of creating a data driven culture. 25 attendees from a range of different companies across Omaha were able to brainstorm strategies on how to lead data science initiatives into our own organizations and navigate politics that may arise in the process. Come join us at our next meeting in early November and help us continue to build the data science community in Omaha.

Read more…


Live Well Omaha is a nonprofit that leads a coalition of organizations to collectively prioritize health in Omaha. Live Well is paving new ground in the nonprofit sector with their data driven mindset.  They approached Contemporary Analysis with an idea: to visualize their health data with interactive storytelling maps, tracking the progress of different health metrics in Omaha such as child healthy weight.

 Join them this Thursday, June 21 to discuss possible solutions to health issues in Omaha.  Discover how the right visuals and story can help people digest information that may be hard to glean from printed statistics alone. Look for our case study about Live Well in the coming weeks to learn more. Register for the event: Map Chats with Live Well Omaha.

Read more…


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Here at Contemporary Analysis we believe good visualization is the key to understanding data and making data-driven decisions from it. We have worked with multiple companies (including nonprofits) over the years to provide valuable visualizations of their data, both at the macro and micro levels, to help them use their data more strategically. While technologically agnostic, we do recommend Tableau for those users who are either new or non-technical. We offer classes on how to use data visualization through our school, the Omaha Data Science Academy.

Read more…


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Data science has been named America’s hottest job by an article written in Bloomberg.

In recent years, there has been an explosion in the amount of available data and an advancement in tools that can tame and harness it. Companies are counting on data scientists to make discoveries within the data, yet there is a major shortage of people who are skilled in this area. The article recounts how this scarcity is causing companies to pay incredibly high wages to attract these sought-after professionals.

 

Programs for aspiring data scientists are difficult to find within traditional institutions because data science has only sprung up in recent years. Nontraditional educational routes such as the Omaha Data Science Academy has tried to fill this gap. Interested in joining the next cohort? Apply now.


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GI Bill

The GI Bill is now accepted by the Omaha Data Science Academy. Veterans, in partnership with the Interface Web School, can now use their GI Bill® to receive relevant tech training at the DSA

We want to help veterans jumpstart their career transition by preparing them with the necessary skills needed for a successful and profitable job in data science. Learn from practicing data scientists and get a leg up on college grads. 

Cohort 1 starts with Introduction to Data Science classes on July 11. Apply now.


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Reading the full GDPR is probably not your idea of fun.

Your Data Isn’t Ready, and Your Company Might Not Be Either

As of May 25th, all organizations working with the data of EU citizens will need to be GDPR (General Data Protection Regulation) compliant.

Global Data Protection Regulation (GDPR) is the EU’s new data regulation and it applies to everyone who has customers that are citizens in the EU. That means it applies to almost any internet business.

These new regulations may completely change how your business is required to handle user data and sometimes even how you operate.

Your organization could be fined up to 20 Million pounds ($28M US dollars as of today) or 4% of global turnover (whichever is greater), so pay close attention!

Seven New GDPR Requirements

Here’s a quick summary of seven new regulatory requirements and how they might affect you. Before we get started, here are two important terms you need to understand:

Data Controller: Any entity that “controls” the data by deciding the purpose or manner that the data is or will be used.

Data Processor: Any person or group that “processes” (obtains, records, adapts, or holds) the data on behalf of a controller.

1. Consent

When asking users to consent to your terms, you cannot use indecipherable terms or conditions documents that are filled with legalese. As a user, I’m a big fan of this; from a company’s perspective, this can be a gray area. Read into the official documentation (linked at the end of this post) for details.

On top of clarity, you also need to ensure that it’s just as easy for users to withdraw their consent (after giving it, not just when you present it initially) as it is for them to give their consent.

2. Breach Notification

In the event of a data breach, you have to notify any data controllers and processors within 72 hours. If a data controller determines that the breach “is likely to result in a high risk to the rights and freedoms of individuals” then they also have to notify each individual user that was affected.

These notifications must contain at least:

  1. The nature of the personal data breach (number of categories of subjects and records affected).
  2. The Data Protection Officer’s contact information.
  3. Describe the likely consequences of the personal data breach.
  4. Describe how you’re going to address the breach.

Thankfully you are allowed to provide this information in phases if it isn’t available all at once.

3. Right to Access

Your users (or “data subjects”) have the right to obtain a free copy of their personal data. In addition, they have a right to receive a confirmation of their personal data being used or processed.

If you’re wondering what providing “a free copy of their personal data” looks like, check out how Google does it1.

4. Right to Be Forgotten

Users (data subjects) have the right to have their data erased from the data controller “without undue delay” if:

  • The controller doesn’t need the data anymore.
  • The subject uses their “right to object” to the data processing, or withdraws their previous consent, or was a child when the data was collected.
  • There is a legal requirement for the erasure.
  • The controller or processor is processing the data unlawfully.

As always, there are a lot of exceptions here, be sure to read the detailed resources below if this applies to you.

5. Data Portability

Not only do users need to have access to download their data, you should also offer different tools for portability; such as APIs alongside a direct download. Direct downloads should be offered in multiple formats, again, Google is a great example here1.

This could mean that you need to allow a competitor to be able to directly import your data if the user requests it.

Thankfully, you’re not responsible for protecting the data copy that has been received by the user.

6. Privacy by Design

This means you need to be thinking about data protection all the way down to the design of your internal systems.

Privacy by design calls for data protection in infrastructure too, meaning there may even be non-technical changes you need to make to your company structure. Now is a great time to look for vulnerabilities in your internal practices and even consider getting a security audit.

7. Data Protection Officers

Qualified officers have to be appointed in any public authority or large organization (over 250 employees) that monitor or process personal data.

If your company qualifies, you should dive into the qualifications and start looking for an officer right away. These regulations go into effect May 2018.

Conclusion

If you’re doing business with EU citizens it’s in your best interest to get on top of these new regulations as quickly as possible. Hopefully, this article provided you with enough detail to know where to start and what to expect.

GDPR isn’t the only thing that requires thoughtful implementation, check out our recent guide on Best Practices for Implementing Data Science.

Detailed Resources

Citations

  1. Download Your Data – Google Account Help

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Contemporary Analysis (CAN) and Cabri Group and have teamed up again to use Machine Learning to predict the 2018 NCAA Men’s Basketball Tournament. This is different than last year as we are picking the entire 2018 bracket instead of just upsets.

Historically, only 26% of tournament’s games end in an upset (this includes games from all rounds). That’s 17 out of 64 games. Last year we did really good. Only failing to predict 3 upsets and getting 50% of our predictions right. We are going to need to improve a bunch to win that 1M/year for life from Berkshire Hathaway–including that wee bit about having to work for Berkshire Hathaway to be eligible. This year we added far more variables and used an ensemble model. Will we be perfect? Probably not. Here is the problem with using Machine Learning to try and predict a perfect bracket:

 

A). Error propagates itself through the bracket. This is why the odds of a perfect bracket are around 1:128 billion. If you pick San Diego State to upset Houston-

Side note: The machine learning is in fact, picking Houston by the slimmest of margins. However, if San Diego State wins, the machine learning is actually picking them to go on to beat Michigan, Providence, and then Ohio State to win the entire region.

 

and then Houston actually wins, you will lose the entire region. Perfection may have to do with a 6/11 game that no one would normally care about except its the tournament, and everyone cares about every game.

B). Machine Learning and Predictive Analytics aren’t about being 100% accurate. You wouldn’t want to pay for that kind of accuracy even if it were possible. We are trying to be less wrong for companies. This is why predicting upsets made sense and the whole 2018 NCAA Bracket is so hard. Figuring out who is most likely to be an outlier (churn) is something we do all the time. And, we can error on the side of being wrong. We would just tell you to call both Houston and San Diego State (in this instance) because calling them to talk to them about staying at your company has no ill effect. (i.e. there is very little cost to being wrong in this example.) There is a huge cost to being wrong in the tournament in the later rounds as you are predicting the next game based on your assumption of correctly predicting the last game.

 Without further ado, here is what the Machine Learning algorithm predicted as the bracket:

 

CAN Bracket-pdf

 

If you have questions on this type of analysis or machine learning in general, (or if we are perfect and you would like to congratulate us), please don’t hesitate to contact:

Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com), or

Nate Watson at CAN (nate@canworksmart.com).

 

Now for some disclaimers: 

Understand the technique that finds a group of winners (or losers) in 2018 NCAA bracket can be based on any metric. Our analysis isn’t to support gambling, but to open up people’s minds onto the possibilities of leveraging Machine Learning for their businesses. If we can predict things as seemingly complex as a basketball tournament (Something that has never been correctly predicted), then imagine what we could do with your data that drives your decisions

We will be keeping score using the very traditional 1,2,4,8,16 point process. 

 

**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.


Today is Contemporary Analysis (CAN)’s 10th birthday!!! Although we are not the company we started back in 2008—different logo, different owners, new leaders, new data scientists, even a new way to serve up data science,—we still have the best team in the region and now 10 years of wisdom of how to implement, build, and train data scientists as well. Let us help — even if it is just a phone call for advice. Our goal is to help every company in the region use data science to be competitive in their niche.

Also, stay tuned for our upcoming article on CAN via 2008, 2018, and in 2028. The world is going to be an exciting place for data science in 10 years and we are here to help you get there.

#Celebrate #CAN10



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