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Any person you hire for your team is an investment. You take careful steps to ensure their fit in the company. You go the extra mile to ensure their skills translate as perfectly as possible to the position you seek to be filled.
Most companies, unfortunately, do not understand the complications of hiring a data scientist. No two data scientists have the same skill set. And there’s not a specific “attitude” associated with data scientists. Personalities range quite drastically. Therefore, you can’t simply choose a “data scientist” and trust that he or she will fit with your company. It’s different than hiring a salesperson or HR representative.
 
So, before you invest in hiring a full time data scientist for your medium to large sized business, there are more than a few things you need to consider.
 
Contemporary Analysis (CAN) offers another option to fill your data analytics needs. We offer a full analysis of your company to determine the projects that will improve your areas of need. We lend you one of our data scientists to use hourly until the projects are over. Less strings attached, less money, higher ROI.
 
Let’s first explore the scenario of hiring a data scientist blindly, and see where it takes this hypothetical company.
 

Scenario 1: Hiring a data scientist full time

 
Perhaps you are the manager of a local bank. You’ve grown significantly in the past 10 years, and you know you have enough data to start analyzing trends with your clients. You’ve noticed that at least three customers drop their services every month, and you wonder if a data scientist could provide an answer to stop this trend.
 
The first step, you believe, is to hire a full time data analyst as part of your team. You write up a short job description and send it out to the hiring sites.
 
Someone with a Master’s degree in data science doesn’t accept a position for less than $100,000/year. Along with $25,000 in benefits, this is quite the price tag. You may understand this and accept it begrudgingly as the only option.
 
What you may not realize, however, is that it takes you about 6 months to find someone with a Master’s in data science and relevant work experience. Then it takes your bank about 3 months to train him in. And finally, 3 months after he is trained in, he builds a software useful to your company.
 
That’s a year from the time you posted the job before you see any kind of ROI, and by then you’ve spent half of a year’s salary on this person.
 
The expenses for hiring and training a data scientist are immense, but there are more than just monetary issues with hiring a data scientist full time. Here is the short list of things you need to consider before you hire full time:
 

• Not all data scientists will like your company.  Just because someone is qualified on paper doesn’t mean they fit well with your company’s community. At $125,000/year and with 6 months needed before ROI, do you want to take that risk? Personality clash is a real threat.
• No two data scientists have the same sets of skills. That’s right. “Data science” is a loose term meaning “an interdisciplinary field about scientific processes and systems to extract knowledge or insights from data in various forms.” Interdisciplinary is the key word here. Some data scientists may be trained in specific softwares, others may have not even heard of it. When you lock yourself into one person, you may be losing out on knowledges beyond his education.

• How do you know you need a data scientist? Without experts to examine your needs, are you sure the hire is worth the investment? Perhaps figuring out why your bank is dropping clients every month will only take about a month, but you’ve hired someone indefinitely. What other work will you find for him?

 
There are other options. Consider hiring an hourly data scientist through CAN.  Let’s explore the benefits below.
 

Scenario 2: Work with Contemporary Analysis

A Case Study from CAN

CAN has worked with several Fortune 500 companies. In one instance, one of these companies needed assistance creating a software that could predict the failure of telecommunication huts. Loss of several huts slows service to customers, which ends up a nightmare for the finance team.
 
The scope of the project was large: 2,500 telecommunication huts over the Western United States. Over 500 of these locations were in fairly remote areas, making them hard to reach. The scope of this project may have been enough to convince the company that they needed to hire a full-time data scientist, but instead, they saw the benefits of working with CAN.
 
The result? CAN set up a weekly survey process for employees at each station, covering 12 potential problem areas.  
 
The data collected from these services was used to create a “survival model” for each roof. CAN set up a system for predictive analytics with this Fortune 500 company over an established period of time, then made sure the system was self sufficient and did not rely on CAN’s constant attention.
 
With work complete, the company paid CAN and CAN moved on to new projects. Always available for advice, CAN remains a tool for that company, but not at a cost of $125,000/year.

The benefits of using Contemporary Analysis to hire an hourly data scientist

 
To save time and money, and increase productivity, consider these benefits of using CAN for your data science needs.
 

• With CAN, you can hire a data scientist that fits the skill level needed to attain your goals. If you need someone trained in Tableau, then you get someone with that training. With CAN, there is no risk of a learning curve with your hire.

• Hiring a part time data scientist means you’re not locked into one set of skills. In a similar vein, when your needs change, instead of paying to train your full time hire, CAN simply assigns a new person to the job. This gives you a bigger skill set advantage.

• No 6 month hiring process. No training. No wallet-bursting budget. No issues with HR. CAN can write up a proposal for your needs in as little as two weeks, and work starts immediately upon signing the contract. Work starts on Monday, not 6 months from Monday.

• You will see an ROI in 30 days or less. We at CAN works fast and effectively. It’s our job. Once our systems are in place, you see immediate results. Before you would have a job description for a full time position on your site, CAN will have created analytic software.

• You don’t need to worry about keeping your full time data scientist busy. Once the project is over, it’s over. You don’t need to worry about filling someone’s time or wasting money on little work.

• You data scientist won’t quit! A part-time data scientist will do his work and fulfill his duties. You won’t have any surprise 2 week notices in your office.

 
Perhaps you believe your data science needs are great, and you still believe full time is the way to go. Before you plummet down this expensive road, give us a call. If anything, we can give you an analysis of how great your needs are, and you can make a decision from there.
 
For more success stories, see CAN’s website for more case studies at http://can2013.wpengine.com/case-study/.

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.

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/

The Omaha Data Science Academy: What is it?

In 2008, Contemporary Analysis (CAN) began helping companies build predictive analytics capabilities, mostly through project based work. Last year, CAN recognized a rising need in companies: more and more, businesses needed to bring PA capabilities in house but lacked the staff to do so.  So, in mid-2015, CAN switched from project based work to a staff augmentation model. This last year, the need has grown exponentially as CAN has been asked to train staff for more companies, sometimes two at a time. CAN decided it needed a better, standardized way to train individuals to be part of data science team.
In July of 2016 Contemporary Analysis (CAN) announced the open enrollment for the Omaha Data Science Academy (Oma-DSA), the ultimate goal of which is to train a data scientist for every company in Omaha. With the help of Interface Web School and through the CONNECT re-education grant, the Oma-DSA was born.

Nebraska’s role in the development of the Oma-DSA:

 
Coursework in the Oma-DSA is designed to provide training to those who already have business acumen and don’t need another degree just to qualify for an entry-level data science job. They really only need skill-based training. This goal led CAN to partner with the CONNECT Grant in Nebraska. This federal grant provides Nebraska’s underemployed workforce with skill training and financial support to begin careers in IT with companies throughout the state. The partnership was perfect as both CONNECT and CAN seek to bolster Nebraska’s professional workforce with more highly trained individuals.
 

Interface’s role in development and administration:

 
In search for an example of how to teach an academy, CAN connected with Shonna Dorsey of the Interface Web School. Interface offers courses to bolster skills and knowledge of technology and online softwares to help strengthen the workforce. It appeals the most to people who may already have degrees and careers, but are looking for new opportunities. Class schedules are flexible for busy lives.
 
CAN was excited because Interface is both a platform for learning and a platform for teaching. They offer students an immersive learning program lead by industry experts and a professional network that connects students and businesses throughout the Midwest.
 
“We understand,” commented Shonna, “that first and foremost it takes talented people to build talented people.
 
This was in complete agreement with how CAN thought and wanted to run the data science, and the partnership was set.
 
“Interface is helping us setup the platform and teaching us the very detailed structure that goes into running an Academy such as the DSA”, commented Nate Watson, president of CAN and administrator of the Oma-DSA, “without them, we would still be back at step one.”

Who teaches the Oma-DSA?

 
The answer to this question sets CAN apart from many other data science courses. The Oma-DSA is taught by the data scientists who work at CAN. Each day the professors spend their time solving a problem for a client and then teach the students those same techniques and solutions. With the DSA, there are no textbooks, students are taught scenarios that are sometimes only hours old.  
 

What was the outcome of the first iteration?

 
In December of 2016, the DSA graduated 6 entry level data scientists. Four have already been hired  by local companies looking to implement data science into their daily managerial tasks. Multiple others companies have shown interest in the graduates and many others are excited to see what the next group of graduates will have to offer.
 

What did CAN learn from the first run of the academy?

 
Although the first run was successful, CAN is building improvements for  second run of the Oma-DSA starting in January. The eighteen week course will be divided into 4 modules: Python programming, statistics and mathematical modeling, database design, and data visualization using Tableau. These can be taken individually in any order. When all four are completed, the graduate receives a Fundamentals of Data Science Certificate.
 
The modular system is also significant because it allows students or company to enroll their employee in just one module. If a person were to only want Tableau and not the entire certificate, the module format allows them to enroll in only one module. This also allows a student to test out of a module as well. A Data Base Administrator, for example, won’t have to take a database design class anymore. They can enroll in the other three and receive a certificate.
 

What is the future for the Oma-DSA?

 
In the second half of 2017, CAN hopes to offer masters-level classes in Tableau and machine learning to continue education after the Fundamentals certificate. CAN is also researching customized classes in vertical-specific problems and solutions.  
The next class begins January 23, 2017. You can apply here.
There is nothing else like the Oma-DSA in the Omaha, NE and great plains area. This means that Omaha has the potential to be known internationally as a hub for budding data scientists. Not only that, but it also means that companies in Omaha have an enormous advantage by their proximity to highly educated and expertly trained data scientists.

Every week CAN will highlight a past or present CAN employee as part of a CAN alumni network series. First up to bat is Eric Burns. 

Eric Burns is a former employee of Contemporary Analysis. In 2011, he brought on CAN’s first international clients. Today he is the CEO and founder of Gazella Wifi Marketing, which turns restaurant guest information into a marketing tool. He continues to be an active member of CAN’s alumni network.

Here are his thoughts on analyzing wifi marketing:

http://blog.gazellawifi.com/10000-visits-to-a-coffee-shop-wifi-marketing-data

No matter what kind of website you are running, web hosting is the first pillar of Website Analytics. In this day and age, it is crucial to rank high with Google and Bing if you expect to draw organic traffic to your site. Many people never make it past page one–no one makes it past page two. It doesn’t matter if your website is a blog, an online marketplace, or simply a static landing page detailing what your business is and how you can be reached. The end goal is to create warm leads of people interested in what you are selling. How do we do this? By measuring who comes to the site, what they read, and how long they stay. But, how do you know your metrics aren’t skewed by unreliable hosting? The answer lies in understanding the measurements. Here are a few things you should know:

First Pillar: Reliable Web Hosting

Uptime and Speed are the key words here. People simply get tired waiting for a site that is ‘temporarily down’ or loading slowly, so they will invariably hit the back button to return to the Search Engine Results Pages (SERPs), never to return. It is through the SERPs that you gain organic traffic, so if you don’t have reliable hosting and your site is often down or slow to load, you won’t gain anything from all that SEO you so painfully worked for (or paid for) to move you up to that coveted first page of Google. Look for hosting from providers such as Flywheel or Best Web Hosting that gives you tools to maximize your up-time as well as speed. Then, and only then, can rest assured you won’t lose traffic due to inaccessible or slow web pages.

Second Pillar: Metrics

Once you are assured that you have a web hosting company that will keep your site up and running at speeds that won’t frustrate visitors, it’s time to see start tracking who is coming to your websites. Externally, this can be done by harvesting Wi-fi log-ins (Gazella Wifi), or internally (Google Analytics). Ideally, you want to track who came, how long they stayed, whether or not moved about through internal pages, or simply didn’t like what they saw and left. It should also be stated, keeping people on your website by leading them to other content is a necessity to keep them on your website, but ultimately your goal is to get them to download something or sign up for something.
How does web hosting have an impact on this? Remember, you can’t get accurate results if you are losing visitors due to unreliable hosting! Your numbers will not paint an accurate picture so your analytics will be skewed.

Third Pillar: Predictive Analytics

Once you have a stable hosting company and are measuring metrics, you can begin to do things like predict which advertisements or offers get a person to buy/interact. This is done through predictive analytics. Contemporary Analysis (CAN) uses data collected from its web site train its machine learning model to offer its potential customers articles they might be interested in. This is similar to what Amazon does with products, Netflix does with movies, and you can do with the right data.
 
Hosting, Metrics, and Analytics are the three elements that must work together if any type of website is to survive, grow, and provide leads. Without reliable hosting, your drop metrics are skewed and your click-through rates are diminished. Your website metrics will be skewed due to bounce rates due, not to bad content, but to speed and hosting. If your site is being damaged by poor hosting, I highly recommend changing hosting companies. With reliable hosting, your metrics will be accurate and your data can be used to predict customer interactions.
Your company will thank you for it.
For additional web hosting information, check out this free guide at https://firstsiteguide.com/web-hosting/. It’s a great way to get familiar with web hosting before you get started.

This summer, long time employee Nate Watson took over as president and owner of Contemporary Analysis. Situated in his new role, Watson has impressive plans for CAN’s future.
Since 2008 Contemporary Analysis (CAN) helped over 100 companies use predictive analytics to find patterns in their business data. CAN uses data businesses already collect, then explores those patterns to figure out what will likely happen. CAN has worked with some of the largest companies in the midwest including Kiewit, Gavilon, Mutual of Omaha, Blue Cross/Blue Shield, and West. CAN holds the reputation of solving the hardest problems in the Omaha data science field.
In mid-2014, after 150+ completed projects, co-founder and CEO, Grant Stanley decided it was time for a new leader to run the company. Stanley appointed then Senior Project Manager Nate Watson to run daily operations while he worked on a new project implementing machine learning into project planning and time management. Stanley’s new company, Bric, launched in late 2014.
Over the next year, Watson kept his eyes open for new ideas on how to make the culture and ideology of CAN work in today’s world. The idea for a new staff-augmentation model (see below) came as a cross between the need to provide a solution to companies that didn’t require massive political buy-in and budgets to build a POC. This idea struck a cord with two friends of Watson who decided to invest in the new ideology and buyout Stanley.

CAN’s motto is “Empower the great to build something greater.” Watson chose two investors who believe in empowering CAN to be something greater.

 
Through the transition, the mission of the company remained unaltered, albeit expanded. Watson partnered with two investors to help him with the buyout.
CAN’s new investors are Nick and Carrie Rosenberry. Both Nebraska natives, the Rosenberrys recently moved back to Nebraska after a stint in Minnesota. They bought into the business because they see a promising future in the data science industry.
“We were looking for a company poised to be on the bleeding edge of a bleeding edge industry. CAN completely fit the bill,” said Carrie Rosenberry.
Carrie is from Tekamah, Nebraska. She received her BS in Mechanical Engineering from UNL while also participating in the Raikes School. She then attended University of Minnesota Law School, where she graduated Magna Cum Laude. She will serve as General Counsel for CAN.
Watson remarked, “Having a lawyer on your team means we can build the ideology behind both the investment group and the agri-tech incubator (scheduled for development next year) using someone who understands the ultimate goal of CAN.”
Nick Rosenberry hails from Scottsbluff, Nebraska. He graduated from UNO with a Bachelors and Masters in Architectural Engineering before getting his MBA from the Carlson School of Management at the University of Minnesota. He serves as Chairman of the Board as well as general wisdom of business management for CAN.
With an on-team lawyer as well as a MBA on the board, Watson believes he has the team built to bring data science to every company regardless of vertical or size.

With the Rosenberrys on his side, Watson unveils a new business plan.

 
While not drastically changing their core business, CAN wants to change how companies interact with data science consultants. CAN aims to shift its main business model from a project model to a staff augmentation model. Previously, when a company needed a project done, they hired CAN, CAN did the job, the company paid CAN, and CAN moved onto a different project.
A staff augmentation model, on the other hand, means that CAN actually provides a data scientist to work directly for the client. By giving businesses the option of hiring a part-time data-scientist, companies no longer need to sift through projects and create extra budgets. It instead allows a company to test out how a data scientist would work in their culture, figure out how to implement ideology, and create the necessary roadmap for success long after CAN’s data scientist has been replaced with their own.
This however, has created a new problem: how and where to recruit the talent necessary to continue data science initiatives after CAN as a consultant has left?
CAN believes the answer lie in one of its new creations, the Omaha Data Science Academy (Oma-DSA). The Oma-DSA is a twelve week course designed to train entry-level data scientists who have business acumen but lack a few of the key skills needed before they take on corporate projects.
The Oma-DSA is designed to augment a person’s existing degree with advice and training from real data science experts in the field. This should provide talent, trained in entry level data science for companies to hire to run their new capabilities.
The first run of the Oma-DSA is this September.

Nate Watson and everyone involved with Contemporary Analysis is ecstatic about these new ventures.

 
CAN has always empowered the great. Under Nate Watson’s new ownership, CAN now has the time and resources to empower greatness within itself.
For more information on the Oma-DSA, or anything you liked about this article, contact Nate Watson below. 
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[contact-form-7 id=”4528″ title=”Omaha Data Science Academy Form”]

CAN is excited to announce, in partnership with the Interface Web School, the creation of Omaha’s first Data Science Academy (ODSA). 
This is something we have been working on for a long time. It is actually a continuation of a service we currently offer to clients where we train a company’s first data scientist. We feel this unique person, trained in both data science and business problem solving, is needed by their company to help implement the ideology more than produce mathematical models or produce visualizations.
In the past, we heard that while companies know how to find and hire a data scientist, they fear not being able to utilize this person or even know how to correctly scope how to use predictive analytics in their business. This caused them to not execute or to execute poorly and leave a bad taste in the organization’s mouth.
CAN has discovered that having a data science advocate (instead of just a data scientist) usually fixes the hangup with implementation in most companies trying to use data science for the first time. The realization there was a considerable lack of talent when looking to fill this need, led us to develop a school that teaches not only entry level data science, but also how to address the political red tape prevalent in changing how an operation thinks and makes decisions.

This academy will help CAN reach its goal of putting a data science advocate in every company in Omaha. While audacious, we feel this is a must to keep Omaha companies relevant in an economy where we are not just in competition from a company down the street but from every other company doing similar work around the world.

 
Details.
This certificate will teach some of the most important techniques and tools necessary to introduce data science into company culture, get necessary political buy-in, find, manipulate, and analyze the data present inside your company’s database, make predictions of outcomes, and create visualizations that can help non-technical users understand and see the identified trends and patterns inside the data.  
The ODSA is designed to help set a company down the road of data discovery and data-driven decision making. While not the heavy mathematician or economist created by four year degrees, the graduate will leave the Academy with the confidence and the 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 manage the skill sets needed for future data scientist projects.

The Certificate consists of 4 Modules: 

• Basics of Python Programming
• Data Manipulation and Management
• Statistics and Computational Modeling
• Data Visualization

All classes meet 2 nights per week for 20 weeks over the course of 28 weeks for a total of 154 hours of in-class instruction to complete the certificate.

 
For more information on course offerings and to apply, go to www.canlearnsmart.com
You may also contact Nate Watson, director of the academy, at nate@canworksmart.com if you have specific questions about offerings or custom classes. 
 
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