Image Mask

Bridget Lillethorup

Technical Writer

bridget@canworksmart.com

Bridget writes much of CAN's website copy and helps optimize SEO. Her background in creative writing and history makes her ideally suited to convey some of our complex technical projects into reader-friendly copy.

Like what you see? Connect with Bridget.

Related

Where in the world CAN you find us?

on May 23

Check out Data Science Central

on May 11

We found this article on Interface’s blog. We thought it was an awesome story about how Interface’s web school turned a busy woman’s career around. Despite obstacles of daily life, Miranda Tharp jump-started a web development career.

DSC_4754-300x300

To read the full article about, click here.

Interface partnered with CAN at the end of last year to create The Omaha Data Science Academy. With a certificate from the Data Science Academy, skilled professionals can boost their resumes with additional real life experience.


Related

Machine Learning Upset Prediction Project Proves its Value

on March 27

10 Questions to Ask Before Buying Sales Leads

on April 7, 2014
The Tableau data visualization above, found at Tableau Public, shows the “Top 100 Songs of All Time Lyrics”. Click here to hover over each square and see what words were used in which lyrics. Tableau is a software that converts data into graphs, charts, and images.

 

CAN’s data scientists love sorting through piles and piles of spreadsheets and numerical data, but it’s not for everyone. There are some amazing tools that convert raw data into visualizations. They help bring out the story of data, so everyone can understand it.

Here’s an old favorite from our blog about the importance of visualization. It’s a way for us at CAN to gear up for the next round of Tableau students at the Omaha Data Science Academy!

We are still accepting applicants for the third round of the Oma-DSA! You can apply here. We accept applications until three weeks before the start date, and start a waiting list after the spots are filled. 

 

Why Visualizing Data is Important


Related

From the archives “Why Become a Data Scientist?”

on July 11

The Advantage of Hiring an Hourly Data Scientist

on January 18

Contemporary Analysis 2017 ebook coming soon!

In celebration of CAN’s forthcoming 10th birthday, we’ve decided to bring out the best ideas from our blog and combine them into an educational ebook. The posts were originally written by some of CAN’s most notable alumni, many of whom have gone on to start their own businesses in data science.

At CAN, we are all about education. We believe in educating our employees through hands on experience and through courses offered at the Data Science Academy. We also believe in educating our clients about who we are and what we do. We want our clients to understand the systems we put in place. We’re proud of our work. What follows is a six step model on how to implement data, taken from our new ebook to be released soon. We hope you enjoy it, and learn something too.

 

CAN’s Best Practices for Implementing Data Science

  1. Define a company’s mission, vision, and values. We want to know how they do business; what values they have that are unique and permanent even when the strategy changes. This understanding set the priorities and filters that guide future discussions.
  2. Define a company’s goals. Goals have clear beginnings and ends and typically are accomplished in less than a year. Goals should be in alignment with the company’s vision for the future, and should be accomplished in a way that adheres to the company’s values.
  3. Define the business question to be answered. The business question is about business process improvement, and should not involve technology or research questions. When answered a business questions should have a noticeable impact on at least one of the three parts of a business; sales, operations and administrative support.
  4. Determine what resources are available. This includes political approval, availability of necessary data, and determining research methodology.
  5. Determine how the models will be implemented. Formal Reports help our clients understand the nuances and details of our research. Marketing Summaries provide our clients with colorful and easy to understand summaries of our research.Visual Dashboards help our clients quickly get the up to date information that need to run their operations. Workflow Integration provides our clients with the ability to use our research to impact the activities and operations of large number of people through the systems they are currently using.
  6. Evaluate the model. Does the model answer the intended business question? Does the model produce results that reflect reality? Does the model produce the expected results?

 

Keep your eyes out for our new ebook. For more information and great ideas, contact Nate Watson (nate@canworksmart.com) or Bridget Lillethorup (bridget@canworksmart.com). 


Related

From the archives “Why Become a Data Scientist?”

on July 11

A Celebration of CAN’s Best Ideas

on February 11

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://canworksmart.com/case-study/.


Related

Happy Fourth of July from CAN

on July 4

Bloggers Writing About Tableau

on June 27

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.


Related

Happy Fourth of July from CAN

on July 4

Bloggers Writing About Tableau

on June 27

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.


Related

Happy Fourth of July from CAN

on July 4

Bloggers Writing About Tableau

on June 27

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. 


Related

Predictive Analysis: What Is It And How Can It Benefit You?

on January 25

The Omaha Data Science Academy

on June 22, 2016

Investing in employees means more than just treating them well by giving them benefits and a flexible schedule. It means putting time and resources into individuals who have potential for greatness, but may need a little guidance.

 

More and more companies find it strategic to invest in their employees, even if it means allowing them to move on to greener pastures when the time comes. These companies care about the personal and career growth of their individual employees. They don’t back away from new talent who may need a little training. They see opportunity in new hires, unlike most who are afraid to waste money on training.

 

One such company is Contemporary Analysis (CAN). CAN provides predictive analytics to businesses with needs to unlock patterns in their data. President Nate Watson committed to investing in employees from the beginning. The result of his investments is the substantive alumni network of CAN.

 

Alumni networks are the metaphorical badges of honor for companies committed to their employees’ growth. Employees may move on to other businesses or start their own companies, but they continue to maintain positive relationships with companies like CAN who invested so much in their future. Past employees reach out to the network for advice, giving them a makeshift peer network even when they are the only data scientist in their company.

 

Watson comments, “By investing in our employees’ future, we get people not only willing to go the extra mile for us, but access to employees who have the tenacity to figure out how to solve problems. We lose really good talent, but a lot of the contracts we have right now are from companies that have our former employees in them.”

 

The opening comic portrays the decision to invest in employees perfectly: CFO: “What if we train them and they leave?” CEO: “What if we don’t…and they stay?” Companies like CAN know that whether employees stay or leave, resources are not wasted.

 

CAN doesn’t wait for the most highly trained analysts to walk through the door. CAN nurtures potential with patience for greatness.

 

Some companies make the switch to investing in their employees years after they begin operations. For CAN, commitment to training employees is embedded in their DNA.

 

Nine years ago, the same people who rely on data analysis to keep their companies moving today had never even thought of hiring a data analyst. This was during the recession. As a result, a lot of creative people with experience in data science were out of work.

 

Grant Stanley is the former CEO of CAN. Stanley saw the small pool of highly driven, intelligent, but under employed people as something special–something with which he could start a company.  

 

He then built CAN, the now 9 year old predictive analytics company. Stanley and his non-traditional co-workers would approach companies like Mutual of Omaha or West and say, “Give us the hardest problem you have and let us have a crack at solving it.” Many times they would get an opportunity because a solution was already tried by the “regulars” and failed. Giving CAN a chance provided no risk and very little outlay of cash.

 

Stanley is now CEO of Bric, a software company originally designed for small companies that now helps Fortune 500 companies plan and project manage using predictive analytics.

 

CAN approached the solution differently even from day one. Many times it felt like crash a course in learning the models necessary to succeed. However, CAN’s employees were already good at learning. They looked for solutions, sometimes in other verticals and industries, and applied that knowledge back to the original problem.

 

They found success even though they were younger and less funded than some of their well known competition. This problem solving ideology has become a cornerstone of how CAN does business, even to this day.

 

After years of solving difficult problem after difficult problem, the young data scientists were well trained. They now had resumes to qualify for more prestigious positions, even CEO or management positions. CAN learned to cope with employees moving on. They started an alumni network to capture the excitement of the “graduation” of the employees. The alumni network now boasts 15 former employees in 13 companies.

 

Nate Watson maintains the same mindset of investing in employees for CAN today.

 

If you look at CAN now, Watson has changed very little. Yes, CAN today has more resources and more consistent work, but their motto still reflects their passion to make businesses better: “Empower the great to build something greater.” This is not only true for how they work with clients, but also how they treat their employees. They aren’t afraid to smile and wave goodbye as their best employees seek other opportunities. That’s why they have such a strong alumni network.

 

In fact, last month CAN announced the start of the Omaha Data Science Academy (Oma-DSA), the ultimate goal of which is to train and place a data scientists in every company in Omaha. Their goal is not to replace the four year degree, but provide training for those who need an extra push before they become entry-level data scientists.The DSA’s motto of “Building Smarter Talent” likens back to Watson and CAN’s original mantra. 

 

Further on down the road, of course, there will be an alumni network for the Data Science Academy. Watson comments, “We want each cohort to be able to connect every cohort as they move between companies and up in each company. Having that peer network is going to be key to the success of graduates.”

 

Watson hopes that the Oma-DSA will inspire even more businesses to invest in their employees.

 

–The Oma-DSA’s Alpha class starts September 19th–the graduation party, already RSVP’d by some of the most forward thinking companies in the surrounding area, will be on December 8th.

 

CAN isn’t the only business with an impressive alumni network. Strong alumni networks like CAN’s, however, do seem unique to the tech world.

 

Aron Filbert at Lyconic is proof of this. Lyconic provides software designed to improve security guard management. Lyconic’s products are proven to increase accountability and decrease turnover rates among guards. 

 

Filbert needed a talented software developer to create Lyconic. However, he knew that he could not compete with the corporate world in pay. He gave his employees other perks to make up for this, like casual dress, time off, a lax work schedule, fluidity in moving up at Lyconic, and so on.

 

These other perks, in particular Filbert’s commitment to train and grow with his employees, helped Lyconic build a strong alumni network. Filbert’s greatest success story is a man named Carl Zulauf. He worked for Lyconic for a little over one year, gaining valuable experience alongside Filbert.

 

He moved on to a start-up company in New York City where he was compensated very well.

 

Filbert speaks of Zulauf with pride, not resentment. This is what makes Lyconic and CAN’s alumni network unique to growing tech world: they believe in each other, and that leads to success for everyone. Most businesses only care about the success of their company, not the growth of individuals.

 

Three significant results of having an alumni network.

 

Companies use their alumni networks in different ways. Since the beginning, CAN noted three positive and long-lasting results for their alumni network.

 

The physical result: CAN’s alumni network includes some of the biggest names in Omaha as well as some of the most promising startups. Names like HDR, Avantas, TD Ameritrade, Kiewit, Flywheel, and even Ebay are sporting former CAN employees. As well as 4 founders of data science software companies: Eric Burns at GazellaWifi, Luis Lopez at Crumb, Grant Stanley at Bric, and James Rolfsen at Kojuba. With each movement out, a vacancy occurs that can be filled is filled by new employees that need only training and an opportunity.

 

The social impact: The alumni network is an active and working peer connection hub. Former employees of CAN left on positive terms which means they still keep in touch and occasionally ask for help on projects or advice on business moves. The reverse is true as well: CAN doesn’t hesitate to reach out to the alumni network with questions and advice.

 

The emotional fulfillment: As Watson continues to invest in his employees and sees other companies like Lyconic do the same, he feels a deep sense of pride for the community CAN helped build.

 

As more and more companies make the switch to investing in employees, what does the future of business look like?

 

Investing in employees allows companies to retain the value of each individual, even after they are gone. As more businesses decide that money spent on staff training is money well spent, their pool of resources grows beyond their own company. Their former employees continue to have value in an alumni network. In a sense, by investing in employees you never really have to cut the cord when they move on from your business. This doesn’t get rid of competitiveness between companies completely, but it does allow different businesses to act as support for each other’s growth.

 

Watson of CAN ends with this note, “Investing in people will always be my personal mantra. I hope it continues to permeates the atmosphere of CAN, the Oma-DSA, and the Data Science Community long after I’m gone.”

 

You learn. You move up. You let go. You come back and talk shop. You train someone else. You maintain connections and continue to encourage each other. That’s CAN. That’s the Oma-DSA. That Lyconic. That’s all companies committed to individual achievement. That’s the beauty of investing in employees.



Looking for something?