Skip to content


Opportunities for individuals with data analytics and big data skills continue to expand even in our highly competitive workforce. Why is that? Nearly all businesses collect data about their operations and examine this data for insight into performance improvement. As the amount of business data becomes increasingly large, insights from the data can no longer be effectively derived manually. Skilled data analytics practitioners are needed to exploit the data’s potential and drive the improvements that keep companies competitive. According to McKinsey,

"The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data."

To fill these positions, XTOL — along with sister company, Schank Academy — launched the Data Analytics Academy to offer a unique Data Analytics/Big Data Certificate designed by current and former faculty of Carnegie Mellon, Northwestern, and Yale Universities. Our program is not aimed at computer scientists, engineers, and statisticians, but at a broader range of people who want to learn to use powerful statistical machine learning tools and big data infrastructure to extract actionable business insights from mountains of business and other data. Plus, our program’s unique learn-by-doing approach teaches practical job skills that can put to use immediately.

 In addition to offering certificate programs, XTOL partners with companies to provide in-house training in Data Analytics and Big Data. Please contact us about tailoring programs to meet your specific needs for up-skilling current employees.

Our achievements

The ones who are crazy enough to think they can change the world, are the ones that do.

Principles of our work



We believe quality is the degree to which something meets or exceeds established standards or expectations. It encompasses the overall excellence, reliability, and performance of a product, service, or process. It is achieved through rigorous adherence to predetermined specifications, continuous improvement efforts, and a focus on customer satisfaction.



We believe integrity is the adherence to moral and ethical principles, demonstrating honesty, transparency, and consistency in actions and decisions. It involves maintaining strong moral character and acting in alignment with one’s values, even when faced with challenges or temptations. 



We feel strongly that innovation is the creation and application of new ideas, processes, or products that bring about significant advancements or improvements. It involves thinking creatively, embracing change, and taking calculated risks to develop novel solutions that address existing problems or meet emerging needs in society.

Not sure what fits your business needs? Get free consultation!

Experience you can trust, service you can count on. 

Where Bootcamps Get It Wrong

Data Science bootcamps may work for those that just want to learn some tools. What these fast-paced programs lack is the lessons and experience of developing business value from data sources. For most people this crucial skill can only be taught on a longer timeline. Students who desire a full mastery of deriving business value from data analytics should look beyond bootcamps.

Problems with Data Science bootcamps:

  • They needlessly require substantial backgrounds in programming and statistics as prerequisites for admission
  • They take a programming approach to data analytics when the profession is so much more. Lots of good jobs are available to people who can define problems appropriately and use a host of powerful tools to solve them
  • They are too short and, thus, too intense; 10-12 weeks is simply not enough time to learn a completely new set of skills
  • Their curricula are not designed by pedagogical experts; the curricula typically are composed of lectures (which are not an effective vehicle for learning) and a collection of ad hoc projects
  • Mentoring also tends to be ad hoc; a student’s experience is all too often completely dependent on the quality of the mentor he or she is assigned
  • Programs tend to be very expensive, especially considered in terms of the amount students learn given what they pay