Pragmatic Data

Learn to use data to power strategy.

Data as a Strategic Advantage

Pragmatic Institute’s data practice helps organizations leverage data to drive business success through practical, high-impact training. We provide individuals and teams with actionable guidance, hands-on practice and a business-oriented approach, so they can solve problems and propel decision making with data.

We believe strengthening data and business fluency across an organization is the key to delivering value for the bottom line. Through tailored training options, data bootcamps and in-depth courses, Pragmatic Institute builds expertise within data teams and stronger collaboration with their stakeholders.

New Course
Business-driven data analysis

Business-Driven Data Analysis

Harness the power of your organization’s data with our new course, Business-Driven Data Analysis. Improve your data team’s approach to analysis and stakeholder communication, and empower them to drive business outcomes through critical insights.
  • Translate business needs into strategic data projects
    Your data team will get to the crux of a business problem by asking the right questions to create objective, measurable goals.
  • Accelerate data analysis in your organization with a proven, repeatable approach
    Optimize processes and implement a cohesive approach for working with data so your team can be more effective overall.
  • Shorten the path to receiving actionable insights
    Ensure data analysts use the most powerful yet efficient method to gather and transform data for business needs.
  • Increase harmony among data and business stakeholders
    Your team will articulate the value and impact of their findings for better alignment and collaboration.

Pragmatic Data Analysis Model

Our instructors bring years of experience in data and business to the classroom. Course content is built around the Pragmatic Data Analysis Model, an optimized, repeatable approach for any data project or toolset, such as Python, R, Power BI or Tableau.

  • Define: Focus on the specific business problem you want to solve with data.
  • Prepare: Explore the available data and the most useful methods.
  • Refine: Revise questions and expectations as necessary.
  • Analyze: Build models to find actionable insights.
  • Present: Communicate actionable insights and next steps to stakeholders.

Interested in corporate training? We offer Business-Driven Data Analysis to data teams on time frames tailored to their needs.

The Business of Data

The Business of Data Science

This course provides business leaders from all areas with an in-depth understanding of data science. Uncover the best methods for turning business challenges into profitable data projects, while driving a data-driven culture throughout your company. Start doing more with the data your company collects every day.

Featured Resources

Define: Laying the Foundation for Successful Data Analysis

What are the most vital questions to ask before embarking on a data analysis project? Our in-depth guide ensures data analysts and business stakeholders avoid disconnects by sharing context and establishing clear goals from the outset.

Applying Data Analysis to Strategy: A Proven Approach

Learn why 80% of data science projects fail and how data analysts can communicate practical business solutions in our recent Data Chat. Founder of Barton Poulson and data science advisor Mike Lukianoff discuss the Pragmatic Data Analysis Model, real-world challenges to which the model can be applied, and key takeaways for ensuring data projects add value to the business.

Data Storytelling: Ensure Your Insights Make an Impact

Learn how to craft a narrative around your data to contribute more strategically in our new episode of Data Chats, a podcast by Pragmatic Institute and The Data Incubator.

Pragmatic Institute Data Science Glossary

What is a data custodian? How about a data engineer? From HiPPO to Python, data wrangling to deep learning, our Data Science Glossary defines the industry terms you should know, no matter your role in the data landscape.

Pragmatic Institute Data Advisory Board

Pragmatic Institute’s Data Advisory Board comprises a group of industry leaders and experts who bring a real-world lens to data and business. Our curriculum was developed in close partnership with the board to ensure alumni maintain a competitive edge in an evolving data and business landscape.

  • Neal Fultz
    Principal Data Scientist and Founder at njnm Consulting
  • Alexey Grigorev
    Principal Data Scientist at OLX Group and Founder of DataTalks.Club
  • Christopher Laubenthal
    Data and Visualization Consultant at Lockton Companies, LLC
  • Yacov Lewis
    Co-Founder at Claps and Data Scientist
  • Michael Li
    Founder of The Data Incubator
  • Harish Krishnamurthy
    President at Sciata
  • Eugenie Packer
    Senior Director, Development Systems and Analytics, at the Mount Sinai Health Systems
  • Shilpam Pandey
    Data Program Leader at M&T Bank
  • Barton Poulson
    Founder of
  • Yonas Yohannes
    Cloud CTO, Oracle Cloud Infrastructure, at Oracle

Pragmatic Data Certification

Students receive certification upon completion of each course. Get certified in Business-Driven Data Analysis or The Business of Data Science.

Looking for something different for your data team?

Reach out to our sales representatives for private data training tailored to your team. Based on our conversations with you and your team’s assessment, we’ll create the right training options for you.

Training on Your Schedule

Fill out the form today and our sales team will help you schedule your private Pragmatic training today.

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Define Ebook Thumbnail
What’s the difference between a successful data analysis project and one that falls flat? 

Before you begin working with the data, you need to understand what you’re solving for. Gathering context and aligning around goals with your stakeholders from the outset will help you avoid disconnects and deliver actionable insights. Discover the most vital questions to ask before embarking on a data analysis project in our in-depth guide, “Define: Laying the Foundation for Successful Data Analysis.”

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