Business Science



Improve Your Data Science Skills

Learn Data Science For Business

The enterprise-grade process of solving business problems with data science and machine learning

Learn Data Science And Business Together

Learning to achieve business objectives using data science enables you to become a strategic asset to your organization. We combine expert instructors, high-performance tools, and long-format courses that teach end-to-end data science projects with a focus on Return-On-Investment (ROI). This gets results.

Choose A Language To Specialize In...

Data Science is about learning the right tool for the job. Each have pros and cons, and you should have options. We offer multiple tracks depending on your programming language of choice.

...Or master all of them!

Data Science Tracks

Track

Virtual Workshop Overview

(R Track)

Data Science For Business Course Virtual Workshop

The Virtual Workshop is an integrated collection of courses that teaches you 90% of the skills you will need to be successful as a data scientist within an enterprise. The central course teaches the critical analysis. The extension courses target critical skills such as web applications, communication, and package development.

Data Science For Business With R

(DS4B 201-R)

DS4B 201-R is a ground-breaking course that takes students from beginner to advanced in 10 weeks! The student evaluates one problem in-depth learning all of the critical aspects of applying data science and machine learning to business. Students consistently rate a 9 of 10 in course satisfaction!

Python Track

Data Science For Business With Python

(DS4B 201-P)

Data Science For Business Course Virtual Workshop

DS4B 201-P is the Python equivalent of our ground-breaking DS4B 201-R course. The student learns Python, Apache Spark (great for Big Data), and other tools taking them from beginner to advanced in 10 weeks! The course is under construction but targeted for Q4 2018.

Organizations We Help

Organizations We Service

Our Expertise


See Us In Person


Upcoming Events
  • Oct 12, 2018: Data Science GO Conference 2018

    Matt is giving a workshop on Machine Learning with H2O and a talk on Data Science For Business With Applications In R And H2O. Come see the fun and learn our tools and systems for success!

Past Events

What's New?


Latest Insights
    IML and H2O: Machine Learning Model Interpretability And Feature Explanation
    Written by Brad Boehmke on August 13, 2018

    Model interpretability is critical to businesses. If you want to use high performance models (GLM, RF, GBM, Deep Learning, H2O, Keras, xgboost, etc), you need to learn how to explain them. With machine learning interpretability growing in importance, several R packages designed to provide this capability are gaining in popularity. We analyze the IML package in this article.

    Read More...
    Kaggle Competition In 30 Minutes: Predict Home Credit Default Risk With R
    Written by Matt Dancho on August 7, 2018

    We were very excited when Home Credit teamed up with Kaggle to host the Home Credit Default Risk Challenge. Default risk is a topic that impacts all financial institutions, one that machine learning can help solve. We decided to flip the goal of this challenge: Kaggle competitions are performance driven, where a data scientist has months to fine tune a model to get maximum performance. This is not reality. We turned this goal upside down, focusing on a combination of speed, efficiency, and performance.

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    DALEX and H2O: Machine Learning Model Interpretability And Feature Explanation
    Written by Brad Boehmke on July 23, 2018

    As advanced machine learning algorithms are gaining acceptance across many organizations and domains, machine learning interpretability is growing in importance to help extract insight and clarity regarding how these algorithms are performing and why one prediction is made over another. There are many methodologies to interpret machine learning results (i.e. variable importance via permutation, partial dependence plots, local interpretable model-agnostic explanations), and many machine learning R packages implement their own versions of one or more methodologies. However, some recent R packages that focus purely on ML interpretability agnostic to any specific ML algorithm are gaining popularity. One such package is DALEX and this post covers what this package does (and does not do) so that you can determine if it should become part of your preferred machine learning toolbox.

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    Press Release: Business Science Partners With Method Data Science To Accelerate Your Data Science Career
    Written by Matt Dancho on July 18, 2018

    The goal is simple: to educate and empower future data scientists so they can help organizations gain data-driven results. This is why it was a no-brainer when the opportunity came up for Business Science to partner with Method Data Science, the go-to data science accelerator for aspiring data scientists. Now Method Data Scientists will get exclusive lectures from Business Science Instructors and have discounted access to Business Science University, the revolutionary online education platform for learning data science for business, along with instructor trainings as part of the Method Data Science accelerator program. This is big news for current and future data scientists seeking to gain real-world experience while learning how to deliver results to organizations!

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    New Course Content: DS4B 201 Chapter 7, The Expected Value Framework For Modeling Churn With H2O
    Written by Matt Dancho on July 16, 2018

    I’m pleased to announce that we released brand new content for our flagship course, Data Science For Business (DS4B 201). Over the course of 10 weeks, the DS4B 201 course teaches students and end-to-end data science project solving Employee Churn with R, H2O, & LIME. The latest content is focused on transitioning from modeling Employee Churn with H2O and LIME to evaluating our binary classification model using Return-On-Investment (ROI), thus delivering business value. We do this through application of a special tool called the Expected Value Framework. Let’s learn about the new course content available now in DS4B 201, Chapter 7, which covers the Expected Value Framework for modeling churn with H2O!

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