Data Science for Business Part 2

Solve a real-world churn problem with H2O AutoML (automated machine learning) & LIME black-box model explanations using R

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Please note that the Shiny Web Application is built in DS4B 301-R: Building A Shiny Web Application (Coming Soon!)

DS4B Part 2 teaches you the tools and frameworks for ROI-driven data science using the R-programming language.

Over the course of 10-weeks you'll dive in-depth into an Employee Attrition (Churn) problem, learning & applying a systematic process, cutting-edge tools, and R code.

At the end of the course, you'll be able to confidently apply data science within a business.

The difference with the DS4B Part 2 program: You get results!

Everything You Need to Become the Data Scientist Level 2 for Your Organization

220 Lessons, 18.3 Hours of Video, and 7 Challenges to Test Your Skills

Who Is This Program For?

We have hundreds of data scientists in the course. Mainly they fall into 3 categories:

  1. Data Scientists In Business: Data scientists seeking to make the link between data science and the business objectives while driving ROI for their organization.
  2. Consultants: Data scientists working for companies in large consulting firms (e.g. Accenture, Deloitte, etc) and boutique consulting firms that are related to enterprise improvement and ROI.
  3. Students: Future data scientists seeking to gain skills beyond their current program offering. Leveraging Business Science University gets you trained on high-demand skills placing you ahead of your peers in the job market.

Cutting Time-To-Deliver Data Science By 50%

Learn how Rodrigo Prado (Managing Partner, Big Data Analytics & Strategy with Genisis Partners) reduced time to deliver projects by 50% after taking Data Science For Business With R (DS4B 201-R) and implementing the Business Science Problem Framework.

A Data Scientist In Business Perspective

A College Student Perspective

What Results Can You Expect?

Whether it's the high-demand tools, the systematic frameworks, or the linkage between data science and business objectives, one thing is certain: Our students are getting results.

Here's what Rodrigo, a high-end data science consultant, had to say:

"Your program allowed me to cut down to 50% of the time to deliver solutions to my clients. Soon I'll enroll all consultants in your program."

-Rodrigo Prado, Managing Partner Big Data Analytics & Strategy at Genesis Partners

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Increase confidence, build critical thinking skills, & take your data science to the next level

How The Program Works

Here's the play-by-play to get you from beginner/intermediate to advanced.


The course takes about 10 weeks to complete. It's an in-depth study of one churn / binary classification problem that goes into every facet of how to solve it. Here's the basic structure of DS4B Part 2.

Week 1: Getting Started

You begin with the problem overview and tool introduction covering how employee churn effects the organization, our toolbox to combat the problem, and code setup.

We introduce the Business Science Problem Framework, which is our step-by-step roadmap for data science project success.

The BSPF is used as guide as you progress through each chapter in the course.

Week 2: Business Understanding

You progress into sizing the problem.

You develop skills with dplyr and ggplot2, critical to exploring data. You are introduced to a new metaprogramming language called Tidy Eval for programming with dplyr.

You use Tidy Eval for the attrition code workflow, building a customizable plotting function to show executives which departments and job roles are costing the organization the most due to attrition.

Week 3: Data Understanding

The goal is to not waste time. You’ll learn two critical packages for exploring data and uncovering insights quickly.

First, you’ll investigate data by data type using the skimr package. You investigate continuous (numeric) and categorical (factor) data.

Next, you’ll investigate data relationships visually using GGally. You uncover key relationships between the target variable (attrition) and the features (e.g. tenure, pay, etc).

Week 4: Data Preparation

Next, you prepare the data for both humans and machines with the goal of making sure you have good features prior to moving into modeling. Again, the goal is to not waste time until we have fully understood the problem and have good features.

First, you use the tidyverse packages to wrangle data into a format that is readable by humans, creating a “human readable” processing pipeline.

Next, you use the recipes package to create a “machine readable” processing pipeline that is used to create a pre-modeling correlation analysis visualization.

The correlation analysis confirms we have good features and can proceed to modeling.

Weeks 5 & 6: H2O Modeling & Performance Analysis

Next, you learn H2O, a high performance modeling package. You spend two chapters with H2O.

In Chapter 4 (modeling), you learn the primary H2O functions for automated machine learning. You generate models including:

  • Generalized Linear Models (GLM)
  • Gradient Boosted Machines (GBM)
  • Random Forest (RF)
  • Deep Learning (DL)
  • Stacked Ensembles.

You create a visualization that examines the 30+ models you build.

In Chapter 5 (performance), you go in-depth into performance analysis. You learn about ROC Plot, Precision vs Recall, Gain & Lift Plots (which are for executive communication). You build the "ultimate model performance dashboard".

Week 7: Explaining Black-Box Models

“The business won’t care how high your AUC is if you can’t explain your Machine Learning models. Explain those models.”

-Matt Dancho, Founder of Business Science

Now, you learn about LIME and how to perform local machine learning interpretability to explain complex models, showing which features contribute to attrition on a localized, employee level.

You'll also have a cool challenge where you recreate the plots with a business-ready theme .

Weeks 8 & 9: Expected Value, Threshold Optimization, & Sensitivity Analysis

Now it’s time to link Machine Learning to Expected Financial Performance. You spend two chapters with on expected value, threshold optimization, and sensitivity analysis.

We start with a basic case of making a "No Overtime" policy change. We then go through Expected Value Framework, a tool that enables targeting high-risk churners and accounts costs associated with false negatives / false positives.

We then teach how to optimize the threshold using purrr for iteration to maximize expected savings of a targeted policy. We then teach you Sensitivity Analysis again using purrr to show a heatmap that covers confidence ranges that you can explain to executives.

Week 10: Recommendation Algorithm Development

“To make progress, you need to make good decisions. Good decisions are systematic and data-driven.”

-Matt Dancho, Founder of Business Science

This is the culmination of your hard work. It’s time to apply critical thinking skills by developing a data-driven recommendation algorithm from scratch.

You will follow a 3-Step Process that shows you how to build a recommendation algorithm for any business problem.

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Virtual Workshop Benefits ($5,000 Value)

A 5-Day On-Premise Machine Learning Workshop with Business Science will cost you individually $5,000 (or an organization $20,000 or more). You get a 10-week machine learning for business training at a fraction of the price. You get:

  • Business Science Problem Framework Training: You learn how to implement the systematic process in your organization to drive ROI.
  • Sizing The Problem, Data Exploration, Preprocessing & Pre-Modeling Correlation Analysis Training: You become confident in how to identify key problems financially, understand the drivers, work with key decision makers, & develop an ROI-driven solution for your organization.
  • Machine Learning Training: You'll learn how to use H2O Automated Machine Learning for a binary classification problem. You learn feature explanation with LIME to explain the key features.
  • Expected Value Training: You learn how to integrate data science with business objectives. You learn threshold optimization which is a critical step in targeting key customers (or those that are likely to purchase by maximizing expected profit). You learn sensitivity analysis to take into account variability in your model parameters.
  • Recommendation Algorithm Development Training: You learn our 3-Step Process for developing a recommendation algorithm for any business problem.

We Didn't Stop There. You Also Get...

Bonus #1: Market Basket Analysis ($995 Value)

As an added bonus, you get a detailed Market Basket Analysis using the recommenderlab R package. You’ll learn how to generate product recommendations using:

  • Collaborative Filtering
  • Association Rules
  • Item Popularity
  • Content-Based Filtering
  • Hybrid Models

Bonus #2: Private Slack Community Channel ($1,995 Value)

We have an exclusive slack channel for students of DS4B Part 2. This is an amazingly useful resource ! Students use it to connect with peers, ask questions, and share data science resources.

Did we mention that Erin LeDell, Chief Machine Learning Scientist at and creator of the H2O AutoML algorithm is in our Private Slack Channel?

No other program has this level of support. Period.

Bonus #3: Instructor Access

Our instructors are experts in data science and machine learning. You have exclusive access to instructors through the Private Slack Channel, email, and lecture forums. This is a great way to ask questions, get mentored, and learn from an expert.

You can connect with Matt! Shoot him an email. He’ll respond quickly.

Your Instructor

Matt Dancho
Matt Dancho

Founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane startups! Matt loves educating data scientists on how to apply powerful tools within their organization to yield ROI. Matt doesn't rest until he gets results (literally, he doesn't sleep so don't be suprised if he responds to your email at 4AM)!

Adding It All Up, You Get...

Summary Of Everything Included

  • 10-Week Data Science For Business With R Program : $5,000 value (compared to 5-Day On-Site Workshop)
    • Business Science Problem Framework Training
    • Sizing Problem, Data Exploration, Preprocessing, & Pre-modeling Correlation Analysis Training
    • Machine Learning Training: H2O & LIME
    • Expected Value Training: Threshold Optimization & Sensitivity Analysis
    • Recommendation Algorithm Development Training: 3-Step Process
  • Bonus #1: Market Basket Analysis ML Tutorial: $995 Value
  • Bonus #2: Private Slack Community Channel: $1,995 Value
  • Bonus #3: Instructor Access: Priceless :-)

Total Value: $7,990

Your Price Today: 👇

*Price excludes local taxes & VAT

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The Ultimate Machine Learning Course For Business

Course Curriculum

  Module 0: Getting Started
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  0.4 Frameworks
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  Module 1: Business Understanding: BSPF & Code Workflows
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  Aside: Intro To Tidy Eval
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  1.6 Module Code Checkpoint
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  Module 2, Data Understanding: By Data Type & Feature-Target Interactions
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  2.1 Setting Up For Data Understanding
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  2.4 Challenge #2: Assessing Feature Pairs
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  2.5 Module 2 Code Checkpoint
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  Course Survey #1: Your Feedback Is Important!
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  Module 3, Data Preparation: Getting Data Ready For People & Machines
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  3.1 Data Preparation Setup
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  3.5 Challenge #3: Correlation Analysis
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  3.6 Module 3 Code Checkpoint
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  Module 4, Modeling Churn: Using Automated Machine Learning With H2O
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  4.6 Code Checkpoint
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  Module 5, Modeling Churn: Assessing H2O Performance
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  5.1 Performance Overview & Setup
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  5.3 Performance Charts For Data Scientists
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  5.6 Modules 4 & 5 Code Checkpoint
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  Module 6, Modeling Churn: Explaining Black-Box Models With LIME
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  6.1 Module 6 Overview & Setup
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  6.4 Module 6 Code Checkpoint
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  Module 7, Evaluation: Calculating The Expected ROI (Savings) Of A Policy Change
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  7.5 Module 7 Code Checkpoint
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  Module 8: Evaluation, Maximizing ROI (Savings) With Threshold Optimization & Sensitivity Analysis
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  8.1 Setup
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  8.8 Module 8 Code Checkpoint
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  Module 9, Evaluation: Creating A Recommendation Algorithm
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  9.8 Module 9 Code Checkpoint
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  Course Conclusion & Next Steps
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  Send-Off Gifts!
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Find Out Why Hundreds Of Data Scientists Are Considering DS4B Part 2 The Best Data Science For Business Course Available

Course Satisfaction Results

We are currently getting an average Course Satisfaction rating from students of

9.0 / 10


We think it's great, but don't just listen to us. Here's what other students have to say about Data Science For Business Part 2.

"Business Science University gives a solid approach to understanding what a Data Scientist needs to do to transform an idea into a full solution, also taking into account that this process must return the investment for the company and add value. Mixing both theory and programming you’ll learn with real-world examples the bulletproof workflow that the successful company founded by Matt Dancho use to do Data Science. This is not another course, this is the ultimate ecosystem for you to develop and improve as a data scientist for your organization."

- Favio Vázquez, Principal Data Scientist, OXXO

"I have been going through books & MOOC's to skill-up my data science game. DS4B Part 2 is the first course that gives me a CLEAR FRAMEWORK to apply data science to Business Intelligence! It gives me the opportunity to bring data science to my organization and clearly articulate the business value proposition throughout the process. All that with the help of bleeding-edge open source tools (H2O, LIME, RStudio)"

- Renaud Liber, Business/Data Analyst - BI, Napoleon Games NV

"Business Science University is an excellent resource for learning data science. The DS4B Part 2 course does a great job of teaching how to communicate a business problem, how to execute investigative thinking to solve the problem, and properly structuring code for collaboration and reusability. Most importantly, I took away a repeatable methodology and project structure that can be used to solve future business problems using data science. This was well worth the investment."

- David Curry, CTO, Africa Talent Management

Sunita Kenner, Senior Manager: Data/Business Analytics at Extensis.

Feedback provided in... R (Awesome!!)

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Employee Attrition: A High-Impact Problem

Employee turnover (attrition) can be a $15M/YEAR COST to an organization that loses on average 200 high performing employees per year. Predicting turnover is at the forefront of Human Resources (HR) needs in many organizations. Further, HR departments typically have historical data on employees making this a perfect problem for DATA SCIENCE FOR BUSINESS.

Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition.

In Data Science For Business (DS4B Part 2), you'll learn how to:

  • Use People Analytics (Human Resources) data to predict and explain employee turnover
  • Implement the Business Science Problem Framework and CRISP-DM to tackle any organizational data science problem
  • Perform automated machine learning with H2O
  • Explain complex, black-box machine learning models with LIME

Lifetime Access Gets You

  • A complete walk-through of an end-to-end data science project by solving a real-world problem
  • A play-by-play strategy to yield Return-On-Investment (ROI) for your company
  • Hours of expert instruction in how to apply data science for business from the Founder of Business Science
  • PDF Frameworks & Excel Calculators & Worksheets that gain buy-in from Executives when pitching your Data Science Project
  • Access to our Slack Channel Community for asking data science questions & discussing the course!


Refer to the free Test Your Baseline Knowledge Check in the Class Curriculum to determine your fitness for this course. As a prerequisite, the learner is expected to:

  • Be familiar with the R statistical programming language (e.g. have R setup on computer, have RStudio IDE working, have basic familiarity with R programming language)
  • Be familiar with the tidyverse (e.g. basic knowledge of dplyr and ggplot2)

Everything else will be taken care of!

Business Discounts

Please contact Business Science to find rates for multiple users & organizations.

Frequently Asked Questions

Who is this course for?
This course is for anyone with R programming experience seeking to apply data science for business (DS4B). It's not for complete beginners! With that said, a basic understanding of R, dplyr, and ggplot2 will be sufficient to complete the course. Although the concepts are advanced, the hard stuff is explained such that a novice/intermediate learner will pick it up!
Should I take this course even though it deals with an Employee Attrition Problem, which is not in my domain?
Absolutely. The course uses a real-world example of an HR problem, which may not be specific to all Data Science For Business (DS4B) needs. However, the system and tools used are applicable to ANY BINARY CLASSIFICATION PROBLEM (for example, customer churn, fraud detection, any yes/no problem!). The real value is in the tools and techniques used - You will learn our process along with advanced tools!
Will this course be beneficial if I have a non-traditional background (e.g. Sales, Finance, Sociology, Marketing, Operations, Classical Music)?
Look, my background is non-traditional (mechanical engineering). If I can do it, you can do it. As long as you are (1) interested in data science, and (2) interested in applying it to business, then you are the right candidate. You should however take a basic course that teaches R, dplyr, and ggplot2 so you have the minimum skillset. Refer to the quiz: Test Your Baseline.
What will I learn beyond the basics?
You will learn a ton: H2O, LIME, recipes, and much much more! In addition to the course overview, the course has free-previews. Take a peek and see if you like the content.
I am finishing my degree. Will this course help me?
Yes. The course bridges the gap between academic data science and real-world data science in a business context. This makes it excellent, if not essential, to your ability to hit the ground running when you transition into an organization.
When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
Are these courses self-paced, asynchronous?
All courses are completely self-paced. You can take them on your own schedule. The content uses pre-recorded video, and we will handle comments as we receive. The courses can be taken asynchronously.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
Will the course continue to be updated with new content?
Yes, I will be actively improving to make sure the content is perfect! Your membership includes lifetime access as the course evolves.
What is the geographic availability for the course? Will it work outside US?
Yes - The course can be taken from virtually any geographic location that is permitted to trade with the US. This should cover 99.9% of the world's population.
I love the course, but I can't afford it. What options do I have?
The value the course will deliver is exponentially more than its price but you have two options. First, your employer may offer education assistance. This is highly recommended because the education will ultimately benefit them... financially! (See the $15M/year problem lecture). Second, if self-funded, there are payment plans available. And view this as an investment. What you are getting will help you get a job, develop a portfolio of experience using cutting-edge tools and processes, and manage a data science project the way we do.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund. Signup is risk-free!

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