Learn about the ground-breaking, enterprise-grade machine learning program that is helping data science consultants, data scientists in business, and data science students connect data science with the business. See how the 10-Week program works including features and bonuses that make this the exclusive program of choice for enterprise-grade machine learning education.

1. Data Science For Business
With R
(DS4B 201-R)
The Enterprise-Grade Machine Learning Program
Powered by
Business Science University
university.business-science.io

2. About The Course
• 10-Week Program
• Machine Learning With R, H2O, LIME
• In-Depth Study
• Employee Churn
• $15M / Year Problem
university.business-science.io

3. Why take the course?
• Links Data Science to Business
Objectives
• ROI-Driven Methodology
• Teaches what other programs
miss
• Our ML course gets results:
• Project times cut in half
• Machine learning skills improve
• Connects business-to-data science
university.business-science.io
Consultant Testimonial

4. How it works
university.business-science.io
Getting Started Business
Understanding
Data
Understanding
Data
Preparation
Modeling &
Performance
Machine Learning
Interpretability
Link Data Science To
Business
(Expected Value)
Recommendation
Algorithm
(Decision Making)
Week 1 Week 2 Week 3 Week 4
Week 5-6 Week 7 Week 8-9 Week 10

5. How it works
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.
university.business-science.io
Chapter 0: Getting Started

6. How it works
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.
university.business-science.io
Chapter 1: Business Understanding

7. How it works
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).
university.business-science.io
Chapter 2: Data Understanding

8. How it works
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.
university.business-science.io
Chapter 3: Data Preparation

9. How it works
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 GLM, GBM, Random Forest, Deep
Learning, & 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".
university.business-science.io
Chapters 4 & 5: H2O Modeling &
Performance

10. How it works
“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.
university.business-science.io
Chapter 6: Explaining Black-Box Models

11. How it works
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.
university.business-science.io
Chapters 7 & 8: Expected Value, Threshold
Optimization, & Sensitivity Analysis

12. How it works
“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.
university.business-science.io
Chapter 9: Recommendation Algorithm
Development

13. Bonus #1: Market Basket Analysis
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
university.business-science.io
Bonus ML Tutorial: Make A Product
Recommendation Algorithm
($995 Value)

14. Bonus #2: Private Slack Channel
We have an exclusive slack channel for students of DS4B
201-R. 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 H2O.ai and creator of the H2O AutoML algorithm
is in our Private Slack Channel?
No other program has this level of support. Period.
university.business-science.io
Private Slack Community Channel
($1,995 Value)

15. 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.
You can connect with Matt! Shoot him an email. He’ll
respond quickly.
university.business-science.io
Connect Your Expert Instructor
($250/hour)

16. Summary Of Everything Included
 10-Week Data Science For Business With R Program: $20,000 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: $250/hour value
university.business-science.io
In this Limited Bonus Package, you get:
Total Value: $22,990
Your Price Today: $395
JOIN THE COURSE NOW

17. What are you waiting for?
university.business-science.io
Data Science For Business With R
(DS4B 201-R)
START LEARNING TODAY