Please note that the Shiny Web Application is built in DS4B 301R: Building A Shiny Web Application (Coming Soon!)
DS4B 201R 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 201-R program: You get results!
This short presentation overviews the DS4B 201-R program.
We have hundreds of data scientists in the course. Mainly they fall into 3 categories:
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
Increase confidence, build critical thinking skills, & take your data science to the next level
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 201-R.
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.
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.
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).
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.
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:
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".
“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 .
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.
“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.
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:
We Didn't Stop There. You Also Get...
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:
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.
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.
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...
Total Value: $7,990
Your Price Today: $395
The Ultimate Machine Learning Course For Business
Sample Lecture from Chapter 1, Business Understanding: BSPF & Code Workflows
Sample Lecture from Chapter 6, Modeling Churn: Explaining Black-Box Models With LIME
There are 100+ coding courses like this that walk you through the process of applying data science to the business problem!
Find Out Why Hundreds Of Data Scientists Are Considering DS4B 201-R The Best Data Science For Business Course Available
As of July 9, 2018, 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 With R(DS4B 201-R).
"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 201-R 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 201-R 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!!)
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 201-R), you'll learn how to:
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:
Everything else will be taken care of!
Please contact Business Science to find rates for multiple users & organizations.
We use a hub-and-spokes model. DS4B 201-R (200-series course) is the hub that serves as the base for each extension (300-series courses). This maintains a consistent theme across multiple courses by using the same business problem while focusing on the tools that data scientist's need to use in their day-to-day work.
There are several advantages to the hub-and-spokes model:
DS4B 201-R is the first course in the 4-Course Virtual Workshop, and DS4B 201-R is what you get when you purchase this course. The release schedule for the others is TBA (to be announced). More information is coming!
A data scientist can never stop learning. When this happens, plateau sets in, which is exactly what you and your organization cannot afford! (This is why Business Science provides data science coaching as a service!)
Continue with the rest of the Virtual Workshop to exponentially multiply your learning!
The most effective means of improving your organization is by helping others make data-driven decisions.
A Machine Learning-Powered Web Application is 100% the best way to do this. (Trust us, we've seen the change it makes in an organization.) Building a Machine Learning-Powered Web Application is easier than you think with Shiny!
You can further your capabilities by taking our integrated DS4B 301-R course, which implements our H2O model in a Shiny Web App for interactive employee attrition prevention recommendations. We call it the Employee Smart Scorecard!
Executive communication makes or breaks a data science project. Further, data science can be extremely valuable in customer communication.
In DS4B 302-R, you'll use RMarkdown to communicate the story through reports and presentations designed for your target audiences: executives (global decision makers), managers (local decision makers), and data science peers (reproducers / reviewers). Additionally, you'll learn about parameterized Rmarkdown reports, which is perfect for automated reporting.
Data scientists need to be able to create packages to simplify workflows and to keep the Data Science Team's analyses consistent.
Build an R package, tidyattrition, in DS4B 303-R. The tidyattrition package follows the workflow developed in the Business Understanding phase. Learn to turn custom tidyeval functions such as assess_attrition(), calculate_attrition_cost(), and plot_attrition() into an R package that others can use!