Please note that the Shiny Web Application is built in DS4B 301: Building A Shiny Web Application (Coming Soon!)
DS4B 201 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 program: You get results!
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!
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:
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)!
The feedback provided in the initial student survey has a few consistent themes for what the students love:
Engage with the instructor (Matt) and other students in the course via our private Slack Workspace.
Increase confidence, build critical thinking skills, & take your data science to the next level
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 (HR 201), you'll learn how to:
The Ultimate Machine Learning Course For Business
Learn how to apply the Business Science Problem Framework & CRISP-DM through an end-to-end data science for business project:
As a data scientist you need to be able to build custom functions to get things done. Learn Tidy Eval, a new programming framework for dplyr and other tidyverse packages, to build reusable functions that the data science team can scale.
Learn H2O, a high-end machine learning library, by building an extremely predictive machine learning employee turnover classifier.
In business, it's more important to know why something happened rather than the prediction itself. We learn LIME to develop these explanations and interpret complex models..
Turn raw data into ML-ready datasets with recipes.
Understand data by data type using skimr, and visualize interactions with GGally.
Apply Correlation Analysis to understand feature importance prior to modeling.
After taking this course, you'll have an excellent understanding of the data science process and how you can immediately apply within a business context to yield positive ROI for your organization.
ENROLL NOW TO LEARN HOW TO CONFIDENTLY APPLY DATA SCIENCE FOR BUSINESS
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 (HR 201).
"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. HR 201 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 HR 201 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!!)
The content for this course is being released following a drip schedule. The first 50% of the course is available at launch. Subsequent lessons are to be released following the schedule outlined in the Class Curriculum.
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 / HR 201 (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:
HR 201 is the first course in the 4-Course Virtual Workshop, and HR 201 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 HR 301 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 HR 302, 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 HR 303. 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!