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

How The Program Works

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

Overview

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.

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

Week 2: Business Understanding

Week 3: Data Understanding

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

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.

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.

• 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 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

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:

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 #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.

Adding It All Up, You Get...

Find Out Why Hundreds Of Data Scientists Are Considering DS4B Part 2 The Best Data Science For Business Course Available

Testimonials

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.

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

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

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

Prerequisites