Bundle - Machine Learning For Business: R-Track - DS4B Parts 1 & 2
Go from A to Z in Data Science & Machine Learning for Business with R.
Get two of our flagship R courses that work in tandem to take you from A to Z in ML for Business
Learn the foundations of data science and how to apply machine learning to return business value
Taking these two courses is the equivalent of:
100+ tool-based courses
3 end-to-end projects
4 frameworks
1 foundational data science education
1 machine learning education
Courses Included with Purchase
Original Price: $1,694
Price for Bundle: $799
That's a savings of $99 when you purchase both!
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Go from beginner to advanced in data science as fast as possible.
Here's what's included.
#1. Business Analysis with R (DS4B 101-R)
A 7-week curriculum that methodically teaches the foundations of data science using R & tidyverse. You will learn:
- Data Import: readr & odbc
- Data Cleaning & Wrangling: dplyr & tidyr
- Time Series, Text, & Categorical Data: lubridate, stringr, & forcats
- Visualization: ggplot2
- Functions & Iteration: purrr
- Modeling & Machine Learning: parnsip (xgboost, glmnet, kernlab, broom, & more)
- Business Reporting: rmarkdown
#2. Data Science For Business with R (DS4B 201-R)
A 10-week curriculum that incorporates R & H2O AutoML to use machine learning within a business problem solving framework called the BSPF. You will learn:
- Getting Started: Business problem foundations, introduction to the BSPF
- Business Understanding: Using dplyr & ggplot2 to size the business problem // tidy eval to build custom functions that fit within the tidyverse
- Data Understanding: Use skimr and GGally packages to efficiently visualize key relationships
- Data Preparation: Use recipes to prepare data in both human and machine readable formats // perform preliminary correlation analysis
- H2O AutoML Modeling & Performance: Use Automated Machine Learning (AutoML) to produce 30+ models // analyze performance using ROC, Precision/Recall, Gain & Lift plots
- Explaining Black-Box Models: Use LIME to explain which features are driving the complex deep learning & stacked ensemble models
- Expected Value, Threshold Optimization, & Sensitivity Analysis: Link the model to financial performance through the Expected Value framework
- Recommendation Algorithm Development: Use a 3-step process to develop a recommendation algorithm capable of assisting managers in retaining employees
After taking both courses you will be able to confidently apply machine learning and data science within a business
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Your Instructor
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)!