Learn Data Science in Business

Experience how to implement Machine Learning for A/B Testing step-by-step

Student feedback led to a BRAND NEW CHEAT SHEET on customer segmentation and a major overhaul to our Week 6 Modeling Chapter in our Business Analysis with R Course.

The first article in a 3-part series on Excel to R, this article walks the reader through a Marketing Case Study exposing the 10X productivity boost from switching from Excel to R.

Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is difficult and not well understood. The issue is twofold, first, the necessary technology to complete such a task has only recently become mainstream, and second, most data scientists are inexperienced in their respective industries. However, with all the uncertainty surrounding this topic, one hedge fund has managed to navigate through these challenges and accomplish what many companies are failing to do: building a high-performing data science team that achieves real return-on-investment (ROI).

Data Science is an amazing field of research that is under active development both from the academia and the industry. One of the saddest facts in the real-world is that most data science projects in organizations fail. Here I’ll present a new iteration of an agile framework called Business Science Problem Framework (Download PDF here) to implement data science in a way that enables decision making to follow a systematic process that connects the models you create to Return On Investment (ROI) and show the value that your improvements bring to the business. The end result is that the BSPF is an agile framework, and we are working to develop a new visualization (BSPF 2.0) that conveys this agility.

The Expected Value Framework connects the machine learning model to ROI. In data science for business, it is critical to quantify the ROI of data science.

Data Scientists want to run successful projects. However, the sad fact is that most data science projects in organizations fail. It’s not because of lack of skill or knowledge. Data science projects need a clear and effective plan of attack to be successful. As data scientists, we study a wide array of tools: advanced algorithms, knowledge of statistics, and even programming skills. However, if you’re like us, you’ve had to learn how to successfully manage a project through trial and error. Fortunately, we’ve learned a lot over the past several years working with clients, and we’ve integrated the best resources into one streamlined framework to make your life easier: The Business Science Project Framework! In this article, we’ll cover the basics showing you how the BSPF helps as a guide for successful data science projects following a Customer Churn Problem example. Download the BPSF for FREE here.

Predict employee turnover using the H2O machine learning and Lime.