Predictive Analytics: Turning Big Data into Smart Data
Todd Cioffi
Conquering the throughput, capture, and storage challenges of Big Data is only the beginning. Once you have a handle on the input, how do you turn that into actionable output? How do you change your mindset from reviewing your past to charting your future?

Simply put: How do you turn your Big Data into smart data?

In this class, we’ll begin with seeing that the critical step in using data analysis to find the right answers is to ask the right questions. We will explore the kinds of questions that people commonly ask, and propose a different style of questions that focus on what will happen, instead of what has already happened. Using real-world examples of tracking customer churn and social media sentiment, you will learn how to use predictive analytics models to forecast which high-value customers you are most likely to lose, or what sentiments might gain momentum in the future. From there, we will see how that information can be used to best prepare resources and develop strategies to retain customers and drive positive sentiment.

We will also review tips and tricks that resolve common issues that can skew reports, visualizations, and recommendations, including false positives, false negatives, and outliers in the data. With the ever-increasing size of available data sets, issues like these present more of a challenge if not quickly identified and handled, allowing for more accurate predictive models.

Note: Prior knowledge of some data analysis is helpful, but no experience in predictive modeling is assumed or required.

Level : Intermediate