AITP Chicago AI SIG Meeting on Predictive Analytics
Topic: How Predictive Analytics is Transforming the Future of Business
Predictive analytics refers to the collective use of statistical algorithms, data mining, machine learning, and predictive modeling to analyze transactional and historical data and forecast future outcomes. Despite sounding like something out of a science fiction novel, the use of predictive analytics can be traced as far back as the 17th century.
Nowadays, advanced predictive analytics techniques have become part of mainstream business, enabling organizations to leverage big data in order to proactively identify risks and opportunities. Modern technology has made predictive analytics more accessible than ever before, and the global predictive analytics market is projected to reach approximately $10.95 billion by 2022.
In his presentation, John will explore the world of predictive analytics — how it works, various predictive analytics techniques, examples by industry, and more.
- How Predictive Analytics Works
- Predictive Analytics Modeling Techniques
- Benefits to Using Predictive Analytics
- Predictive Analytics Examples by Industry
- Prescriptive Analytics: The Next Frontier
The presentation will be an overview of the predictive process sprinkled with ‘nuggets’ like distributed data processing (Spark as foundational), like insuring you have a simple means for putting models into production, and for pursuing models that your customers can interact with directly.
Speaker: John Young, VP – Head of Data Science and Machine Learning at Hitachi Solutions America
Practice lead for Data Science and Machine Learning. Work with business clients to envision and frame Use Cases to increase revenue, improve operations, and enhance customer experiences. Developed a complete suite of data science services to craft an analytics strategy, modernize your data environment, build and deploy pipelines, and implement advanced models requiring distributed workloads.
Hands on data scientist working on Azure Cloud using Spark and the Databricks Unified Analytics platform to develop, deploy, and manage machine learning production pipelines. Architected and built end to end solutions from IoT devices to data lakes to ML training and deployments for batch and streaming. Very comfortable in R and Python for single or distributed frameworks.
EDUCATION: BS, Computer Engineering, Milwaukee School of Engineering; MS, Predictive Analytics, Northwestern University
FUN FACT: Craft cocktail enthusiast and junior mixologist.