Data Science for Business Analysts – The critical role of the BA in delivering business value by using latest predictive analytics techniques (Earn 2 CDUs)

Join IIBA Bay Area Chapter in welcoming Sam Nelson for a hands-on session that will focus on how Business Analysts can expand their skill set and help analytics team to produce better predictive models for the business by learning the latest predictive analytics techniques

About Speaker
Sam Nelson
Product Manager – Data
Linkedin Profile

Sam is the Product Lead for Udacity’s Business Analyst Nanodegree program, which prepares students for Business Analyst jobs. Prior Udacity, he worked as a product manager and analytics consultant on projects in several industries, include tech, agriculture, finance, and manufacturing. Sam is passionate about data democratization and helping others develop proficiency in data science.

Mission (the what and why)
More and more companies are incorporating Predictive Analytics into their data strategies. Current research from MarketsandMarkets “predicts the global predictive analytics market to grow from USD 2.74 Billion in 2015 to USD 9.20 Billion by 2020, at a Compound Annual Growth Rate (CAGR) of 27.4%.” New research from TDWI Research identifies the top five reasons why companies are looking to use Predictive Analytics:
– predict trends
– understand customers
– improve business performance
– drive strategic decision-making
– predict behavior

The appeal of Predictive Analytics is very clear, and the ability to produce this kind of actionable, forward-looking analysis is rapidly becoming a key point of distinction for brands looking to stay ahead of their competitors. But, these new approaches bring up a key reality—effective data analysis today requires two things: tools, and those who know how to use them.

A capable Data Science Business Analyst can impact the 80/20 rule in data science projects:
– Reduce the 80% time spent on data & business understanding
– Improve the 20% time spent on in-depth modeling & testing

Less expense. Higher quality. Happier customers. Productive data scientists.

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