The Evolution of Data Science and Its Impacts on Consumer Lending - S20

Course Length
90 mins

Instructor
Jennifer Priestly

Price
$275.00

Upcoming Sessions

Description

Presenter:   Jennifer Priestley, Kenneshaw State University

This 90-minute program will be presented live on: August 5, 10:00-11:30 a.m. Central Time
Recording available through: November 5, 2020
Price: $275

 

Are you hearing a lot of buzzwords around "big data" and "data science" and wondering if the people using them actually know what they are talking about?  This course will demystify data science, explain how new and evolving forms of data and analytics have given rise to new banking products, improved decisions, reduced risk, and facilitated the rise of FinTech.  We will discuss how data has evolved from being “small, structured and static” to becoming “massive, unstructured and in-motion”.  We will have a helpful conversation on the role of Excel, Python and the dozens of available software platforms.  Then we will go through the evolution of methods and techniques to translate the new forms of data into information to inform and improve decision making, separating the "buzzwords" from reality and explore how data science is changing the financial services sector (mostly for the better).  Finally, we will explore the new and evolving questions related to ethical data science.

 

This course requires no math or programming.

 

Topics to be covered:

  • An overview of how data has evolved from being “small, structured and static” to becoming “massive, unstructured and in-motion”. 
  • How analytical and modeling methods have evolved to take advantage of new forms of data.   An explanation of the most common data science applications will be provided with minimal math.  These include:
    • Regression
    • Support Vector Machines
    • Decision Trees/Random Forest
    • K Means Clustering
    • Neural Networks
    • Deep Learning
  • Discussion of how data science is changing the financial services sector, with particular emphasis on the emergence of new products and new competitors. 
  • Overview of the new and evolving questions related to ethical data science.

 

Target Audience:  Managers who have responsibility for analytical personnel but may not have any formal training in analytics or data science themselves

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