Jump to Content
ABA: The American Bankers Association
Skip Section Navigation

Curriculum | Data & Analytics School

Register

Earn CFMP CE Credits

CE credits from ABA Professional Certifications are awarded based on their relevance to certification examination outlines. The school is eligible for a maximum of CFMP 21 CE credits.

2023 Schedule

All times are in ET. 

  • Mon, Jan 23

  • Wed, Feb 1

  • Thurs, Feb 2

  • Fri, Feb 3

  • Tues, Feb 7

  • Wed, Feb 8

  • Wed, Mar 22

Mon, Jan 23

2:00 PM – 4:00 PM

Live Synchronous Session:

ABA Executive Welcome & Program Kick-Off and Participant Introductions

David Schweidel, Faculty Director

Introduction to Zoom
How to Navigate Canvas Introduction to Collaboration Tools
Launch Pre-Read & Preparatory Work

Wed, Feb 1

11:30 AM – 12:45 PM

Setting the Stage: Developing a Data and Analytics Strategy
David Schweidel

Objective: 
Develop a leader's perspective on how to use current and emerging trends in data science to build effective business strategies. 

  • Examine sources and uses of data
  • Discuss the integration of your data strategy into overall business and marketing strategies and the role of data in evaluating the impact of marketing actions. 

12:45 PM - 1:30PM

Break

(East Coast grab lunch)

1:30 PM – 2:45 PM

Text Analysis for Marketers
David Schweidel

Objective: 
Understand the data insights that can be captured from social media and other sources of text data (e.g., call center transcripts/chat logs).

2:45 PM - 3:15 PM

Break & Activity 

(Mid/West Coast grab lunch)

3:15 PM – 3:45 PM

Learning Group Activity
[Virtual Breakout Rooms]

Objective: Demonstrate comprehension and practical application of course content.

  • Each student group will develop and deliver solutions using a case scenario with data analysis concepts taught in the course. 

3:45 PM – 4:00 PM

Debrief & Next Steps
Faculty Director

Thurs, Feb 2

11:30 AM - 12:30 PM

Data Visualization

Objective: 
Introduce participants to the techniques and tools used to create effective visualizations that clearly and efficiently communicate relations within data.

  • Consider analysis through visualization
  • Survey how to use visualizations effectively in reports and presentations to engage audiences

12:30 PM - 12:45 PM

Break 

(East Coast grab lunch)

12:45 PM - 2:30 PM

Using Data to Support Brand & Product
Doug Bowman

Objective: 
Explore how marketers can better leverage data to implement customer-centric product development and marketing.

  • Leverage data insights to understand who the right target is for existing productions and what new products we should be developing
  • Identify unique, profitable growth opportunities

2:30 PM - 2:45 PM

Break

(Mid/West Coast grab lunch)

2:45 PM – 3:15 PM

Learning Group Activity
[Virtual Breakout Rooms]

Objective: Demonstrate comprehension and practical application of course content.

  • Each student group will develop and deliver solutions using a case scenario with data analysis concepts taught in the course. 

3:15 PM – 3:30 PM

Debrief & Next Steps

Fri, Feb 3

11:30 AM - 12:30 PM

Opportunities in Mobile
Michelle Andrews

Objective: 
Learn how to gain key customer insights from mobile use and mobile data.

  • What can we learn from customers?
  • What can you enable with mobile technology that we know will work?
  • Identify opportunities for applications such as customer targeting

12:30 PM - 12:45 PM

Break 

(East Coast grab lunch)

12:45 PM - 2:00 PM

Artificial Intelligence & Machine Learning

Jesse Bockstedt

Objective: 
Explore the role of machine learning in data science, predictive modeling, and artificial intelligence.

  • Survey the multitude of ways businesses are leveraging machine learning to gain insights
  • Understand how machine learning and artificial intelligence can combine to support predictive modeling

2:00 PM - 2:30 PM


Break

(Mid/West Coast grab lunch)

2:30 PM – 3:15 PM

Bias and Ethics in AI/Machine Learning

David Schweidel

Objective:  
Raise awareness of how bias can unintentionally show up in AI/Machine learning-driven algorithms.

  • Examine the questions we should be asking about how bias might find its way into our processes (if we are now aware of how algorithms are formed, we may unintentionally discriminate)
  • Discuss data rights and ownership of customer data

3:15 PM – 3:30 PM

Debrief & Next Steps

Faculty Director

Tues, Feb 7

11:00 AM - 11:30 AM

Welcome Back & Debrief
Faculty Director and Pam Tipton

11:30 AM - 1:00 PM

Security, Privacy, and the Quantitative Self
Benn Konsynski

Objective: 
Raise awareness of key privacy and security matters that every leader must incorporate into data strategies.

  • Understand how enhanced capabilities observe, measure, capture, assimilate, curate, and retain enormous amounts of information that impact individuals and activities.

1:00 PM - 1:15 PM

Break (East Coast grab lunch)

1:15 PM - 3:15 PM


Designing for Digital: Architecting Your Business for Success in a World Gone Digital

Anandhi Bharadwaj

Objective: 

Examine key considerations for operationalizing your data strategy.

  • Examine the foundations of digital environments as new ways or working, interacting with customers and making money
  • Consider the technology and data bias implications for your business in implementing a data strategy

3:15 PM - 3:30 PM

Break (Mid/West Coast grab lunch)

3:30 PM – 3:45 PM

Debrief & Next Steps

Faculty Director

Wed, Feb 8

11:30 AM - 12:30 PM 

Customer Experience: How Customer Journey Informs Growth Opportunities
David Schweidel

Objective: 
Reshape marketing strategies around a holistic view of the customer journey based on internal and external data sources.

  • Leverage data sources to interpret customer intent
  • Identify opportunities to engage in customer centric marketing efforts

12:30 PM - 12:45 PM

Break (East Coast grab lunch)

12:45 PM - 2:30 PM

Customer Journey (cont.)

David Schweidel

2:30 PM - 2:45 PM

Break

(Mid/West Coast grab lunch)

2:45 PM - 3:15 PM

Learning Group Capstone

[Virtual breakout rooms]

Objective: Demonstrate comprehension and practical application of course content.

  • Each student group will develop and deliver solutions using a case scenario with data analysis concepts taught in the course. 

3:15 PM – 4:00 PM

Readout, Debrief & Conclusion

Faculty Director & ABA Team

Wed, Mar 22

11:30 AM - 1:00 PM 

Cohort Reunion & Debrief

What You Have Tried, What Has Worked, What Hasn't Worked

David Schweidel
Faculty Director

This program is subject to change. Please bookmark this page to check for continuing updates. Read ABA Conference and School Policies to help answer additional questions.

Keep Me Informed

Get updates on the Data & Analytics School. Contact John Capotosto if you have any questions.