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Business Analytics Bootcamp

August 19 - December 10, 2019

How much would your company benefit from having a cadre of problem solvers?

We have the only program developed and taught by a seasoned professional using 37 years of experience in solving very complex business problems.

Our Data Analytics Boot Camp trains bright people to become corporate problem solvers using data and analytics. Problems can run the gamut from pricing, to supply chain, to forecasting demand, to optimizing production. Problem solvers generate enormous value returns for their organizations by making them better, faster, and cheaper over time.

There is very high demand for Data Scientists, in fact it is one of the leading professions in the market at the moment. Harvard Business Review called it "The Sexiest Job of the 21st Century." According to Glassdoor, the average base pay for Data Scientists is $120,931. There is a reason Data Scientists are in such high demand. Employers all over the world are looking to make their firms more data-driven, more disciplined, and with a far better decision-making capability.

Graduates of the Bauer Data Analytics Bootcamp will leave ready to pursue employment as a Data Scientist. In just 16 weeks we will provide you with the skills and understanding for a career in Big Data.

This boot camp is a concentrated program designed to prepare students for the extraordinary demand for corporate problem-solvers. Graduates will leave with the ability to use data, mathematics, and technology in concert to solve complex business challenges.


  • Analytical problem solving
  • Creating a comprehensive diagram of an enterprise
  • Data-driven strategy
  • Using statistics for business insight
  • Forecasting
  • Advisory on complex business operations, such as supply chain
  • Simulation and business modeling
  • Blockchain
  • Automation
  • Managing large data sets
  • Visualization/UI
  • Internet of Things (IoT)
  • Systems thinking
  • Factory thinking
  • Technical computing

Students should be highly motivated with a basic understanding of business concepts, spreadsheet calculations, rudimentary office application skills, and math. Students are required to bring and use a current generation laptop computer.

In-class lectures are conducted in the evenings on Tuesday and Thursday from 6-8 p.m., followed by a Saturday morning lecture/lab from 9 a.m.-12 p.m. (this class is not presently offered online). Each week is a new unit.


Week of August 19

Unit 1: Welcome to Business Analytics

  • Understand the scope of business problem solving
  • Realize the range of topics in The Art of the Possible
  • Overturn common myths

Week of August 26

Unit 2: The Analytics Process

  • Know the process for putting together effective solutions
  • Create a correct hypothesis

Week of September 2

Unit 3: Dealing with Data

How to collect, manage, clean, and use data effectively

Week of September 9

Unit 4: Systems Thinking

Understand how to apply Systems Thinking, the underlying principle behind all modeling

Week of September 16

Unit 5: The Art of the Possible

Grounding in all of the common (and uncommon) methods that we have at our disposal to apply to highly complex problems

Week of September 23

Unit 6: Visualization

The skills to visualize the behavior of complex systems; how to make patterns self-revealing

Week of September 30

Unit 7: Tools of the Trade

A comprehensive grasp of the technology of problem-solving

Week of October 7

Unit 8: Case Study Walk-Throughs

Understand the problem-solving process through the lens of a handful of actual case studies from a variety of industries

Week of October 14

Unit 9: Simulation

How to create faithful replicas of complex businesses or subsystems to generate keen insight about their behavior

Week of October 21

Unit 10: Digital Twins and Automation

Automation of organizations through the development of Digital Twins

Week of October 28

Unit 11: Optimization

How to find the optimal performance for a system using mathematical methods

Week of November 4

Unit 12: Machine Learning

Using Machine Learning for classification and prediction

Week of November 11

Unit 13: Testing and Validation

How to ensure that analytical systems we build are both accurate and valid

Week of November 18

Unit 14: Analytics and the Enterprise

Tips for success when working within a large organization in an analytics role

Week of December 3

Unit 15: End of Course Project Reviews and Critiques

Review student course projects; presentations

Week of December 10

Unit 16: Advanced Topics

Going beyond the conventional: methods and technologies that are likely to shape future problem-solving


George E. Danner is the instructor for the course. He is currently President of Business Laboratory, an award-winning consultancy providing analytical problem-solving for organizations around the world. George has 35 years of experience in advising companies on their most challenging problems, and in building teams to do the same.

George has a BS in Mechanical Engineering from Texas A&M University and an MS in Management from The Sloan School at MIT.

Both textbooks were written by the course instructor, and are available for purchase online:

Students will be assigned a course project at the beginning of the course for completion by week 15. The project will resemble a real world application of business problem-solving.


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CONTACT US: Gary Randazzo, Director:, 713-743-4754 | Jennifer Coppock, Program Manager: 713-743-4702