Programming Bootcamps

C++ code

I offer courses that teach computing technologies--skills participants need to create solutions to business issues.  My courses emphasize applicability to participants' needs, and expanding their depth and breadth of knowledge.  They can be tailored ahead of time or modified on the fly to suit the evolving needs of the students.  If you’re looking for a hands on introduction to new concepts, techniques, and styles, or if you’re looking for guidance to solve particular kinds of problems, I can design a course for you.

With over 20 years' experience in guiding others toward solutions, I like to create a relaxed, interactive environment that encourages across-the-board participation. Have fun learning new, challenging stuff, and don’t just learn from me. Learn how to learn from each other.

Makers: Learn solid programming techniques for sensing, automation, and control. Get guidance on specific problems or receive a more general introduction. Interact with other makers to learn how the same problems (and solutions!) show up in a variety of situations.

Programmers: Gain access to new languages, language specific topics, or topics that transcend multiple languages. Develop insights into style: readability, longevity, team programming. 

I offer bootcamps in a variety of subjects, primarily related to Data Analytics and analysis of large datasets.  I also provide training in C++ and Fortran, parallel computing, Statistics, Epidemiology, Cardiology, and related fields.

Possible topics include:

  • Data Analytics for sports betting, health care, and other areas
  • Statistical methods (usually in Python or R)
  • Sensing, automation, and control (Arduino, Raspberry Pi, etc.)
  • Modeling and prediction
  • Healthcare anaytics and predictive medicine


If this list seems long, understand that programming in different languages is like driving different cars. Each has its own idiosyncracies, but good driving practices remain across all models.

  • Modern C++ (Best Practices with C++14 and beyond)
  • Python (a favored choice for non-programmers)
  • R (a favored choice for statistics and statistical modeling)
  • Matlab (a superior language for stability with numerical algorithms)
  • Perl (does everything Python can do and more)
  • Bash (everyone eventually learns this in self-defense)
  • Fortran (a favored choice for parallel computing)