Teaching & Training

We provide hands-on courses on the big picture of quantitative analysis and how to convert generic conceptual problems into rigorous, scientific, unbiased computational frameworks – from the conceptual and formal design, generation/collection of data sets, to the choice of the mathematical/statistical models, interpretation and communication of the analyses and results. Ultimately, their goal is to provide the tools to Stay Relevant in the Age of Knowledge Automation, as we call our overarching program. The courses can be adapted to a range of backgrounds, proficiency levels, and availability of time. Although they have a common core and several offshoots that can be combined and blended, our curriculum can be generally divided in two main parts. Additionally we are able to provide shorter, simpler courses (e.g. coding, introductory statistics, data wrangling, reproducible workflows) to complement our main offerings and get professionals and teams up to speed on some basics.

  1. From the Math to the Masses (a.k.a. Scientific & Quantitative Thinking for Everyone) – 8-24 classroom hours (single day to few weeks distribution):

Our first flagship course. From the Math to the Masses uses an approach based on real-world problems to teach and demonstrate the process of developing a vague idea into a concrete, scientific, statistically-accurate research project. It shows how we can use statistical models (including Machine Learning/Artificial Intelligence) to formalize and solve open-ended problems, but how natural intelligence is still irreplaceable in this process. It is the broadest and most complex of the course topics, also the most flexible and adaptable to different contexts. It can be taught as an intensive short course, or over a longer time period. Its audience can range from less technical professionals – who will gain insight into how to think scientifically and formulate real world problems as data-driven, quantifiable, algorithmic tasks – all the way to very technical personnel that will benefit from acquiring a critical and scientific view of the tools of the trade. The most important skill any professional will learn in this course is the importance of the interaction between humans and technology to better solve problems in their organization.

  1. Putting the Intelligence into the Artificial (a.k.a. AI, Machine Learning, Big Data Algorithms, Mathematical Modeling & Statistical Inference) – 8-24 classroom hours (single day to few weeks distribution)

Our second flagship course describes the formal bases of Statistical Inference and Mathematical Modeling, which underpin all Data Science, Machine Learning, and more generally any kind of analysis that uses data – it is rooted in technical defintions while maintaining an intuitive presentation. It also uses real-world problems to work through the process of assembling all parts of a mathematical model (be it linear, nonlinear, regression, classification, machine learning, differential equations, neural networks, transformers/LLMs, etc), expanding the deterministic model structure into a statistical model, and using it in the inverse problem of identifying unknown features and mechanisms from data generated by the system. Depending on the audience and goals of the organization, the level of detail and specialization can be tuned to the level of mathematical and coding proficiency, going from a higher level modeling approach, to a from-scratch implementation of advanced models.

  1. Building Blocks, Scafolding, Wiring, Plumbing & Carpentries® (a.k.a. Fundamentals of Quantitative and Data Sciences) – 8-16 hours

With science and technology becoming increasingly multidisciplinary and rapidly embedded in most industries, the technical bar becomes not only higher but more complex; therefore, skill development must keep up with custom-tailored training that fills these knowledge gaps. That is true not only for researchers and engineers, but also users of technology and professionals that end up interacting with tech, which is now a large portion of all profefssionals.

We offer supplementary courses on any aspects of fundamentals such as Coding, Math, Statistics, Experimental Design, Scientific Method, Reproducible Workflows, and any other concepts that may be necessary to catch up to more advanced training. These may be combined in whatever proportion and order and adapted to the needs of specific teams or individuals.

Fundamentals are taught in and adapted to the context of the real-world problems tackled by the organization or team, helping set the foundations for the use of methods and designs described in the advanced courses.