Analytica Data Science SolutionsContact
Services
Work
Products
Company
ESC

↑↓ to move↵ to open

← All services

Service

Training & Enablement

The hardest room to teach AI to is a mixed one: the person who signs off on a system sitting next to the analyst who will build with it. Our curriculum is built for that room — in government and enterprise alike — so it leads with judgment and risk before syntax. One delivered example is the data-science and AI curriculum built for analysts at the Department of Homeland Security and the Department of Defense.

The room

Seniority and technical experience don't line up

A mixed cohort contains all four of these people — and the same afternoon of teaching has to work for each of them.

  • An analyst who writes code daily

    Wants the internals: methods, failure modes, what to build first.

  • A director who came up through engineering

    Technical fluency at the top of the room — it happens more than curricula assume.

  • An analyst who has never written a line

    Domain expertise without the tooling. The course cannot leave them behind.

  • A director who has never written code

    Signs off on the system, and needs to know what it can and cannot promise.

That mix is why the foundation and decision-support courses assume no programming — and why the room is kept together rather than split by seniority.

The director needs to hear what the analyst can and can't promise; the analyst needs to hear what the director will be asked in a briefing. The four seats here are illustrative of the mix, not counts from a delivered cohort.

What this covers

  • Live virtual and on-site delivery
  • Worked exercises and scenario vignettes in every course
  • No programming prerequisite on the foundation and decision-support courses
  • Custom cohorts assembled from a 16-topic curriculum
  • Published courses and materials are unclassified; any additional security requirements are agreed with the client under the contract

What the courses lead with

Conceptual understanding, risk awareness, and human–machine teaming come before tooling, so the executive making the procurement call and the analyst who will write the code are working from the same material.

The curriculum

Sixteen topics, in teaching order

A cohort is assembled from these rather than picked off a menu, and the order matters: each topic assumes the ones before it. The sequence starts before code and ends at geospatial analysis.

  1. 01The ABCs of Data Science
  2. 02R for analystsR
  3. 03Project planning in AI
  4. 04Data preparation
  5. 05Tidy data principles
  6. 06Intermediate RR
  7. 07Advanced RR
  8. 08Exploratory data analysis
  9. 09Machine learning and predictive modeling
  10. 10Advanced machine learning
  11. 11Introductory Shiny developmentR
  12. 12Advanced Shiny developmentR
  13. 13Tableau
  14. 14Data visualization with ggplot2 and plotlyR
  15. 15Big data using Spark
  16. 16GIS analytics in RR

7 of the 16 cover R or the R ecosystem. The published curriculum is R-first because R fits the analyst audiences these courses teach; client delivery uses whatever the engagement calls for, Python included.

Two of these are published as complete hour-by-hour syllabi you can read before speaking to anyone — the five-day R foundations course and the three-day defense decision-support course, both listed below. The rest have been delivered, but their agendas aren't yet written up; we scope those with you when we assemble a cohort.

Published syllabi

Both courses are published in full: every session, its duration, and its outcomes.

AI for Decision Support in Defense Operations

How machine learning and data fusion enhance situational awareness and planning

3 days
No programming

R for Data Science: Foundations of Analytics

Rapid analytics and visualization for mission support

5 days
Beginner-friendly

Discuss a cohort

Tell us about your team — size, roles, and what they need to be able to do. We'll propose a course plan from the published curriculum.

Plan a course