Operations Research Analyst
Build mathematical models that find the best schedules, routes and resource plans.
Design surveys and experiments and analyze the resulting data.
Statisticians decide how to collect data so results can be trusted, then analyze it to estimate effects, test hypotheses and report how certain the findings are, in fields such as medicine, government and sports. They use R, SAS or Python, probability theory and survey design. Entry is a bachelor's degree in statistics or math; most positions require a master's degree.
Take these courses in order, from beginner to mid-level.
The shared base every STEM career uses: working with numbers and units, testing ideas fairly, handling data and code, and explaining your work.
How statisticians, actuaries and operations research analysts work: the math they use, pairing it with code, credentials, evidence, and explaining results honestly.
The everyday computer, internet, safety and AI skills every tech and STEM job assumes you already have.
Write clear emails and documents, present and run meetings, work well on teams, and navigate résumés, interviews and networking.
The practical math behind tech and STEM work: numbers, algebra, functions, geometry, logic and counting, with short code exercises.
Clean tables, correct formulas, lookups, pivot tables and honest charts in Google Sheets, LibreOffice Calc or Excel.
Learn to program in Python from zero: values, decisions, loops, functions, data structures, files, modules and a small data project.
Describe data, reason with probability, and draw honest conclusions from samples, with short fill-in-the-blank Python exercises.
Load, clean, reshape and explore real data with Python and pandas, then turn the results into clear findings.
Turn analysis into charts people read correctly: visual principles, matplotlib and seaborn, small multiples, dashboards and a short data story.
The calculus and linear algebra behind science, engineering and machine learning: derivatives, integrals, vectors, matrices, gradients and eigenvalues, with Python exercises.
Model uncertainty with random variables and distributions, then estimate, test and update beliefs from data, with Python simulations you run in the browser.
Fit and check multiple regression and GLMs, design randomised, blocked and factorial experiments, and turn analyses into clear, reproducible reports for the researchers you work with.
Build mathematical models that find the best schedules, routes and resource plans.
Calculate the financial risk of insurance, pension and investment plans.
Build statistical and machine learning models that predict outcomes from data.