Visual decks

One subject per deck, drawn rather than told. Each click or key press plays one animation beat, so you can stop on an idea, say it back, and only then move on. I built them to record videos; they work just as well read on your own.

SFBy Sitraka Forler · Lecturer, Durham Business School·Updated 3 October 2026· About this site

• Git, visually10 scenes · about 8 minAn animated walk through Git: commits as snapshots, branches as sticky notes, merges with two parents, push and pull, and the daily loop. Ten scenes, no jargon first.Read and open
• Pandas, visually10 scenes · about 9 minAn animated tour of pandas: the anatomy of a DataFrame, Series, boolean masks, loc versus iloc, vectorisation, groupby, merge and rolling windows, on a small price table.Read and open
• SQL joins, visually10 scenes · about 8 minTwo tables with a deliberate mismatch, and every join drawn as row-pairing lines: INNER, LEFT, RIGHT, FULL and the anti-join, plus the duplicate-key trap and a one-rule cheat sheet.Read and open
• Black-Scholes, visually10 scenes · about 10 minPayoff, time value, the five inputs, the hedge, then the live explainer where you drag volatility and watch the option value curve move. The formula decoded, a worked number and implied volatility.Read and open
• The Sharpe ratio, visually9 scenes · about 7 minTwo funds with the same +12 % and a very different ride. Volatility is the ride, the Sharpe ratio is return per unit of it. The live explainer, the higher-return trap, leverage, how to read a Sharpe, and Sortino in one line.Read and open
• A data science project, visually11 scenes · about 10 minDecision, question, metric; the loop, not the pipeline; 80 % is data work; split before you touch anything; beat naive first; evaluate in the business currency; complexity is a loan; drift after deploy.Read and open
• Zabbix, visually10 scenes · about 9 minMonitoring as collect, decide, act: the server, agents and proxy architecture, items that become time series, triggers as opinions, the 3 am flapping trap and hysteresis, escalation ladders, and templates stamped across 200 hosts.Read and open
• OLS & logistic regression, visually13 scenes · about 12 minA cloud of points, the residuals, why squares (wiggle the line and watch the sum), Cov/Var and a CAPM beta, R², then the line breaks on yes/no data, the sigmoid, the decision boundary, odds and maximum likelihood.Read and open