# Course Tasters — glossary

Plain-English definitions grouped by career.

## Data Science

Words you meet while cleaning tables, exploring A-level data, and writing a clear handover for a manager.

| Term | Plain English |
| --- | --- |
| **Dataset** | A table of observations you analyse — rows are cases, columns are fields (for example subject entries by year and sex). |
| **pandas** | The main Python library for working with tables — load CSVs, filter rows, group, and summarise. |
| **SQL** | A language for asking questions of data stored in databases (SELECT, WHERE, GROUP BY). |
| **Mean / median** | Mean is the average; median is the middle value when sorted. Median is less pulled around by extreme outliers. |
| **Chart** | A visual summary (bar, line, box plot) that makes a pattern easier to brief than a raw table. |
| **Handover note** | A short written brief for the next person — what you found, how you got there, and what to watch out for. |

## Quantitative Finance

Market and portfolio language used when you measure risk, test a strategy, and write an investment research note.

| Term | Plain English |
| --- | --- |
| **Return** | How much a price moved over a period, usually as a percentage (daily return, annualised return). |
| **Volatility** | How much returns jump around — higher volatility means a wider range of outcomes, not “more return”. |
| **Correlation** | How two assets tend to move together (from −1 opposite, to +1 lockstep). Diversification needs less-than-perfect correlation. |
| **Moving average** | A smoothed average of recent prices (e.g. 20-day) used to spot trend without reacting to every tick. |
| **Backtest** | Run a trading rule on historical prices to see how it would have performed — useful, but not a guarantee of the future. |
| **Buy and hold** | Own the asset for the whole period with no timing trades — the usual baseline a strategy must beat after costs. |
| **Monte Carlo** | Simulate many possible future paths using random draws from a model, then summarise the range of outcomes. |
| **Efficient frontier** | The set of portfolios that offer the best expected return for each level of risk (under a given model). |

## Actuarial Science

Pricing and risk words used when you model claims, set premiums, and brief a risk committee.

| Term | Plain English |
| --- | --- |
| **Frequency** | How often claims happen (for example claims per policy-year). |
| **Severity** | How large a claim is when it occurs (claim amount distribution). |
| **Pure premium** | Expected loss cost ≈ frequency × severity — before expenses, profit, or loading. |
| **Loading** | Extra amount added to pure premium for expenses, profit, or uncertainty. |
| **Relativity** | How much riskier (or safer) one segment is versus a base — e.g. an age-band factor of 1.3 means 30% higher expected cost. |
| **IBNR** | Incurred but not reported — claims that have happened but are not yet on the books; estimated with techniques such as chain-ladder. |
| **Reserve** | Money set aside for unpaid claims (reported and IBNR). |
| **Reinsurance** | Insurance for insurers — transfer part of large losses to another carrier for a premium. |
| **Stress test** | Replay the book under a worse scenario (flood, longevity) to see whether capital and decisions still hold. |

## Machine Learning

Core modelling language for features, training, and honest evaluation before you specialise into computer vision.

| Term | Plain English |
| --- | --- |
| **Feature** | An input measurement the model uses (hours studied, pixel values, a column in a table). |
| **Label** | The answer you want the model to predict (passed/failed, habitat class, clothing category). |
| **Training set** | Examples the model learns from — weights are updated on these. |
| **Validation set** | Held-aside examples used while training to check progress and tune choices (epochs, hyperparameters) — not the final exam. |
| **Test set** | Examples never used for training or tuning — evaluate once at the end for an honest score. |
| **Epoch** | One full pass through the training set. |
| **Accuracy** | Percentage of examples labelled correctly on a given set. |
| **Overfitting** | The model memorises training examples but fails on new ones — train score high, validation flat or falling. |
| **Classifier** | A model that assigns each example to a category (spam/not spam, habitat class). |
| **Neural network** | Layers of simple units (neurons) that learn weights from data; deeper stacks can represent more complex patterns. |

## Computer Vision (Analyst)

Map-reading language for analysts who interpret model outputs and write briefs rangers can act on — without building the model.

| Term | Plain English |
| --- | --- |
| **Land-cover class** | A habitat or surface label on the map (forest, grassland, water, urban, and so on). |
| **Tile / patch** | A small square cut from a larger image that the model scores as one class. |
| **Confidence** | How sure the model is about a prediction — high confidence is not the same as being right; always check the map. |
| **Confusion matrix** | Table of true labels (rows) versus predicted labels (columns). Off-diagonal cells are the mistakes that matter in the field. |
| **Limitation** | An honest bound on what the map can support (season, cloud, rare classes) — required in briefs even when accuracy looks high. |
| **Park brief** | A short note for rangers or ecologists — what the map shows, where it is weak, and what action (if any) follows. |

## Computer Vision (Developer)

Developer vocabulary for building and training convolutional models on satellite tiles and reporting results.

| Term | Plain English |
| --- | --- |
| **Convolution / filter** | Slide a small grid of numbers over an image, multiply and sum — highlights edges or textures. |
| **Pooling** | Shrink a feature map by keeping the strongest signal in each small block. |
| **Batch size** | How many images the model looks at before updating its weights once. |
| **Augmentation** | Random flips, rotations or brightness changes during training so the model sees variation without new photos. |
| **Backbone** | A pre-trained network (e.g. MobileNetV2) that already knows edges and textures; you add a small head for your land classes. |
| **Transfer learning** | Reuse a model trained on millions of general photos, then retrain only the top for your habitats. |
| **Softmax** | Final layer that turns raw scores into class probabilities that add up to 100%. |
| **Stratify** | When splitting data, keep the same mix of classes in each part (e.g. same proportion of Forest). |
| **Confusion matrix** | Table of correct labels (rows) vs predicted labels (columns). Off-diagonal cells are mistakes. |