Kinds of algorithms (in plain language)
Algorithms in advanced analytics fall into a few families. You don't need to implement them; it helps to know what each family is for.
Predicting a number (regression)
What it does: Estimates a numeric value (e.g. next month's revenue, a customer's lifetime value, how long until a machine fails).
Idea: Use past data where you know the outcome, find patterns (e.g. "when X and Y were high, the outcome was Z"), then apply those patterns to new cases where you don't yet know the outcome.
Typical use: Demand forecasting, pricing, risk scores, resource planning.
Putting things into categories (classification)
What it does: Assigns a label or category (e.g. "will churn" vs "will stay," "high risk" vs "low risk," "spam" vs "not spam").
Idea: Learn from historical examples where the label is known, then apply that learning to new cases to assign the most likely label.
Typical use: Credit approval, fraud detection, customer segmentation (e.g. likely buyer vs not), prioritisation (e.g. which lead to follow first).
Finding groups in the data (clustering)
What it does: Groups similar items together without you having to define the groups in advance. The algorithm discovers structure in the data.
Idea: Items that are "close" in terms of their attributes end up in the same cluster; items that are different end up in different clusters.
Typical use: Customer segments you didn't pre-define, anomaly detection (the odd one out), organising large sets of entities (products, documents, locations).
Working with sequences and time (time series)
What it does: Models how something changes over time — trend, seasonality, level — and uses that to forecast the next values or detect when something breaks from the pattern.
Idea: Past behaviour over time is used to project forward or to flag "this week looks different from normal."
Typical use: Sales forecasting, capacity planning, early warning (e.g. drop in conversions, spike in complaints).
Working with text and language (NLP)
What it does: Extracts meaning, topics, or structure from text (emails, documents, chat, reviews) so you can search, summarise, or classify it at scale.
Idea: Represent text in a way a machine can work with (e.g. counts, embeddings), then apply rules or models to classify, extract entities, or summarise.
Typical use: Sentiment analysis, document routing, chatbots, summarising feedback or reports.
Optimising decisions (optimisation)
What it does: Finds the best combination of choices (e.g. prices, allocations, schedules) given your goal and constraints (e.g. capacity, rules, risk limits).
Idea: Define an objective (e.g. maximise profit, minimise cost) and the rules that must hold; the algorithm searches for the best feasible solution.
Typical use: Pricing, resource allocation, scheduling, marketing spend, inventory.
In practice, a single use case often combines several of these (e.g. a classification model whose inputs were built using feature engineering and then tuned with optimisation). The important thing is to know what kind of question you're answering — a number, a category, a group, a forecast, text, or a decision — and that maps to which family of algorithms is relevant.