Data Analyst

Programming Basics

• Null / None / nilA value meaning "nothing here", and the source of countless crashes.
• Off-by-One ErrorA loop or index that runs one step too many or too few.
• StringA sequence of characters, usually immutable.

Version Control (Git)

• .gitignoreFiles Git should never track, like build output and .env.
• BranchA movable pointer to a line of commits, for isolated work.
• CommitA snapshot of changes with a message explaining them.
• GitThe distributed version control system almost everyone uses.
• Push, Pull, FetchSending commits, downloading and merging, or only downloading.
• RemoteAnother copy of the repository, usually "origin" on GitHub or GitLab.
• Version ControlTracking every change to code so you can review, revert and collaborate.

Pull Requests & Code Review

• Pull RequestA proposal to merge a branch, with discussion and review.

Documentation & Writing

• MarkdownThe plain-text formatting syntax used for READMEs, docs and PRs.
• READMEThe front page of a project: what it is, how to run it, how to contribute.

Math for Programmers

• Percentiles (p50, p95, p99)The value below which a given share of measurements fall; the right way to read latency.
• Probability BasicsReasoning about chance, for sampling, A/B tests and failure rates.

HTTP

• HTTPThe request-response protocol of the web.
• HTTP Status CodesThree-digit codes grouped 1xx–5xx describing the result.
• Query StringKey-value parameters after the ? in a URL.
• URLThe address of a resource: scheme, host, path, query and fragment.

API Styles & Formats

• JSONThe text data format most APIs speak.
• RESTAn API style built on resources, URLs and HTTP methods.

Relational Databases & SQL

• CASE ExpressionConditional logic inside a SQL query.
• COALESCE and NULLIFHandling NULLs inside SQL expressions.
• DatabaseOrganized, persistent storage for data.
• Database Client / GUITools like psql, DBeaver or TablePlus for exploring databases.
• Database SchemaThe structure of tables, columns and relationships.
• DDL, DML, DCL and DQLSQL's categories: defining structure, changing data, granting access, querying.
• Foreign KeyA column referencing another table's primary key.
• HAVINGFiltering groups after aggregation.
• JOINCombining rows from several tables.
• NULL in SQLThree-valued logic, and why NULL = NULL isn't true.
• PostgreSQL, MySQL and SQLiteThe common relational databases and where each one fits.
• Primary KeyA column that uniquely identifies each row.
• SQLThe language for querying relational databases.
• SQL Data TypesChoosing integer, numeric, text, timestamp and other column types.
• SQL String, Date and Math FunctionsBuilt-in functions for transforming values in queries.
• Table, Row, ColumnThe basic structure of relational data.
• UNION, INTERSECT, EXCEPTCombining the results of several queries.
• ViewA saved query that acts like a table.

Data Engineering Foundations

• Data Engineer vs Analyst vs Scientist vs ML EngineerWho builds pipelines, who answers questions, who models, and who ships models.
• Data ProductA dataset treated as a product, with an owner, documentation, quality guarantees and users.
• Data Science Hierarchy of NeedsCollect, move, store, clean, analyze, then learn: why reliable plumbing comes before AI.
• DatasetA named collection of related data, like a table or a set of files.
• Modern Data StackCloud warehouse, managed ingestion, SQL transformations and BI tools wired together.
• Source SystemsWhere data originates: application databases, APIs, logs, files, SaaS tools and devices.
• Structured, Semi-Structured and Unstructured DataTables, JSON-like records, and free-form text, images or audio.

Batch & Distributed Processing

• Notebooks (Jupyter)Interactive documents mixing code, output and notes.

Transformation & Analytics SQL

• ETL vs ELTTransforming before loading vs loading raw data and transforming in the warehouse.

Serving & Analytics

• Self-Service AnalyticsLetting non-engineers explore data safely on their own.

Statistics

• Descriptive StatisticsSummarising a dataset with counts, averages, spread and shape.
• Distribution ShapesSymmetry, tails and peaks — the shape that tells you which summary is honest.
• Mean, Median and ModeThree ways of naming a 'typical' value, and why the choice changes the story.
• Standard DeviationHow far a typical value sits from the average, in the same units as the data.

Charts & Visualization

• Bar ChartComparing amounts across categories with lengths a reader can rank instantly.
• Choosing a ChartMatching the comparison you want to make to a chart form that shows it.
• Data VisualizationTurning numbers into marks a person can read in a few seconds.
• Gauge ChartA dial showing one value against a target — familiar, and often the wrong choice.
• HistogramShowing how values are spread by binning them and plotting the counts.
• Line ChartShowing how a measure moves over an ordered scale, usually time.
• Scatter PlotPlotting two measures together to see whether they move in step.
• SparklineA tiny line that shows a shape over time inside a table cell or a headline.
• Stacked Bar ChartTotals plus their composition in one bar, and the readability cost of the choice.
• Table DesignLaying out rows and columns so the important number is found before everything else.

Types of Analysis

• Ad-Hoc AnalysisOne-off questions answered quickly, with the right amount of rigour.
• Descriptive AnalysisSummarising what happened: counts, averages, breakdowns.
• Period-Over-Period ComparisonThis week vs last week, done without fooling yourself.

Time & Forecasting

• Naive ForecastPredicting 'same as last time' — the baseline every model must beat.
• Run RateExtending today's pace to the full period — and why it usually overstates.
• TrendThe long-run direction underneath the noise.

The Analyst's Toolkit

• Analysis NotebooksCells of code, output and notes run in order, and the out-of-order execution that ruins them.
• Data ExtractA point-in-time copy of data pulled for one question, and how it drifts from the source.
• Lookup FunctionsPulling a value from another table by key, in a spreadsheet or in SQL.
• Pivot TableDragging fields into rows, columns and values to rebuild a table any way the question needs.
• Spreadsheet FormulasThe cells that recalculate, and the ones that quietly stop being true.
• Spreadsheets for AnalysisThe grid as a first analysis tool: sorting, filtering, pivots and formulas.
• Spreadsheets vs SQLWhen the spreadsheet is the right tool and when it quietly stops being one.

Data Engineering Basics

• Data LakeCheap storage for raw data in any format.
• Data WarehouseA database optimized for analytics, like BigQuery or Snowflake.

Estimation & Planning

• OKRsObjectives and key results for setting goals.

Communication

• Async CommunicationWriting so people can respond on their own time.
• Status UpdatesProactively telling people where things stand.
• When to Ask for HelpNot too soon, not too late: time-box before asking.
• Writing ClearlyLead with the point, be specific, keep it short.

Product Thinking

• Business MetricsRevenue, retention, conversion: what the business measures.
• North Star MetricThe single metric that best captures the value you deliver.

Junior Habits & First Job

• Clarifying TicketsAsking questions before building the wrong thing.
• Testing Your Own WorkChecking that your change works before calling it done.

Programming Basics

• Dates and TimesTime zones, UTC, ISO 8601, and why date bugs are everywhere.
• Floating-Point NumberA binary approximation of real numbers, and why 0.1 + 0.2 != 0.3.
• ISO 8601The standard text format for dates and times, like 2026-10-10T09:00:00Z.
• Regular ExpressionA pattern language for matching and extracting text.
• Time ZoneOffsets from UTC that change with location and daylight saving.
• Unix TimestampSeconds since 1970-01-01 UTC; a simple, unambiguous point in time.
• UTF-8The dominant variable-length encoding of Unicode.

Version Control (Git)

• Merge ConflictTwo branches changed the same lines and Git needs you to decide.

Documentation & Writing

• Design DocumentA written proposal of how to build something, reviewed before building it.
• Technical WritingWriting clearly for other engineers.

Math for Programmers

• Confidence IntervalThe range a true value plausibly falls in.
• Correlation vs CausationWhy related numbers don't prove one causes the other.
• P-valueHow surprising data is if nothing is going on.
• Sampling BiasWhen the data you see isn't the population you claim.
• Simpson's ParadoxA trend that reverses when groups are combined.
• Statistical SignificanceWhether a result is real or plausibly chance.
• Survivorship BiasConclusions drawn only from the winners that survived.

HTTP

• HTTP MethodsGET, POST, PUT, PATCH, DELETE and what each one means.

Backend Basics

• PaginationReturning large result sets one page at a time.

API Design

• 429 Too Many RequestsThe status returned when a client goes over a rate limit.
• Bulk OperationsEndpoints that act on many items in one request.
• Rate LimitingLimiting how many requests a client can make.

Relational Databases & SQL

• Audit Columns (created_at, updated_at)Recording when, and by whom, rows changed.
• DenormalizationDeliberately duplicating data for read performance.
• JSON ColumnsStoring semi-structured data inside a relational database.
• Pivot / UnpivotTurning rows into columns and back.
• Self JoinJoining a table to itself, e.g. employees and their managers.
• Soft DeleteMarking rows as deleted instead of removing them.
• Timestamp With vs Without Time ZoneStoring moments in time correctly in the database.

Indexing & Query Performance

• Database IndexA lookup structure that speeds up queries at the cost of slower writes.

Web Application Security

• Parameterized QueryPassing values separately from SQL so they can't change its meaning.
• SQL InjectionAttackers running SQL through unescaped input; prevented with parameterized queries.

Privacy & Compliance

• Anonymization vs PseudonymizationIrreversibly vs reversibly removing identity from data.
• Data RetentionHow long to keep data, and when to delete it.
• PIIPersonally identifiable information that needs special care.

Collection & Instrumentation

• Bot and Test Traffic FilteringKeeping crawlers, monitoring checks and internal users out of the numbers.
• Consent at CollectionOnly collecting what users agreed to, and recording that they agreed.
• Customer Data Platform (CDP)Tools like Segment that collect events once and route them to many destinations.
• Event Naming ConventionsConsistent names like order_completed so events are findable and comparable.
• External and Third-Party DataData you buy, scrape or pull from partners, and the contracts and quality risks it brings.
• Identity ResolutionStitching anonymous visitors, devices and accounts into one person.
• InstrumentationAdding code that records what users and systems do, so the data exists at all.
• SessionizationGrouping events into sessions by user and inactivity gaps.
• Tracking PlanA shared spec of which events to track, their names and their properties.

Data Modeling for Analytics

• Conformed DimensionOne shared dimension used by many fact tables so numbers line up.
• Date Dimension / Date SpineA table with one row per day and its calendar attributes.
• Dimension TableA table describing the who, what, where of facts, like customers or products.
• Dimensional ModelingKimball's approach: facts surrounded by descriptive dimensions.
• Fact TableA table of measurable events, like orders or page views, at a fixed grain.
• GrainWhat one row of a table represents; the first decision in any model.
• One Big Table (Wide Tables)Denormalizing everything into one wide table for simple, fast queries.
• SCD Types 1, 2 and 3Overwrite, add a versioned row, or keep a previous-value column.
• Slowly Changing Dimension (SCD)Handling attributes that change over time, like a customer's address.
• Star vs Snowflake SchemaSimpler queries vs less duplication.

Batch & Distributed Processing

• DataFrameA table-like data structure with named columns, as in pandas, Polars and Spark.
• Distributed SQL Query Engines (Trino, Presto)Querying data where it lives, across lakes and databases.
• pandasPython's standard DataFrame library for data analysis.
• Single-Node Engines (DuckDB, Polars)Fast local processing that often makes a cluster unnecessary.

Transformation & Analytics SQL

• Data CleaningFixing types, formats, missing values and duplicates.
• Data ProfilingSummarizing a dataset's columns to understand what's really in it.
• Data TransformationCleaning, joining and reshaping raw data into useful tables.
• Deduplicating with ROW_NUMBERKeeping one row per key using a window function.
• Gaps and IslandsFinding consecutive runs and breaks in sequences with SQL.
• GROUP BY ROLLUP and CUBEComputing subtotals and grand totals in one query.
• Incremental ModelsProcessing only new or changed rows instead of rebuilding a whole table.
• QUALIFYFiltering on window function results without a subquery.
• SamplingWorking on a representative subset to go faster.
• SQL Transformation Models (dbt)Transformations written as versioned SELECT statements that build tables.
• Staging, Intermediate and Mart LayersA conventional way to organize transformation code.

Orchestration & Pipelines

• Pipeline DAGA pipeline expressed as tasks and their dependencies.
• Pipeline SchedulingRunning pipelines on a time schedule or when upstream data arrives.
• Task DependenciesWhich steps must finish before others can start.

Data Quality & Observability

• Anomaly Detection on DataFlagging unusual volumes or values automatically.
• Data DowntimePeriods when data is missing, late or wrong.
• Data Expectations / AssertionsDeclared rules data must satisfy, checked in the pipeline.
• Data FreshnessHow recently a table was updated, and whether that's recent enough.
• Data TestsAutomated checks like not-null, unique and accepted values on tables.

Data Governance & Privacy

• Data CatalogA searchable inventory of datasets, their meaning and their owners.
• Data Dictionary / Business GlossaryDefinitions of tables, columns and business terms.
• Data LineageWhere data came from and everything it flows into.

Serving & Analytics

• Aggregate / Summary TablesPrecomputed rollups that make dashboards fast.
• Dashboard DesignBuilding dashboards people actually use.
• Funnel AnalysisMeasuring how users move through a sequence of steps.
• KPI DesignChoosing the few numbers that steer a team.
• OLAP CubePre-aggregated data for fast slicing by dimensions.
• Retention AnalysisMeasuring whether users come back over time.
• Semantic Layer / Metrics LayerOne place that defines metrics like revenue, so every tool computes them the same way.

Working as a Data Engineer

• Documenting DatasetsWriting what a table contains, its grain, owner and caveats.

Statistics

• Confounding VariableA third factor driving both things you measured, producing a real but meaningless relationship.
• Correlation CoefficientOne number from -1 to 1 for how tightly two measures move together.
• Effect SizeHow big the difference is, separately from how sure you are that it is real.
• Hypothesis TestingDeciding, in advance, what evidence would change your mind — then checking it.
• Law of Large NumbersAverages settle toward the true value as you gather more observations.
• Linear RegressionThe straight-line fit that answers 'what changes when this number moves'.
• Logistic RegressionPredicting yes-or-no outcomes with a linear model and a cutoff.
• Long-Tail DistributionA few huge values and a very long tail of small ones, where the average misleads.
• Margin of ErrorThe plus-or-minus around a polled number, and why small samples make it large.
• Normal DistributionThe bell curve many natural and sampled quantities roughly follow, and the results that lean on it.
• Null HypothesisThe 'nothing is happening' assumption a test measures against.
• OutliersValues far from the rest of the data, and when to keep, cap or exclude them.
• Quartiles and IQRSplitting data into quarters to summarise the middle half without letting extremes rule.
• Regression to the MeanWhy the worst week of the year is almost always followed by a better one.
• Sampling MethodsChoosing who or what you measure so the result stands for the whole population.
• The Chi-Square TestComparing counts and proportions between groups, and testing whether categories are independent.
• The t-TestComparing two averages and asking whether the gap is bigger than chance.
• Variance and Standard DeviationHow spread out numbers are, and why the average alone misleads.

Charts & Visualization

• Accessible VisualizationCharts that survive color blindness, small screens, greyscale printing and screen readers.
• Box PlotSummarising a whole distribution with a median box, whiskers and the outliers left outside.
• Chart AnnotationMarking the thing that happened, so a reader is told what the shape means.
• Chart JunkDecoration that adds ink but not information, and quietly slows reading down.
• Color ScalesSequential, diverging and categorical palettes, and the misuse that invents patterns.
• Drill-DownClicking a summary number to see the detail behind it, and the trap of drilling without a question.
• HeatmapColouring a grid of values so patterns across two categories appear at once.
• Pareto ChartBars in descending order with a cumulative line, showing where the few big causes are.
• TreemapNested rectangles sized by value, for showing parts of a whole when there are many parts.
• Truncated AxesStarting a bar chart's value axis above zero, which exaggerates every difference.
• Waterfall ChartStepping from a start value to an end value through the rises and falls in between.

Experiments

• Guardrail MetricsThe numbers that must not get worse while you chase a win somewhere else.
• Holdback GroupA slice of users deliberately left out of a change so its long-term effect stays measurable.
• Novelty EffectA temporary lift that comes from something being new, not from being better.
• Randomised Controlled TrialRandomly splitting subjects to compare an outcome, the design every A/B test comes from.
• Test DurationRunning long enough to cover weekly cycles and novelty, and no longer than you can defend.
• Treatment EffectThe measured difference the change made, and the many reasons it may not be that difference.

Types of Analysis

• Diagnostic AnalysisFiguring out why a number moved.
• Predictive AnalysisEstimating what will happen, and saying how sure you are.
• RFM AnalysisScoring customers on how recently, how often and how much they bought.
• Scenario AnalysisTesting decisions against several plausible futures instead of one forecast.
• SegmentationSplitting users or customers into groups that behave differently.
• Sensitivity AnalysisFinding which assumptions your conclusion actually depends on.

Time & Forecasting

• Forecast IntervalA range around a forecast saying how uncertain it is.
• ForecastingPredicting future values from past patterns, with honest uncertainty.
• Lag FeaturesUsing past values of a series as inputs to predict its future.
• Mean Absolute Percentage ErrorForecast error as a percentage, and where it misleads.
• Moving AverageSmoothing a series by averaging each point with its neighbours.
• Root Mean Squared ErrorForecast error that punishes big misses more than small ones.
• SeasonalityPatterns that repeat on a calendar: weekly, monthly, yearly.
• Time SeriesData ordered in time, and why the order changes everything.
• Trend, Seasonality and NoiseSplitting a series into direction, repeating pattern and leftover.

The Analyst's Toolkit

• Data Quality ChecksThe assertions a dataset must pass before you build numbers on it.
• ImputationFilling blanks with a stand-in value, and what that costs you in certainty.
• Missing DataBlank values that arrive for reasons, and what the reason does to your analysis.
• Reproducible AnalysisProducing the same number again from the same inputs, months later, on someone else's machine.
• Versioning Analysis CodePutting queries and models in source control so a number can be re-run and diffed.

Data Engineering Basics

• Data PipelineA sequence of steps that moves and transforms data.

Machine Learning Basics

• Machine LearningSoftware that learns patterns from data instead of following explicit rules.
• ModelThe learned function that turns inputs into predictions.
• OverfittingA model memorizing its training data instead of generalizing.
• Train / Test SplitHolding out data to evaluate a model honestly.

LLM & AI Engineering

• How LLMs WorkTokens in, next-token prediction out: a mental model of what's happening.
• Prompt EngineeringWriting instructions that get reliable results from a model.

Agile & Delivery Process

• Acceptance CriteriaThe conditions a story must meet to be done.
• User Story"As a user, I want… so that…": a requirement from the user's point of view.

Communication

• Explaining to Non-EngineersTranslating technical trade-offs into business terms.
• Giving a DemoShowing your work clearly to an audience.
• PresentingCommunicating ideas to a group.
• Saying NoDeclining or pushing back on work constructively.
• Stakeholder ManagementKeeping the people affected by your work informed and aligned.
• Working with Product ManagersCollaborating on what to build and why.

Product Thinking

• Edge CasesUnusual inputs and situations that break naive code.
• Experimentation CultureTesting ideas with data before committing to them.
• Feature AdoptionWhether users actually use what you shipped.
• Product ThinkingUnderstanding the user problem behind the ticket.
• RequirementsWhat the software must do.

Junior Habits & First Job

• Done Is Better Than PerfectShipping working software instead of polishing forever.

Backend Basics

• Feature FlagsTurning features on and off without deploying.

Relational Databases & SQL

• Correlated SubqueryA subquery that runs once per row of the outer query.
• EXPLAINShowing how the database plans to run a query.
• Materialized ViewA view whose results are stored and refreshed.

Privacy & Compliance

• Data MinimizationCollecting only the data you actually need.
• Data ResidencyRequirements that data stays within certain countries.
• GDPRThe EU regulation on personal data: consent, access and deletion rights.

Data Modeling for Analytics

• Data VaultModeling with hubs, links and satellites for auditable, change-friendly history.
• Inmon vs KimballA normalized enterprise warehouse first vs dimensional marts first.

Batch & Distributed Processing

• Apache SparkThe most widely used engine for distributed batch and streaming processing.

Transformation & Analytics SQL

• Approximate AggregatesFast, nearly exact counts and percentiles on huge data.

Data Quality & Observability

• Data ObservabilityMonitoring freshness, volume, schema and distributions to catch silent breakage.
• Data ReconciliationChecking that totals match between source and destination.
• Data SLAs and SLOsPromises about when data will be ready and how correct it will be.

Data Governance & Privacy

• Data ClassificationLabeling data by sensitivity: public, internal, confidential, restricted.
• Data MaskingHiding sensitive values while keeping data usable.

Statistics

• Bayes' TheoremUpdating a belief with new evidence, and why 'how likely is that really' beats 'how surprising'.
• Central Limit TheoremWhy averages and rates behave predictably even when the underlying data does not.
• Multiple RegressionFitting several drivers at once so each one is read holding the others still.
• Non-Parametric TestsTests that fall back to ranks when the data does not meet the assumptions of a t-test.
• Regression AnalysisFitting a line or curve so you can describe relationships and make estimates.
• ResidualsWhat the model failed to explain — the leftover that tells you where the fit breaks.
• Sample Size CalculationHow much data you need before starting a test so its result is worth reading.
• Standard ErrorHow much your estimate would wobble if you repeated the measurement on new data.
• Statistical PowerThe chance a test would have detected an effect of the size you care about.

Charts & Visualization

• Dual-Axis ChartsTwo scales on one plot, where the crossing points depend on the scales you chose.
• Small MultiplesThe same chart repeated on a common scale, side by side, instead of one crowded plot.

Experiments

• Bayesian vs Frequentist TestingTwo readings of probability that give different answers to the same experiment.
• Difference-in-DifferencesEstimating an effect by comparing a treated group's change with an untreated group's change.
• Minimum Detectable EffectThe smallest change worth finding, which decides how much traffic the test needs.
• Multi-Armed BanditsLearning the best option while serving, instead of testing then shipping.
• Multivariate TestingChanging several things at once and using the design to separate their effects.
• Pre-registrationWriting down the analysis plan before seeing the data.
• Quasi-ExperimentMeasuring an effect without randomisation, using groups that were naturally separated.
• Randomization UnitWhether you randomize by user, session or device, and how much it changes what you can conclude.

Types of Analysis

• Association RulesFinding which items tend to appear together, and how weak 'tends to' really is.
• Prescriptive AnalysisRecommending actions, not just predictions.
• Survival AnalysisModelling time until an event: churn, failure, conversion.

Time & Forecasting

• ARIMAA classical forecasting model combining autoregression, differencing and moving averages.
• AutocorrelationHow much a series resembles its own past, at each lag.
• Exponential SmoothingForecasting by weighting recent history more than old history.
• Forecast AccuracyJudging forecasts on held-out data, against a baseline.
• Seasonal AdjustmentRemoving the calendar pattern so the underlying movement is visible.
• StationarityWhen a series' statistical behaviour doesn't drift over time.

Communication

• Influence Without AuthorityGetting things done across teams you don't manage.

Technical Leadership

• HiringInterviewing and selecting engineers.
• Interviewing CandidatesRunning fair interviews that produce real signal.
• Onboarding New EngineersHelping new teammates become productive.