Data Science & Analytics Vocabulary: 100 Key English Terms
Most data teams work in English: SQL queries, dashboard labels, experiment write-ups, documentation and stakeholder updates all share one vocabulary. Knowing these terms helps analysts and data scientists read documentation and research faster, explain their results clearly to non-technical colleagues and feel confident in international teams and job interviews.
100 terms
10 topics
46 core terms
200 example sentences
Read, understand, keep going
No account needed. Read the meaning, see where the term is used, then study the examples. Click any word in the lesson for help. You can also select a word and press Alt + Enter.
Data Collection and Cleaning
dataset
noun
Meaning
A collection of related data, usually organised as a table of rows and columns, that is used for analysis or for training a model.
Where you use it
One of the most frequent words in data work. You load, explore, clean, merge and share a dataset; people talk about a training dataset, a public dataset or a large dataset. Usually written as one word, though 'data set' also appears.
Examples
The dataset has about two million rows, so let's take a sample before plotting.
Can you share the cleaned dataset in the team folder?
Usage note
'Data' is pronounced /ˈdeɪtə/ in most of the US and the UK, and /ˈdætə/ by some American speakers; both are accepted.
Common phrases
load a dataset · clean a dataset · public dataset · training dataset · merge datasets
The process of finding and fixing problems in data, such as wrong formats, typos, duplicates and missing values, so that it can be trusted for analysis.
Where you use it
A daily task for analysts and data scientists, often said to take most of a project's time. You do, automate or document data cleaning; the verb form is 'clean the data', and 'clean up the data' is more informal.
Examples
Most of last week went on data cleaning; the date column had three different formats.
Let's automate the data cleaning steps so we don't repeat them every month.
Usage note
'Data cleansing' means the same thing and is common in business and data management texts.
Common phrases
do data cleaning · data cleaning step · data cleaning script · clean up the data
Empty cells or fields in a dataset where no value was recorded, often shown as NULL, NaN or a blank, which need to be handled before analysis.
Where you use it
You check for, count, drop, fill or impute missing values. Analysts often report the share of missing values per column and explain why data is missing, because the reason affects which method is safe to use.
Examples
About 8% of the income column has missing values, mostly from the old sign-up form.
Before you drop rows with missing values, check whether they all come from one region.
Usage note
NULL in SQL and NaN ('not a number') in Python libraries both mark missing values, but they behave differently in comparisons and calculations.
Common phrases
handle missing values · fill missing values · drop missing values · check for missing values
Data in the form in which it was first collected or exported, before anyone has cleaned, combined or summarised it.
Where you use it
You collect, store, export or keep raw data. Teams often keep an untouched raw copy so they can rerun the cleaning steps. 'Raw' contrasts with 'clean', 'processed' and 'aggregated'. Treated as uncountable: 'the raw data is…'.
Examples
Can you send me the raw data, not just the summary table?
We keep the raw data for 90 days in case we need to reprocess it.
Common phrases
raw data file · keep the raw data · export raw data · raw event data
Any place that data comes from, such as a database, an app, a spreadsheet, a survey, an external API or a file sent by a partner.
Where you use it
You connect to, combine, document or question a data source. Analysts often have to explain why two data sources give different numbers. In BI tools, 'data source' also means the connection a dashboard reads from.
Examples
The two data sources disagree because one counts refunds and the other doesn't.
Please list every data source you used in the appendix.
Common phrases
connect a data source · external data source · primary data source · combine data sources
A record that appears more than once in a dataset, for example the same customer or order stored twice, which can make counts and totals too high.
Where you use it
You find, check for or remove duplicates; the process is called deduplication, often shortened to 'dedupe'. Also used as an adjective ('duplicate rows') and a verb ('the join duplicated the orders').
Examples
Revenue looked too high because the table had duplicates for every refunded order.
Always check for duplicates after a join.
Usage note
The noun and adjective end in /-kət/, but the verb ends in /-keɪt/: 'a duplicate' /ˈduːplɪkət/, 'to duplicate' /ˈduːplɪkeɪt/.
Common phrases
remove duplicates · check for duplicates · duplicate rows · duplicate records
How accurate, complete, consistent and up to date data is, and therefore how far it can be trusted when people make decisions.
Where you use it
You check, monitor, measure or improve data quality. Teams set up data quality checks or tests that send an alert when values look wrong. 'Data quality issues' is a polite way to say that the data has problems.
Examples
We found some data quality issues in the March numbers, so please don't share them yet.
A daily data quality check now flags any day with zero orders.
Common phrases
data quality issues · data quality check · improve data quality · poor data quality
To fill in missing values with estimated ones, for example with the column's median, the most common category or a value predicted from other columns.
Where you use it
A technical word used by data scientists and statisticians; the noun is imputation. You impute missing values with or using a method. Good practice is to record which values were imputed, for example in a separate flag column.
Examples
We imputed missing ages with the median for each country.
How did you impute the missing incomes, and how many rows did it affect?
Usage note
Outside statistics, 'impute' means to say that someone has a certain quality or motive, as in 'impute bad intentions'; in data work it only means filling gaps.
Common phrases
impute missing values · impute with the median · imputation method · imputed values
The work of turning messy raw data into a clean, well-structured form for analysis, including reshaping, merging, filtering and fixing formats.
Where you use it
An informal but widely used term, close in meaning to 'data munging' and 'data preparation'. It is broader than data cleaning, because it also covers reshaping and combining. Tools such as pandas, dplyr and SQL are used for it.
Examples
The modelling took a day; the data wrangling took two weeks.
Most of the data wrangling happens in SQL before anything reaches Python.
Usage note
In American English 'wrangle' also means to herd horses or cattle, which is why the word suggests hard, untidy work.
Common phrases
data wrangling skills · do the data wrangling · data wrangling script · wrangle the data
To rescale numeric values to a common range, such as 0 to 1, or to adjust figures so that groups of different sizes can be compared fairly.
Where you use it
In modelling you normalize features so that no single one dominates; in reporting you normalize by population or user count ('per 1,000 users'). The British spelling is 'normalise' and the noun is normalization.
Examples
We normalized the sales figures by store size, so small shops can be compared with large ones.
Normalize the features before you run k-means.
Usage note
In database design 'normalization' means something different: organising tables so that the same data is not stored many times. Make clear which meaning you intend.
Common phrases
normalize the data · normalize by population · normalized score · min-max normalization
A document or table that describes every field in a dataset: its name, meaning, data type, allowed values, source and owner.
Where you use it
You write, maintain, check or update a data dictionary. New analysts are told to read it first, and good teams keep it up to date. In larger companies it often lives inside a data catalog tool.
Examples
According to the data dictionary, status 4 means 'cancelled by customer'.
Please add the new columns to the data dictionary before the release.
Common phrases
maintain a data dictionary · check the data dictionary · data catalog · field definitions
Recording specific user actions in an app or website, such as clicks, page views or purchases, as events with details like time, user and device.
Where you use it
Engineers set it up, while analysts decide which events and properties to track, often in a 'tracking plan'. Google Analytics, Segment, Amplitude and Mixpanel are well-known tools. Broken event tracking is a common reason for strange numbers.
Examples
We can't measure the new button yet because nobody added event tracking to it.
After the app update, event tracking for purchases fired twice.
Common phrases
set up event tracking · tracking plan · track an event · event properties
Collecting data automatically from websites with a program that downloads pages and extracts the parts you need, such as prices or product names.
Where you use it
You scrape a site or build a scraper, often with tools such as Beautiful Soup or Scrapy. Many sites limit scraping in their terms of use, and personal data is protected by laws such as GDPR and KVKK. An official API is usually preferred when one exists.
Examples
We collected competitor prices through web scraping until they released a public API.
The web scraping job broke because the site changed its page layout.
Common phrases
web scraping script · scrape a website · build a scraper · scraped data
A value that is very far from most of the other values in a dataset, for example one order of 50,000 dollars among orders of around 40 dollars.
Where you use it
You detect, investigate, remove or cap outliers. A careful analyst first asks whether an outlier is an error or a real and important case. Common in EDA, data cleaning and when explaining surprising averages.
Examples
The average order value jumped because of a few outliers from wholesale customers.
Don't just delete the outliers; check whether they're data entry errors first.
The first stage of an analysis, in which you summarise and plot a dataset to understand its structure, spot patterns and problems, and form questions to test.
Where you use it
Usually shortened to EDA, said letter by letter. You do EDA at the start of every project, often in a notebook, using summary statistics, histograms and scatter plots before any modelling.
Examples
During exploratory data analysis we noticed that half the customers had no age recorded.
I'll share my exploratory data analysis notebook before we decide on a model.
Common phrases
do exploratory data analysis · EDA notebook · initial EDA · findings from EDA
The general direction in which a value moves over a period of time, such as a steady rise in sales, ignoring short-term ups and downs.
Where you use it
A trend can be upward, downward, flat, long-term or seasonal. You spot, identify or follow a trend, and a metric 'is trending up'. Analysts warn that one or two data points are not yet a trend.
Examples
The downward trend in sign-ups started well before the price change.
One good week isn't a trend; let's see what happens over the next month.
Common phrases
upward trend · downward trend · long-term trend · spot a trend · trending up
A set of measurements of the same thing recorded in order at regular time intervals, such as daily revenue or hourly website visits.
Where you use it
Singular and plural are the same: 'a time series', 'several time series'. Analysts plot, smooth, decompose and forecast time series, looking for trend, seasonality and anomalies. Before a noun it is often hyphenated: 'time-series data'.
Examples
We built a time series of daily orders going back to 2019.
This time series has a strong weekly pattern, with dips every Sunday.
Common phrases
time series analysis · time-series data · forecast a time series · daily time series
A group of customers or users who share certain characteristics, such as country, device or buying behaviour; as a verb, to divide data into such groups.
Where you use it
You analyse, compare or target a segment, or break results down by segment. Analysts often segment results to check whether an average hides different behaviour in different groups. The process is called segmentation.
Examples
The new pricing works well for small businesses but not for the enterprise segment.
Let's segment the results by device; mobile users might behave differently.
Usage note
The stress changes with the part of speech: the noun is SEG-ment, while the verb is usually seg-MENT.
Common phrases
customer segment · segment the data · break down by segment · high-value segment
Describes a distribution that is not symmetrical and has a long tail of values on one side, such as income data where a few people earn far more than most.
Where you use it
Data is 'right-skewed' when the long tail is on the high side and 'left-skewed' when it is on the low side. In right-skewed data the mean is usually above the median. Also used loosely to mean biased: 'the results are skewed'.
Examples
Revenue per customer is heavily skewed, so the median is more useful than the mean.
The survey results are skewed because only our most loyal users replied.
Common phrases
right-skewed · heavily skewed · skewed distribution · skewed results
A small set of numbers that describe a dataset at a glance, such as the count, mean, median, minimum, maximum and standard deviation of each column.
Where you use it
Usually the first thing you calculate in EDA, and most tools have a one-line command for it, such as describe() in pandas. Also called descriptive statistics. You compute, report or check summary statistics.
Examples
The summary statistics show a maximum age of 212, so something's wrong with the data.
Start the report with a table of summary statistics for each region.
A quick, simple test to make sure a result is reasonable before you trust it, for example checking that a total matches a figure you already know.
Where you use it
Very common and informal in data teams. You do or run a sanity check, or 'sanity-check' a number. People ask colleagues 'Can you sanity-check this before I send it?' when they want a second pair of eyes.
Examples
As a sanity check, I compared our revenue total with the finance report.
Could you sanity-check these numbers before I send them to the board?
Common phrases
do a sanity check · quick sanity check · sanity-check the numbers · pass a sanity check
A data point or pattern that clearly differs from what is expected, such as a sudden spike in sign-ups or a day with no orders at all.
Where you use it
Anomaly detection systems monitor metrics and send alerts. You detect, investigate or explain an anomaly, which may come from a real event, a bug or broken tracking. The plural is anomalies and the adjective is 'anomalous'.
Examples
The anomaly on 14 March turned out to be a bot hitting the sign-up page.
Our anomaly detection flagged a 40% drop in payments within an hour.
Usage note
An outlier is usually a single extreme value, while an anomaly can also be an unusual pattern over time. Many people use the two words loosely as synonyms.
Common phrases
anomaly detection · detect an anomaly · investigate an anomaly · data anomaly
A regular pattern in data that repeats over a fixed period, such as higher sales every December, more traffic on weekdays or a dip every summer.
Where you use it
Analysts adjust for seasonality before comparing periods, which is why they compare a month with the same month last year. Weekly and yearly patterns are the most common. Periods on the lunar calendar, such as Ramadan, move about 11 days earlier each year, which complicates comparisons.
Examples
Sales are down from November, but that's normal seasonality, not a real problem.
The forecast model needs to handle both weekly and yearly seasonality.
A table that summarises a larger dataset by grouping rows and columns by categories and calculating totals, counts or averages for each combination.
Where you use it
Best known from Excel and Google Sheets, but the same idea exists in pandas (pivot_table) and in some SQL dialects (PIVOT). You create or build a pivot table; to 'pivot' data means to turn rows into columns.
Examples
I made a quick pivot table of revenue by region and month.
Finance still checks our numbers with pivot tables in Excel.
Common phrases
create a pivot table · pivot table in Excel · pivot the data · pivot by month
The value you get by adding all the numbers in a group and dividing the total by how many numbers there are; the usual meaning of 'average'.
Where you use it
Statisticians say 'mean'; business people usually say 'average'. You calculate, compare or report a mean. Because outliers and skewed data pull it easily, analysts often report it together with the median.
Examples
The mean session length is eight minutes, but most sessions are under two.
Can you add the mean and the median to the summary table?
Usage note
This noun has nothing to do with the verb 'mean' (to have a meaning) or the adjective 'mean' (unkind); the context always makes it clear.
Common phrases
calculate the mean · sample mean · mean value · mean and standard deviation
The middle value when all the numbers in a group are sorted from smallest to largest; half the values are below it and half are above it.
Where you use it
Preferred over the mean for skewed data such as income, house prices or response times, because outliers barely affect it. You report, compare or calculate the median; 'median salary' and 'median time to …' are common phrases.
Examples
We report median delivery time because a few very late orders distort the average.
The median customer places three orders a year.
Common phrases
median value · median income · report the median · median time
The way the values of a variable are spread out, showing which values are common, which are rare and how far apart they are; often viewed as a histogram.
Where you use it
You look at, plot, describe or compare a distribution. Analysts describe its shape with words such as normal, skewed, wide, narrow or long-tailed. Named examples include the normal distribution and the Poisson distribution.
Examples
Let's look at the distribution of order values before we pick a threshold.
The distribution of session lengths has a very long right tail.
Usage note
In business English 'distribution' also means getting products to shops or customers; the context makes the meaning clear.
Common phrases
normal distribution · plot the distribution · skewed distribution · distribution of values
A relationship between two variables in which they tend to change together, often measured by a coefficient from -1 to 1, where 0 means no linear relationship.
Where you use it
You find, measure or check for a correlation; it can be positive, negative, strong or weak. Analysts often remind each other that 'correlation is not causation'. The verb is 'correlate with'.
Examples
There's a strong correlation between app usage and renewal, but we can't say one causes the other.
The correlation between page speed and bounce rate is negative: faster pages lose fewer visitors.
A characteristic that can take different values from one case to another, such as age, city or price; in a table it is usually one column.
Where you use it
You measure, create, recode or compare variables. They are described as numeric or categorical, continuous or discrete, dependent or independent. In programming, 'variable' also means a named value in the code.
Examples
Region is a categorical variable, so we can't take its average.
Let's create a new variable for the number of days between sign-up and first purchase.
A measure of how spread out values are around the mean: a small value means most values are close to the average, a large one means they vary a lot.
Where you use it
Often written as SD, std or the Greek letter sigma (σ). You calculate or report the standard deviation, usually next to the mean. 'Two standard deviations above the mean' is a common way to describe unusual values.
Examples
Delivery times have a mean of three days and a standard deviation of half a day.
Anything more than three standard deviations from the mean is flagged for review.
Common phrases
calculate the standard deviation · high standard deviation · one standard deviation · mean and standard deviation
A smaller group selected from a larger population or dataset and studied in order to draw conclusions about the whole.
Where you use it
You take, draw or select a sample, and a good sample is random and representative. Also a verb: 'we sampled 10% of users'. The whole group being studied is called the 'population'.
Examples
We took a random sample of 5,000 support tickets and labelled them by hand.
The survey sample isn't representative; it's mostly people from big cities.
Common phrases
random sample · take a sample · representative sample · sample data
The value below which a given percentage of observations fall; for example, the 90th percentile of load time is the time within which 90% of page loads finish.
Where you use it
Often written as p50, p90 or p99, especially for response times. The 50th percentile is the median, and the 25th and 75th are the quartiles. People also say a customer is 'in the top 10th percentile', though 'in the top 10%' is clearer.
Examples
The 95th percentile of page load time is 1.8 seconds, while the median is 0.6.
Customers above the 90th percentile of spending get a personal account manager.
A relationship in which a change in one thing directly produces a change in another, rather than the two simply moving together.
Where you use it
Most often heard in the phrase 'correlation does not imply causation'. Analysts establish causation with controlled experiments such as A/B tests, or with careful causal inference methods. The adjective is 'causal'.
Examples
Users who watch the tutorial stay longer, but we can't claim causation without a test.
The A/B test finally gave us evidence of causation, not just correlation.
Usage note
Do not confuse 'causal' (about cause and effect) with 'casual' (relaxed, informal); the two letters are easy to swap.
Common phrases
correlation and causation · establish causation · evidence of causation · imply causation
A screen that shows the most important charts and numbers about a business or system in one place, usually updated automatically.
Where you use it
Built in tools such as Tableau, Power BI or Looker. You build, update, share and maintain a dashboard, and managers check it every day. People talk about a sales dashboard, an executive dashboard or a self-serve dashboard.
Examples
The sales dashboard refreshes every morning at seven.
Can we add a country filter to the marketing dashboard?
The practice of showing data as charts, graphs and maps so that people can see patterns, comparisons and changes more quickly than in a table.
Where you use it
Used both for the skill and for a single chart ('a visualization'). Often shortened to data viz or dataviz in speech. You create, design or present a visualization. British texts spell it 'visualisation'.
Examples
Good data visualization makes the drop in March obvious without any explanation.
She runs the company's internal training on data visualization.
Common phrases
data visualization tool · create a visualization · interactive visualization · data viz
A chart that uses rectangular bars of different lengths to compare values across categories, such as sales by region or users by country.
Where you use it
The most common chart in business reports. Bars can be vertical (often called a column chart) or horizontal, stacked or grouped. Good practice is to start the value axis at zero and sort the bars.
Examples
A horizontal bar chart works better here because the country names are long.
The bar chart makes the gap look huge because the axis starts at 90.
Usage note
A bar chart compares categories; a histogram shows the distribution of one numeric variable. They look similar but answer different questions.
Common phrases
stacked bar chart · horizontal bar chart · grouped bar chart · column chart
A chart that shows the relationship between two numeric variables by drawing one dot for each observation, with one variable on each axis.
Where you use it
Used to look for correlation, clusters and outliers. You plot or draw a scatter plot, sometimes adding a trend line. Also written 'scatterplot' or called a 'scatter chart' or 'scatter graph'.
Examples
The scatter plot of price against rating shows almost no relationship.
Add a trend line to the scatter plot so people can see the direction quickly.
Common phrases
draw a scatter plot · scatter plot with a trend line · scatter plot matrix · plot X against Y
A chart that shows the distribution of one numeric variable by dividing its range into intervals, called bins, and drawing a bar for how many values fall in each.
Where you use it
Usually one of the first charts in EDA. You plot a histogram and choose the number or width of the bins, which can change how the shape looks. The bars touch each other, unlike in a bar chart.
Examples
The histogram shows two peaks, so we probably have two different customer groups.
Try a histogram with narrower bins; the current one hides the detail.
Common phrases
plot a histogram · bin width · number of bins · histogram of values
A chart that connects data points with a line to show how a value changes over time, such as monthly revenue or daily sign-ups.
Where you use it
The standard choice for time series and trends. You plot a line chart, often comparing several lines, but too many lines turn it into a 'spaghetti chart'. Also called a line graph.
Examples
A line chart of weekly orders makes the seasonal pattern easy to see.
There are twelve lines on this line chart; let's highlight just the top three countries.
Common phrases
plot a line chart · multi-line chart · line chart over time · line graph
To move from a summary view to more detailed data, for example from yearly sales to monthly sales and then to single stores, in order to find the cause of a pattern.
Where you use it
Used about dashboards and analysis: you drill down into a number, by region or to the order level. Many dashboards let users click to drill down. The noun is 'drill-down', as in 'a drill-down view'.
Examples
If you drill down by region, you'll see the drop is all in the north.
Can the dashboard let managers drill down to individual stores?
Common phrases
drill down into · drill down by region · drill-down view · drill down to the detail
A chart that shows values in a grid using colours, where darker or warmer colours usually mean higher values, such as orders by day of the week and hour.
Where you use it
Also written 'heat map'. Common for correlation matrices, cohort tables and activity by time. Website heatmaps show where visitors click or how far they scroll. Designers prefer colour scales that colour-blind readers can also follow.
Examples
The heatmap shows that most orders come in on weekday evenings.
I'd use a heatmap for the correlation matrix instead of a table of numbers.
A number that measures something specific about a product, business or process, such as daily sign-ups, average order value or page load time.
Where you use it
You define, track, monitor, report and improve a metric. Teams often disagree about how a metric is defined, so analysts write down the exact formula. 'Success metric' and 'guardrail metric' are common in experiments.
Examples
Which metric are we actually trying to move with this feature?
We changed how the metric is calculated, so the numbers before May aren't comparable.
Usage note
A metric is any measured number; a KPI is a metric the business has chosen as a key target. Every KPI is a metric, but not every metric is a KPI.
Common phrases
define a metric · track a metric · success metric · guardrail metric · move the metric
Short for key performance indicator: one of a small number of metrics that a company or team has chosen to measure progress towards its main goals.
Where you use it
Said letter by letter, plural KPIs. You set, track, hit or miss KPIs; they appear in dashboards, quarterly reviews and performance targets. Managers often ask analysts how a KPI is trending.
Examples
Our main KPI this quarter is the 30-day retention rate.
The team hit all its KPIs except customer satisfaction.
The percentage of users who complete a desired action, such as buying or signing up, out of all the users who had the chance to do it.
Where you use it
One of the most watched metrics in e-commerce and marketing. You track, improve or boost the conversion rate, and people say a page or campaign 'converts well'. Always define the denominator: visitors, sessions or sign-ups.
Examples
The checkout conversion rate dropped from 3.2% to 2.7% after the update.
Is this conversion rate per visitor or per session?
Usage note
A change from 3% to 4% is a rise of one percentage point, but a relative increase of about 33%. Say which one you mean.
Common phrases
improve the conversion rate · checkout conversion rate · low conversion rate · conversion rate by channel
The loss of customers or users who stop using a product or cancel a subscription during a given period; also the rate at which this happens.
Where you use it
Used as a noun ('monthly churn is 4%') and a verb ('customers who churned'). You measure, predict or reduce churn; 'churn rate' and 'churn model' are common. A key idea in subscription, telecom and banking businesses.
Examples
Churn is highest in the first month, so onboarding matters most.
We're building a model to predict which customers will churn.
The ability to keep customers or users coming back over time; also the percentage of them who are still active after a set period, such as 30 days.
Where you use it
The mirror image of churn. You measure, improve or drive retention; analysts show it with retention curves and cohort tables, as in 'day-7 retention'. In HR, 'employee retention' means keeping staff.
Examples
Day-30 retention for the January cohort was 22%.
The new onboarding flow improved retention in the first two weeks.
A group of users who share a starting point or experience in the same period, such as everyone who signed up in January, and who are followed over time.
Where you use it
Cohort analysis compares how different cohorts behave in the weeks or months after they start, usually in a triangular cohort table. You build, compare or track cohorts. Common in product analytics, subscription businesses and medical research.
Examples
Retention in the March cohort is much better than in the February cohort.
Let's do a cohort analysis by sign-up month to see whether the onboarding changes helped.
The series of steps users go through to reach a goal, such as visit, sign-up and purchase; fewer people remain at each step, like the shape of a funnel.
Where you use it
You build, analyse or optimise a funnel and look at where users drop off. Common phrases are 'top of the funnel' (first contact), 'bottom of the funnel' (purchase), 'funnel conversion' and 'drop-off rate'.
Examples
The biggest drop in the funnel is between adding to cart and starting checkout.
Can you build a funnel chart for the new sign-up flow?
Common phrases
conversion funnel · funnel analysis · drop off in the funnel · top of the funnel
The number of unique users who use a product at least once on a given day; one of the standard measures of how engaged an audience is.
Where you use it
Almost always shortened to DAU, said letter by letter. Related measures are WAU (weekly) and MAU (monthly), and the DAU/MAU ratio shows how 'sticky' a product is. Teams must agree on what counts as 'active'.
Examples
Daily active users went up 12% after the new feed launched.
We define daily active users as people who open at least one lesson that day.
Common phrases
DAU/MAU ratio · grow daily active users · monthly active users · DAU chart
The single metric that a company believes best captures the core value its product gives customers, used to align teams around one long-term goal.
Where you use it
Popular in product and growth teams. Examples are weekly active users for a social app or nights booked for a travel site. You choose, define or move your north star metric, and other metrics are treated as inputs to it.
Examples
Our north star metric is the number of lessons completed per week.
Every team should be able to explain how its work moves the north star metric.
Common phrases
define a north star metric · move the north star · input metrics · company north star
An experiment in which users are randomly split into two groups that see different versions of something, so you can measure which version performs better.
Where you use it
Said 'A-B test'. You run, launch, set up and analyse an A/B test, and it also works as a verb: 'we A/B tested the new checkout'. Version A is usually the current one (the control) and B the new one. Very common in product, marketing and growth teams.
Examples
We ran an A/B test on the sign-up button, and the green version won.
Let's not launch the redesign to everyone yet; let's A/B test it for two weeks first.
Usage note
Tests with more than two versions are often called A/B/n tests; testing several page elements in combination is called multivariate testing.
Common phrases
run an A/B test · A/B test results · set up an A/B test · A/B testing platform
A sign that a result is unlikely to be explained by random chance alone, usually judged by whether the p-value falls below a threshold chosen in advance, such as 0.05.
Where you use it
Used in A/B test reports and research. A result reaches or fails to reach statistical significance; the adjective 'statistically significant' is even more common. Analysts remind stakeholders that significant does not automatically mean large or important.
Examples
The new layout increased clicks, but the difference didn't reach statistical significance.
We'll wait until the test reaches its planned sample size before checking statistical significance.
Usage note
In everyday English 'significant' means 'important', but in statistics it only means 'unlikely to be due to chance'. In a very large sample even a tiny effect can be statistically significant.
Common phrases
reach statistical significance · statistically significant result · test for statistical significance · level of significance
A specific, testable statement about what you expect to happen and why, written down before an experiment or analysis starts.
Where you use it
You form, state, test, support or reject a hypothesis. Product teams often use the pattern 'If we…, then…, because…'. The plural is hypotheses /haɪˈpɑːθəsiːz/.
Examples
Our hypothesis is that a shorter form will increase completed sign-ups.
The data didn't support the hypothesis, which is still a useful result.
Common phrases
form a hypothesis · test a hypothesis · support the hypothesis · reject the hypothesis
The group in an experiment that does not get the change being tested, so its results show what would have happened without that change.
Where you use it
Also called 'the control'. In an A/B test the control group sees the current version and the treatment group sees the new one. You assign users to a control group, compare against it or hold one out.
Examples
Conversion in the treatment group was 4.1%, compared with 3.6% in the control group.
We kept 10% of users in a control group that never received the emails.
Common phrases
assign to the control group · treatment and control groups · compare against the control · holdout group
The probability of seeing a result at least as extreme as the one observed if there were really no effect, that is, if the null hypothesis were true.
Where you use it
Written with a hyphen and a lowercase p, often in italics. A small p-value, commonly below 0.05, is taken as evidence against the null hypothesis. You calculate, report or look at the p-value.
Examples
The p-value was 0.03, so the result is significant at the 5% level.
Don't stop the test early just because the p-value dipped below 0.05 on day two.
Usage note
A p-value is not the probability that the hypothesis is true, nor the probability that the result happened by chance alone; this is one of the most common misreadings in data work.
Common phrases
low p-value · calculate the p-value · report the p-value · p-value threshold
The number of observations, people or units included in a sample or an experiment; larger samples usually give more reliable estimates.
Where you use it
You calculate, increase or reach the required sample size. Before an A/B test, analysts estimate the sample size needed to detect an effect of a given size. Often written as n, as in 'n = 1,200'.
Examples
With this sample size we can only detect a lift of 5% or more.
The sample size is too small to say anything about users over 65.
Common phrases
calculate the sample size · small sample size · required sample size · sample size calculator
A range of values, calculated from sample data, that is likely to contain the true value; a 95% interval comes from a method that captures the true value 95% of the time.
Where you use it
Often shortened to CI. You calculate or report a confidence interval, usually at 95%. Analysts present results as 'a lift of 3%, with a 95% CI from 1% to 5%'. A narrow interval means a more precise estimate.
Examples
The confidence interval runs from -1% to 4%, so we can't rule out that there's no effect at all.
Please show the confidence interval on the chart, not just the point estimate.
One of the versions shown to users in an experiment, usually the new version that is compared with the control.
Where you use it
Teams talk about 'the control and two variants' or 'variant B'. You create, launch or roll out a variant and assign users to it; the best-performing one is the 'winning variant'. 'Treatment' means much the same.
Examples
Variant B increased add-to-cart clicks by 6% compared with the control.
We'll roll out the winning variant to all users next week.
Common phrases
winning variant · test variant · variant B · roll out a variant
The default assumption in a statistical test that there is no effect or no difference, for example that a new design does not change the conversion rate.
Where you use it
Written H0 and read 'H-nought' or 'H-zero'. You reject or fail to reject the null hypothesis; statisticians avoid saying that you 'accept' it. Its counterpart is the alternative hypothesis (H1).
Examples
With a p-value of 0.4, we can't reject the null hypothesis.
Our null hypothesis is that free shipping has no effect on order size.
Common phrases
reject the null hypothesis · fail to reject the null hypothesis · under the null hypothesis · alternative hypothesis
The probability that a test will detect an effect of a given size if that effect really exists; tests are commonly designed to have 80% power.
Where you use it
Mostly used when planning experiments: power depends on sample size, effect size and the significance level. A test with low power, called 'underpowered', can easily miss real effects. Closely linked to the minimum detectable effect (MDE).
Examples
With only two weeks of traffic, the test has very low statistical power.
We did a statistical power calculation and need about 30,000 users per group.
Common phrases
power analysis · statistical power calculation · underpowered test · 80% power
Short for Structured Query Language: the standard language for reading, filtering, combining and changing data stored in relational databases and data warehouses.
Where you use it
One of the most requested skills in analyst job adverts. You write SQL, know SQL and run SQL queries. Systems such as PostgreSQL, BigQuery and Snowflake use slightly different versions, called SQL dialects.
Examples
Most of the analysis can be done in SQL before we move to Python.
Could you send me the SQL behind this chart?
Usage note
English speakers use two pronunciations: letter by letter (S-Q-L) or like the word 'sequel'. Teams usually accept either.
A request for specific data from a database, usually written in SQL; as a verb, to ask a database or dataset for that data.
Where you use it
You write, run, optimise and debug a query; a slow query 'times out'. As a verb: 'query the table'. In emails 'query' can also simply mean a question, as in 'a quick query about the report'.
Examples
This query scans the whole events table, which is why it takes ten minutes.
You can query the orders table directly; you don't need the export.
Common phrases
write a query · run a query · slow query · query the database · optimize a query
An operation that combines rows from two or more tables using a shared column, such as a customer ID; as a verb, to combine tables in this way.
Where you use it
You write, do or fix a join, and you join one table to another 'on' a key. The main types are inner, left, right and full outer joins. A wrong join can silently duplicate or drop rows.
Examples
I used a left join so that customers without orders still appear in the result.
Join the orders table to the customers table on customer_id.
Common phrases
left join · inner join · join on a key · join two tables
To combine many rows of data into summary values, such as totals, counts or averages, usually grouped by a category such as day, product or region.
Where you use it
In SQL this is done with functions such as SUM, COUNT and AVG together with GROUP BY. You aggregate data by a category or to a level ('aggregate to weekly'). The nouns 'aggregate' and 'aggregation' are also common.
Examples
Let's aggregate the events by day before we plot them.
The dashboard shows aggregated numbers, so you can't see individual customers.
Usage note
The verb ends in /-geɪt/ ('AG-ri-gate'), while the noun and adjective end in /-gət/ ('AG-ri-git').
Common phrases
aggregate by · aggregate to monthly · aggregated data · aggregate function
To keep only the rows of data that meet certain conditions, such as orders from 2024 or users in Turkey, and leave out the rest.
Where you use it
In SQL you filter with WHERE, or with HAVING after grouping; in dashboards users choose filters from drop-down menus. You filter by something and filter out what you don't want. Also a noun: 'add a filter'.
Examples
Filter out test accounts before you calculate the conversion rate.
The dashboard is filtered to last month by default.
Common phrases
filter by · filter out · apply a filter · date filter
A column, or a set of columns, whose value is unique for every row in a table and is used to identify each row, such as an order ID.
Where you use it
Shortened to PK in diagrams and casual talk. A primary key cannot be empty or repeated. When the same column appears in another table to link the two, it is called a foreign key there. Analysts check the key first when counts look wrong.
Examples
The primary key of this table is order_id, so every order should appear only once.
This events table has no primary key, which is why we keep finding duplicates.
Short for common table expression: a temporary, named result defined with WITH at the start of a SQL query, which makes long queries easier to read.
Where you use it
Said letter by letter. Analysts break complex queries into several CTEs instead of nesting subqueries, and reviewers often ask 'Can you move this into a CTE?' A recursive CTE can walk through hierarchies such as an org chart.
Examples
I rewrote the query with three CTEs, and now it's much easier to follow.
The first CTE gets active users and the second joins their orders.
Common phrases
write a CTE · recursive CTE · WITH clause · chain CTEs
A SQL function that calculates a value across a set of rows related to the current row, such as a running total or a rank, without merging those rows into one.
Where you use it
Written with OVER, often together with PARTITION BY and ORDER BY. Common examples are ROW_NUMBER, RANK, LAG, LEAD and moving averages. A favourite topic in SQL interviews for analyst jobs.
Examples
Use a window function with LAG to compare each month with the previous one.
I used a window function to rank products by sales within each category.
Common phrases
running total · PARTITION BY · rank within a group · moving average
A series of automated steps that moves data from its sources, transforms it and delivers it to a destination such as a warehouse, dashboard or model.
Where you use it
Data engineers build and maintain pipelines; analysts notice when one breaks or runs late. You build, schedule, monitor and fix a pipeline, and people say a pipeline 'failed', 'ran' or 'is down'. In context often just 'pipeline'.
Examples
The dashboard is empty because the data pipeline failed overnight.
We're building a new data pipeline to bring in app events every hour.
Common phrases
build a data pipeline · pipeline failure · maintain a pipeline · pipeline runs daily
A central database designed for analysis that stores cleaned, structured data from many systems, such as sales, marketing and finance, often over many years.
Where you use it
Snowflake, BigQuery and Redshift are well-known examples. Data is loaded into the warehouse and queried from it; people often just say 'the warehouse'. It is built for reading large amounts of data, not for running the live app.
Examples
All the marketing data is now in the data warehouse, so you can join it with sales.
Please don't run analysis on the production database; use the data warehouse instead.
Common phrases
load data into the warehouse · cloud data warehouse · data warehouse table · query the warehouse
Short for extract, transform, load: the process of taking data out of source systems, cleaning and reshaping it, and loading it into a warehouse.
Where you use it
Said letter by letter. Used as a noun ('the ETL') and before other nouns ('ETL job', 'ETL pipeline'). Many modern teams use ELT instead: they load raw data first and transform it inside the warehouse.
Examples
The nightly ETL job loads yesterday's transactions into the warehouse.
The numbers look off because someone changed the ETL logic last week.
The structure of a database or dataset: which tables exist, which columns they have, the data type of each column and how the tables relate to each other.
Where you use it
You design, change or document a schema, and a schema change upstream can break pipelines and dashboards. In many warehouses a schema is also a folder-like group of tables, as in 'the marketing schema'. The plural is usually schemas.
Examples
The app team renamed a column without telling us, and the schema change broke the pipeline.
All the cleaned tables are in the analytics schema.
Usage note
Pronounced SKEE-muh, with a hard 'k' sound; Turkish speakers sometimes say 'shema' by mistake.
Common phrases
database schema · schema change · star schema · document the schema
A large storage system that holds huge amounts of raw data in its original format, structured or not, until someone needs to process and analyse it.
Where you use it
Usually built on cloud storage such as Amazon S3. It is cheaper and more flexible than a warehouse but harder to query. A badly managed lake is jokingly called a 'data swamp', and a design that combines both is called a 'lakehouse'.
Examples
All the raw clickstream logs go into the data lake first.
The data lake has everything, but nobody knows which files are current.
Common phrases
store in the data lake · cloud data lake · data lake storage · lakehouse
Processing data in large groups at scheduled times, for example once every night, rather than handling each record the moment it arrives.
Where you use it
The opposite is stream processing, or real-time processing. Batch jobs run on a schedule, and people talk about the 'nightly batch' or 'daily batch'. Most reporting dashboards rely on batch processing, so their data is a few hours old.
Examples
We use batch processing for finance reports because they only need daily numbers.
With batch processing, today's orders won't show up until tomorrow morning.
One agreed place where a piece of data or a metric is stored and defined, so that everyone in a company works with the same numbers.
Where you use it
Often shortened to SSOT. Data teams build a single source of truth so that sales and finance stop arguing about whose revenue figure is right. It usually means a warehouse table, a metrics layer or one official dashboard.
Examples
The new revenue table is our single source of truth; please stop using the old spreadsheets.
Without a single source of truth, every team brings different numbers to the meeting.
Common phrases
act as the single source of truth · build a single source of truth · SSOT · one version of the truth
The level of detail in data, meaning what one row represents, for example one row per order, per customer per day or per store per month.
Where you use it
Also called the 'grain' of a table. Data can be fine-grained (very detailed) or coarse (summarised). Analysts ask 'What's the granularity of this table?' before a join, because mixing levels leads to double counting.
Examples
The orders table is at order-level granularity, but the targets are monthly.
We store events at the finest granularity and aggregate them later.
Common phrases
daily granularity · level of granularity · finer granularity · grain of the table
A record of where data comes from, which steps and systems it passes through and how it changes along the way, right up to the reports that use it.
Where you use it
Used by data engineers and data governance teams, and often shown as a graph in tools such as dbt or data catalogs. You trace or document lineage, for example to see which dashboards will break if a source table changes.
Examples
The data lineage graph shows that six dashboards depend on this table.
Without data lineage, it took us two days to find where the wrong number came from.
A mathematical representation of a real process, built from data, that is used to explain relationships or to make predictions about new cases.
Where you use it
You build, train, fit, evaluate and deploy a model; a model 'predicts' or 'scores' something. The activity is 'modelling' in British and 'modeling' in American spelling. In business the word also covers spreadsheet models, such as a financial model.
Examples
The churn model flags customers who are likely to cancel next month.
We need to retrain the model because customer behaviour changed after the price increase.
Common phrases
build a model · train a model · fit a model · predictive model · model performance
In modelling, one of the input variables a model uses to make predictions, such as a customer's age, number of purchases or days since last login.
Where you use it
You add, create, select or drop features, and analysts ask which features are most important. Statisticians may say 'predictor', 'independent variable' or 'covariate' for the same idea.
Examples
Adding a 'days since last purchase' feature improved the model a lot.
Which features does the model rely on most?
Usage note
Product teams use 'feature' for a function of an app, such as a new search box. When data and product people are in the same meeting, make clear which meaning you intend.
The examples, with known inputs and usually known answers, that a model learns from; they are kept separate from the data used to test the model.
Where you use it
You collect, label, prepare or split training data. If the training data is biased, out of date or too small, the model will be too. Often mentioned together with validation data and test data.
Examples
The training data only covers 2022, so the model has never seen a holiday season like this one.
We need more labelled training data for the rare complaint types.
Common phrases
label training data · split into training and test data · biased training data · training data set
A problem in which a model learns the training data too closely, including its noise, so it performs well on that data but poorly on new data.
Where you use it
You detect, avoid or reduce overfitting, for example with simpler models, more data, regularization or cross-validation. The verb is 'overfit': 'the model overfits'. The opposite problem is underfitting.
Examples
Training accuracy is 99% but test accuracy is 70%; that's classic overfitting.
With only 200 rows and 50 features, overfitting is a real risk.
Common phrases
avoid overfitting · reduce overfitting · sign of overfitting · prone to overfitting
A family of methods that model how a numeric outcome, such as sales or price, depends on one or more input variables, used to estimate effects or make predictions.
Where you use it
You run, fit or interpret a regression. Linear regression is the classic example; logistic regression, despite its name, is used for classification. In machine learning, 'a regression problem' means predicting a number rather than a category.
Examples
We ran a regression of sales on price and advertising spend.
Predicting delivery time is a regression problem, not a classification one.
Usage note
Software teams use 'regression' for a bug that brings back an old problem after a change; in data work it is a statistical method.
Common phrases
linear regression · logistic regression · run a regression · regression model · regression coefficient
A type of modelling task in which the model predicts which category something belongs to, such as spam or not spam, or which product a review is about.
Where you use it
Binary classification has two classes, such as yes and no; multi-class classification has more. You train a classifier and measure it with accuracy, precision, recall and a confusion matrix.
Examples
Fraud detection is a binary classification problem with very few positive cases.
We're testing three classification models to route support tickets automatically.
Common phrases
binary classification · classification model · multi-class classification · classification problem
The share of all predictions that a model gets right; for example, 90 correct predictions out of 100 means an accuracy of 90%.
Where you use it
The simplest model metric, but misleading when classes are imbalanced: if only 1% of transactions are fraud, a model that always says 'not fraud' is 99% accurate. That is why teams also report precision and recall.
Examples
The model's accuracy is 97%, but it misses most of the cases we care about.
Accuracy dropped from 88% to 81% when we tested on last month's data.
Usage note
In everyday English 'accuracy' and 'precision' are close in meaning, but in model evaluation they are two different metrics.
Common phrases
model accuracy · improve accuracy · test accuracy · high accuracy
Of all the cases a model labels as positive, the share that really are positive; high precision means few false alarms.
Where you use it
Used together with recall to evaluate classification models. Precision matters most when false positives are costly, for example when a customer's card is blocked by mistake. 'Precision at k' is used for ranked results such as search.
Examples
The spam filter has high precision: almost everything it blocks really is spam.
If we lower the threshold, recall goes up but precision goes down.
Common phrases
high precision · precision and recall · improve precision · precision score
Of all the cases that really are positive, the share that a model correctly finds; high recall means the model misses few real cases.
Where you use it
Also called sensitivity or true positive rate, especially in medicine. Recall matters most when missing a case is costly, as in fraud detection or disease screening. Precision and recall usually trade off, and the F1 score combines them.
Examples
The model's recall is only 60%, so it misses four in ten fraud cases.
For this use case we care more about recall than precision.
Usage note
In everyday English 'recall' means remembering something, or a product recall, when a company asks customers to return a faulty product.
Common phrases
high recall · precision and recall · improve recall · recall score
A way of testing a model by splitting the data into several parts, training on all but one, testing on the remaining part and repeating until each part has been used for testing.
Where you use it
The most common form is k-fold cross-validation, for example 5-fold or 10-fold. You run cross-validation to get a more reliable estimate of performance and to compare models. Often shortened to CV in code and notebooks.
Examples
We used 5-fold cross-validation, and the scores were stable across folds.
With time series you can't use ordinary cross-validation, because the model would be trained on the future.
A simple reference point, such as last year's figure, the current version or a very basic model, against which new results are compared.
Where you use it
Used in modelling ('beat the baseline'), experiments ('baseline conversion rate') and reporting ('compared with the baseline'). You set or establish a baseline and measure against it. A model that cannot beat a simple baseline is not useful.
Examples
Our baseline is simply predicting last week's sales for every store.
The baseline conversion rate is 2%, and we want to be able to detect a rise to 2.3%.
Common phrases
beat the baseline · baseline model · baseline conversion rate · compared with the baseline · set a baseline
The work of creating new input variables from raw data to help a model learn, for example turning a date of birth into an age or counting a customer's purchases.
Where you use it
Often the part of a project that improves a model most. Feature engineering relies on domain knowledge; typical steps are ratios, time windows, encoding categories and aggregating events. Finished features are often kept in a 'feature store'.
Examples
Good feature engineering beat the fancy model: a simple ratio of returns to orders did most of the work.
Most of the feature engineering is done in SQL so that it runs in the warehouse.
Common phrases
do feature engineering · feature engineering step · feature store · domain knowledge
The value a model is trained to predict, such as whether a customer will churn or how much they will spend; also called the label or dependent variable.
Where you use it
Often just called 'the target' and written y in formulas and code. You define, predict or encode the target variable. Defining it precisely, such as 'churn within 30 days', is one of the most important decisions in a project.
Examples
Our target variable is whether the customer cancels within 30 days of renewal.
Make sure no feature is calculated from the target variable itself.
Common phrases
define the target variable · binary target variable · predict the target · target leakage
A part of the data that is held back and never used in training, so that it can show at the end how well a model works on data it has not seen.
Where you use it
Data is often split into training, validation and test sets, for example 70/15/15. You hold out a test set and evaluate the model on it. If information from the test set leaks into training, results look better than they really are.
Examples
On the test set, the model's accuracy fell from 92% to 84%.
Keep the last three months as the test set so we can see how the model handles recent data.
Common phrases
hold out a test set · evaluate on the test set · train-test split · test set performance
A method that groups similar data points together without being told the groups in advance, for example to find natural customer segments.
Where you use it
A type of unsupervised learning; k-means is the best-known algorithm. You run clustering, then interpret and name the clusters, which takes business knowledge. The groups it produces are called clusters.
Examples
Clustering split our customers into five groups, including one of bargain hunters.
The clustering results change a lot when we add more features, so let's be careful.
Any person or team with an interest in a piece of work and its results, such as the managers, product owners or clients who will use an analysis.
Where you use it
Analysts mention stakeholders all the time: you gather requirements from, update, manage and present to stakeholders. 'Key stakeholders' are the most important ones, and 'stakeholder management' is a skill listed in many job adverts.
Examples
Before building the dashboard, let's agree on the metrics with the main stakeholders.
Our stakeholders in finance need the numbers by Friday.
A useful finding from data that explains why something is happening or what could be done about it, going beyond simply describing the numbers.
Where you use it
Very common in job titles ('Insights Analyst') and reports. You find, share, draw or deliver insights, and good insights are 'actionable'. Because marketing language overuses the word, careful writers state exactly what the insight is.
Examples
The key insight is that most cancellations happen in the first week.
The report has lots of charts but very few real insights.
Describes decisions, teams or organisations that rely on evidence from data rather than on opinion, habit or intuition alone.
Where you use it
Common in job adverts, strategy documents and company values: 'a data-driven culture', 'data-driven decisions'. Written with a hyphen before a noun. Similar terms are 'data-informed' and 'evidence-based'.
Examples
We want pricing decisions to be data-driven, not based on what competitors did last year.
She helped build a more data-driven culture in the sales team.
Common phrases
data-driven decision · data-driven culture · data-driven approach · become more data-driven
A one-off analysis done quickly to answer a specific question that has just come up, rather than a regular report or a planned project.
Where you use it
A large part of many analysts' week. You get, handle or prioritise ad hoc requests, and too many of them can block bigger projects. 'Ad hoc' is Latin for 'for this purpose' and is also written 'ad-hoc' before a noun.
Examples
Sorry for the delay; I had three urgent ad hoc analysis requests this week.
If this ad hoc analysis keeps coming up, we should turn it into a dashboard.
Common phrases
ad hoc request · ad hoc query · handle ad hoc requests · one-off analysis
The most important point or conclusion that the audience should remember from a presentation, report or meeting.
Where you use it
Very common in business English, especially at the start or end of a presentation: 'The key takeaway is…', 'three key takeaways'. Analysts often put key takeaways at the top of a report so that busy readers see them first.
Examples
The key takeaway is that mobile users convert at half the rate of desktop users.
Start the deck with the three key takeaways, then show the details.
Usage note
In British English a 'takeaway' is also a meal you buy and take home (American 'takeout'). In a report it always means the main lesson.
Common phrases
main takeaway · the key takeaway is · three key takeaways · key takeaways slide
Describes information or findings that are clear and specific enough for someone to act on, for example by changing a process, a product or a budget.
Where you use it
Mostly heard in 'actionable insights' and 'actionable recommendations'. Managers often ask for analysis that is actionable, not just interesting. The word is overused in corporate language, so a concrete example helps.
Examples
The analysis is interesting, but what's the actionable part for the sales team?
We turned the survey results into five actionable recommendations.
Usage note
In legal English 'actionable' describes something that can be taken to court; in data and business writing it simply means 'useful for making a decision'.
Common phrases
actionable insights · actionable recommendations · make it actionable · clear and actionable
Presenting findings from data as a clear story, with context, a few well-chosen charts and a conclusion, so that the audience understands and remembers them.
Where you use it
A skill often mentioned in analyst job adverts and training. Good data storytelling starts with the question, shows only the charts that support the message and ends with a next step. The verb phrase is 'tell a story with data'.
Examples
Her data storytelling is excellent: she turned forty slides into five clear points.
This deck has the numbers but no data storytelling; nobody knows what to conclude.
Common phrases
tell a story with data · data storytelling skills · build the narrative · storytelling with charts
A warning or limitation that the audience should keep in mind when using a result, such as a small sample, missing data or a recent tracking change.
Where you use it
Analysts add caveats to reports and presentations to be honest about uncertainty: 'One caveat is…', 'with the caveat that…'. Too many caveats can hide the main message, so experienced analysts list only the important ones.
Examples
One caveat: the March data only covers three weeks because of the tracking outage.
The results look good, with the caveat that the test ran during a holiday week.
Usage note
Pronounced in three syllables, usually KAV-ee-at; it comes from Latin and is not said like the word 'cave'.
Common phrases
one caveat is · with the caveat that · add a caveat · important caveat
Ten questions from this glossary. Pick the right meaning for each term and see your score.
How to learn data science & analytics vocabulary
Read real texts from the field regularly: the documentation of the tools you use, engineering blogs of data teams, experiment write-ups and well-commented public notebooks.
Learn statistics words in contrasting pairs: mean and median, correlation and causation, precision and recall, sample and population.
Practise describing one chart a day in two or three plain English sentences, as if you were explaining it to a manager who has never seen the data.
Pay attention to the small words around numbers: 'rose by 5 percentage points' is not the same as 'rose by 5%', and 'roughly', 'on average' and 'statistically significant' each say something precise.
Say abbreviations the way teams do: KPI and ETL letter by letter, SQL either as letters or as 'sequel', and A/B test as 'A-B test'.
Frequently asked questions
How can I learn English vocabulary for data science and analytics?
Read real texts from the field and notice how the words are used: tool documentation, experiment write-ups, analytics blogs and the comments in public notebooks. Learn each term with the verbs it goes with (run a query, clean the data, build a dashboard) and use new words in your own query comments, tickets and short summaries of your results.
Which data science terms should I learn first?
Start with the words of daily work: dataset, query, data cleaning, missing values, outlier, metric, KPI, dashboard and stakeholder. Then learn the basic statistics and experiment vocabulary (mean, median, distribution, A/B test, statistical significance) and after that the pipeline and modelling terms. This glossary is ordered the same way, from the most essential terms to the more specialist ones.
Is 'data' singular or plural in English?
Both are correct. 'Data' was originally the plural of 'datum', so scientific journals often write 'the data are'. In business and most tech teams, 'data' is treated as a mass noun like 'information': 'the data is ready', 'how much data', not 'how many data'. A single value is usually called a 'data point', not a 'datum'.
Should I use the Turkish or the English term at work?
Turkish data teams often keep English words such as dashboard, pipeline, churn, query and feature, sometimes with Turkish endings ('query atmak', 'dashboard'a eklemek'). Turkish equivalents such as 'aykırı değer', 'örneklem', 'güven aralığı' and 'veri ambarı' are standard in university courses, academic papers and formal reports. Knowing both lets you move easily between a Turkish meeting and English documentation.
How do I present analysis results in English to non-technical stakeholders?
Lead with the answer, then the evidence. Useful patterns are 'The key takeaway is…', 'We saw a 12% increase in…', 'This is likely driven by…' and 'One caveat is…'. Avoid jargon such as p-values unless the audience asks; say 'we're fairly confident this isn't due to chance' instead, and finish with a clear recommendation or next step.
