CompTIA DataAI: key terms

Flashcards for this exam CompTIA DataAI guide Class dates

• ARIMAAutoRegressive Integrated Moving Average: a forecasting model that combines autoregression, differencing (to reach stationarity) and moving-average error terms.Advanced
• Artificial Intelligence (AI)The field of building computer systems that perform tasks normally requiring human intelligence, such as reasoning, perception and language.Foundational
• Bayes' TheoremA rule for updating the probability of a hypothesis given evidence: P(A|B) = P(B|A) × P(A) / P(B).Intermediate
• Binomial DistributionThe distribution of the number of successes in n independent trials, each with the same success probability p.Intermediate
• BootstrappingEstimating the sampling distribution of a statistic by repeatedly resampling the observed data with replacement.Advanced
• Central Limit Theorem (CLT)The theorem that the distribution of sample means approaches a normal distribution as sample size grows, regardless of the population's shape.Intermediate
• Chi-Square Test (χ²)A test for categorical data: goodness of fit (observed vs expected frequencies) or independence (association between two categorical variables).Intermediate
• ClusteringAn unsupervised task that groups similar data points together so items in a cluster are more alike than items in other clusters.Foundational
• Conditional ProbabilityThe probability of an event given that another event has occurred: P(A|B) = P(A and B) / P(B).Intermediate
• Confidence Interval (CI)A range computed from sample data that, under repeated sampling, would contain the true population parameter a stated percentage of the time.Intermediate
• Confounding VariableA variable that influences both the supposed cause and the effect, creating a spurious association.Intermediate
• Correlation CoefficientPearson's r: a value from -1 to +1 measuring the strength and direction of a linear relationship between two numeric variables.Intermediate
• Correlation vs CausationThe principle that a statistical association between two variables does not prove that one causes the other.Foundational
• CRISP-DMCross-Industry Standard Process for Data Mining: six iterative phases from business understanding through data prep, modeling, evaluation and deployment.Intermediate
• Effect SizeA measure of the magnitude of a difference or relationship, independent of sample size, such as Cohen's d or a correlation coefficient.Advanced
• Expected Value (EV)The probability-weighted average of all possible outcomes of a random variable: E[X] = Σ x × P(x).Intermediate
• Exploratory Data Analysis (EDA)An open-ended investigation of a dataset with summary statistics and visualizations to understand structure, spot anomalies and form hypotheses.Foundational
• Exponential SmoothingA forecasting method that weights observations with exponentially decreasing weights, with variants for trend (Holt) and seasonality (Holt-Winters).Advanced
• HeteroscedasticityNon-constant variance of regression residuals across the range of predicted values.Advanced
• Hypothesis TestingA statistical procedure for deciding whether sample evidence is strong enough to reject a null hypothesis about a population.Intermediate
• ImputationFilling in missing values in a dataset with estimated substitutes.Foundational
• Inferential StatisticsUsing sample data to draw conclusions or make predictions about a larger population, with quantified uncertainty.Foundational
• MulticollinearityHigh correlation among predictor variables in a regression, which makes coefficient estimates unstable and hard to interpret.Advanced
• Multiple Linear RegressionA regression model that predicts a numeric outcome from two or more predictors: y = β0 + β1x1 + ... + βkxk + ε.Intermediate
• Normal DistributionA symmetric, bell-shaped continuous probability distribution fully described by its mean and standard deviation.Foundational
• OutlierAn observation that lies unusually far from the other values in a dataset.Foundational
• P-ValueThe probability, assuming the null hypothesis is true, of observing results at least as extreme as those in the sample.Intermediate
• Poisson DistributionThe distribution of the count of events in a fixed interval of time or space, when events occur independently at a constant average rate λ.Intermediate
• Predictive AnalyticsAnalytics that answers 'what is likely to happen?' using statistical models and machine learning on historical data.Foundational
• Prescriptive AnalyticsAnalytics that answers 'what should we do?' by recommending actions using optimization, simulation and decision models.Intermediate
• Probability DistributionA function describing the likelihood of each possible value of a random variable.Foundational
• ResidualThe difference between an observed value and the value predicted by a model (y - ŷ).Intermediate
• Sampling BiasSystematic error that occurs when some members of a population are more likely to be sampled than others, so the sample misrepresents the population.Foundational
• SeasonalityRegular, calendar-related patterns that repeat over fixed periods, such as daily, weekly or yearly cycles.Foundational
• Selection BiasBias introduced when the way subjects or data are selected relates to the outcome being studied.Intermediate
• Simpson's ParadoxA phenomenon in which a trend in several groups reverses or disappears when the groups are combined.Advanced
• Spearman Rank Correlation (ρ)A nonparametric correlation computed on ranks, measuring the strength of a monotonic relationship.Advanced
• Standard Deviation (SD)The square root of variance, describing typical distance from the mean in the data's own units.Foundational
• Statistical PowerThe probability that a test correctly rejects a false null hypothesis (1 - β); 80% is a common target.Advanced
• Student's t-TestA test comparing means (one sample against a value, or two independent groups) when the population standard deviation is unknown, using the t-distribution.Intermediate
• Time SeriesA sequence of observations recorded at successive points in time, often at regular intervals.Intermediate
• Time Series DecompositionSeparating a time series into trend, seasonal and residual (irregular) components, additively or multiplicatively.Advanced
• Type I ErrorRejecting a true null hypothesis: a false positive, with probability α.Intermediate
• Type II ErrorFailing to reject a false null hypothesis: a false negative, with probability β.Intermediate
• Unsupervised LearningMachine learning that finds structure, groupings or patterns in data that has no labels.Foundational
• VarianceThe average squared deviation from the mean, measuring how spread out data are; units are the square of the data's units.Intermediate
• Z-ScoreThe number of standard deviations a value lies from the mean: z = (x - μ) / σ.Intermediate