100 Data Analysis terms you should know

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

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Absolute value: The distance of a number from zero on a number line.
Accuracy: The degree to which a measurement or calculation agrees with the true or accepted value.
Algorithm: A set of instructions or rules used to accomplish a specific task.
Array: A collection of items stored in a specific order.
Average: A measure of central tendency calculated by summing a set of values and dividing by the number of values in the set

Bar charts: A graphical representation of data using bars of different lengths to represent different values.
Bayesian Analysis: A statistical method that uses prior beliefs about a parameter to update knowledge about it based on new data.
Binomial experiment: An experiment with two possible outcomes, such as a coin flip.
Bootstrapping: A statistical method used to estimate the distribution of a sample statistic by randomly sampling with replacement from the data.
Box plot: A graphical representation of data using a box to show the range and a line inside the box to show the median.
Bubble chart: A chart that uses bubbles to represent data points, with the size of the bubble indicating a third variable.

Categorical data: Data that can be divided into categories, such as gender or color.
Cluster analysis: A data analysis technique that is used to group data into clusters based on similarities.
Coefficient: A numerical value that represents the relationship between two variables.
Cohort Analysis: A method of analyzing data that tracks a group of individuals over time.
Confidence interval: A range of values that is likely to contain the true value of a population parameter with a certain level of confidence.
Continuous: A variable that can take on any value within a given range.
Control chart: A graphical representation of data that is used to monitor the stability of a process.
Correlation: A statistical measure of the relationship between two variables.

Dashboard: A visual representation of data that provides an overview of key metrics.
Data: Information that can be analyzed and used to make decisions.
Data Analysis: A process of cleaning, translating, analysing, and visualizing data for the purpose of getting insights for informed decisions.
Data Collection: The process of gathering data.
Data profiling: An analysis of the data to understand the data elements, their relationships, and the quality of the data.
Data visualizations: The use of charts, graphs, and other visual representations to display data.
Data wrangling: The process of cleaning, organizing, and transforming data to make it ready for analysis.
Database: A collection of data organized in a specific way.
Data frame: A two-dimensional table in which each row represents a case and each column represents a variable.
Data cleaning: The process of identifying and correcting errors in data.
Dataset: A collection of data used for analysis.
Descriptive Analysis: A type of statistical analysis that summarizes and describes the characteristics of a dataset.
Dependent variable: A variable that is being studied and is affected by the independent variable(s) in an experiment or study.
Discrete: A variable that can only take on certain values, such as integers.
Discriminant Analysis: A statistical method used to identify the factors that discriminate between two or more groups of individuals.
Distribution: The pattern or spread of a set of data.
Dummy variables: Variables used in statistical modeling that take on the values of 0 or 1 to represent the presence or absence of a certain category.

Experiment: A study in which an independent variable is manipulated and the effects on a dependent variable are observed.

Factor Analysis: A data analysis technique that is used to identify the underlying factors that influence a particular phenomenon.
False negative: A type of error in which a test fails to detect a condition that is present.
False positive: A type of error in which a test detects a condition that is not present.
Forecasting: The process of making predictions about future events or conditions.

Heat Map: A graphical representation of data that uses colors to indicate the magnitude of a value.
Histogram: A chart that shows the distribution of a set of data using bars of different heights.
Hypothesis: A statement or prediction about a relationship between variables that can be tested using data.

Inferential Analysis: A type of statistical analysis that uses sample data to make inferences about a population.
Inlier: A data observation that lies within the rest of the dataset and it is unusual or an error.
Interquartile range: A measure of the spread of a dataset that is calculated by subtracting the first quartile (Q1) from the third quartile (Q3).
Intersect: The point or points where two or more sets of data overlap.
Interval: A range of numerical values.

Lambda: A statistical parameter used in some models.
Likert data: Data collected using a Likert scale, a type of rating scale often used in surveys.
Line chart: A chart that shows the relationship between two variables using a series of connected points.
Logistics Regression: A statistical method used to predict a binary outcome using one or more independent variables.
Lower bound: The smallest value in a range.

Market Basket Analysis: A method of analyzing data to identify which items are frequently purchased together.
Matrix: A two-dimensional array of numbers, used in mathematics and computer science.
Mean Absolute Deviation: A measure of the variability of a dataset that is calculated by taking the average of the absolute differences between each data point and the mean.
Mean Imputation: A method of handling missing data by replacing it with the mean of the available data.
Multivariate Analysis: A type of statistical analysis that looks at the relationships between multiple variables at the same time.
Multilevel Modelling: A type of statistical modeling that is used to analyze data that has a nested structure, such as data collected from individuals within groups.

Null values: Missing or undefined data values.
Numerical variable: A variable that can take on any numerical value.

Observation: A single data point or case in a dataset.
Oddities/Outliers: Data points that are significantly different from the rest of the data.
Ordinal: A variable that can take on values that can be ranked or ordered, such as low, medium, and high.
Outcome: The result or effect of an experiment or study.
Outlier: A data point that is significantly different from the rest of the data.
Overplotting: The phenomenon where multiple data points overlap in a plot, making it difficult to see the underlying pattern.
Oversampling: The practice of increasing the frequency of minority class examples in a dataset to balance the class distribution.

P value: A statistical measure that represents the probability of obtaining a test statistic as extreme or more extreme than the one observed, assuming the null hypothesis is true.
Parameter: A numerical value that describes a characteristic of a population or a model.
Pareto chart: A chart that shows the relative importance of different factors by plotting the cumulative percentage of a variable along the y-axis and the items that make up that variable along the x-axis.
Population: The entire group of individuals or items of interest in a study or experiment.
Principal Component Analysis: A statistical method used to identify the underlying structure of a dataset by finding the linear combination of variables that explains the most variance.
Probability: A measure of the likelihood of an event occurring.

Qualitative: A variable that is not numerical, such as a categorical variable.
Quantitative: A variable that is numerical, such as a continuous variable.
Quartiles: The three points that divide a dataset into four equal parts: the first quartile (Q1), the second quartile (Q2), and the third quartile (Q3).

R-squared: A measure of the proportion of the variability in a dependent variable that is explained by the independent variable(s) in a regression model.
Random Sampling: A method of selecting a sample of data in such a way that each data point has an equal chance of being selected.
Range: The difference between the largest and smallest values in a dataset.
Ratio: A type of variable that can take on any non-negative value and represents a relationship between two quantities.
Reciprocal: The reciprocal of a number is the number that when multiplied by the original number gives a result of 1.

Sampling: The process of selecting a subset of data from a larger dataset.
Scatter plot: A chart that shows the relationship between two variables by plotting individual data points as coordinates on a graph.
Set: A collection of items or data points.
Sigmoid: The Sigmoid function is a mathematical function having an “S” shaped curve.
Simulation: The process of using a model to imitate a real-world process or system.
Standard deviation: A measure of the spread of a dataset, calculated as the square root of the variance.
Structural Equation Modelling: A multivariate statistical method that is used to test a theoretical model of relationships between variables.
Syntax error: An error in the structure or formatting of a programming code.

Ticks: The marks on an axis that indicate the values of the data.
Time Series: A type of data that is collected at regular intervals over time.
True Negative: In a binary classification problem, a true negative is an outcome where the model correctly predicts the negative class.
True Positive: In a binary classification problem, a true positive is an outcome where the model correctly predicts the positive class.

Under Sampling: The practice of reducing the number of examples in the majority class in a dataset to balance the class distribution.
Upper bound: The largest value in a range.

Variable: A characteristic or quantity that can take on different values.
Variance: A measure of the spread of a dataset, calculated as the average of the squared differences between each data point and the mean.