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GMAT Available Summer 2026

GMAT^® Data Insights

GMAT Focus Edition Data Insights teaches candidates to parse data sufficiency, multi‑source reasoning, tables, graphics, and two‑part analysis, enabling rapid, accurate answer selection for business school admissions.

Who Should Take This

Graduate‑bound applicants who have mastered quantitative fundamentals but struggle with interpreting complex data sets find this course essential. It equips them with systematic extraction, calculation, and logical evaluation techniques for sufficiency, chart, and multi‑source questions, boosting speed and confidence on the GMAT Focus Edition.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph

Practice Questions

Lesson Modules

Console Simulator Labs

Exam Tips & Strategy

20 Activity Formats

Course Outline

65 learning goals

1 Data Sufficiency

5 topics

Data Sufficiency Framework and Answer Choices

Recognize the five standard Data Sufficiency answer choices and describe the systematic evaluation process: test Statement 1 alone, Statement 2 alone, then both combined.
Identify the question stem type — value question versus yes/no question — and describe how the sufficiency determination differs for each type.
Recognize when a Data Sufficiency statement provides redundant information that is already derivable from the question stem or the other statement.

Arithmetic and Algebra Data Sufficiency

Determine whether given statements about ratios, percents, and fractions provide sufficient data to calculate a specific numerical value or range.
Apply algebraic manipulation to assess whether one or two linear equations with unknowns provide enough constraints to determine a unique solution.
Evaluate Data Sufficiency problems involving inequalities and absolute value to determine whether the given constraints restrict a variable to a single value or a definitive yes/no answer.
Apply word-to-algebra translation to convert Data Sufficiency word problem statements into algebraic expressions before evaluating whether sufficient information exists.
Assess Data Sufficiency problems involving quadratic equations to determine whether the number of solutions and their sign constraints yield a unique answer.

Number Properties Data Sufficiency

Determine sufficiency of statements involving divisibility, factors, multiples, and prime factorization to establish a unique integer value or property.
Apply case testing with even/odd, positive/negative, and remainder properties to prove sufficiency or demonstrate insufficiency in Data Sufficiency problems.
Formulate a minimal set of test cases that efficiently proves whether number property statements are sufficient, using strategic selection of boundary and special-case values.

Geometry and Statistics Data Sufficiency

Determine whether geometric properties stated about triangles, circles, and coordinate geometry provide sufficient information to calculate area, perimeter, or length.
Assess whether statements about mean, median, range, or standard deviation of a data set provide sufficient information to determine a specific statistical measure.
Evaluate complex Data Sufficiency problems requiring synthesis of geometric constraints with algebraic conditions to determine whether a unique configuration exists.

Advanced Data Sufficiency Strategy

Evaluate when combining two individually insufficient statements produces sufficiency by identifying complementary constraints that together eliminate ambiguity.
Formulate an efficient evaluation strategy for Data Sufficiency problems by recognizing patterns (e.g., trap answer C, hidden sufficiency in single statements) to avoid common errors.

2 Multi-Source Reasoning

3 topics

Source Identification and Navigation

Identify the types of sources presented in Multi-Source Reasoning prompts — including emails, memos, data tables, charts, policy documents, and reports — and recognize their informational roles.
Recognize which source tabs contain information relevant to a specific question, distinguishing essential data sources from background or irrelevant sources.

Information Extraction and Integration

Apply reading and data extraction techniques to pull specific values, constraints, and conditions from text-based sources such as emails, policies, and business memos.
Apply data extraction techniques to pull numerical values, trends, and relationships from tables and charts embedded within Multi-Source Reasoning source tabs.
Integrate information from two or more sources to perform calculations or draw conclusions that require combining data not available in any single source.
Apply cross-referencing techniques to match entities, dates, or categories mentioned in one source with corresponding data in another source to answer lookup-style questions.

Multi-Source Inference and Evaluation

Determine whether a yes/no claim about a business scenario can be definitively answered by synthesizing constraints and data from multiple sources simultaneously.
Evaluate conflicting or complementary information across sources to determine which conclusions are supported and which are contradicted by the full body of evidence.
Synthesize information from three distinct sources with varying formats to evaluate a complex business decision scenario involving trade-offs between cost, timeline, and quality constraints.

3 Table Analysis

3 topics

Table Structure and Sorting

Identify the structure of data tables presented on the GMAT, including column headers, row labels, data types (numeric, categorical, percentage), and the sorting functionality available.
Apply column-based sorting to organize table data in ascending or descending order by a selected column to answer ranking and comparison questions efficiently.

Table Calculation and Interpretation

Calculate percentages, ratios, and averages from table data to answer questions about proportions, growth rates, and relative magnitudes across table entries.
Interpret table data to identify trends, patterns, and anomalies across rows and columns, and determine which table entries satisfy specified conditions.
Apply filtering and conditional logic to table data to count entries meeting multiple criteria simultaneously, such as values exceeding a threshold in one column while falling below a threshold in another.
Calculate derived values from table data such as per-unit costs, growth rates between periods, and weighted averages across categories to answer quantitative table questions.

True/False Table Analysis Claims

Evaluate true/false statements about table data by verifying claims about maximum, minimum, median, and range values across sorted or unsorted columns.
Assess whether comparative claims about table entries (e.g., one category exceeding another by a specified margin) are supported by the data after performing necessary calculations.
Evaluate claims requiring inference beyond direct table values, such as determining whether a trend observed in the data would continue, given stated assumptions.
Assess whether a set of true/false claims about a complex table can be answered using only the visible data or whether external information or additional calculation is required to determine truth value.

4 Graphics Interpretation

4 topics

Graph Types and Visual Literacy

Recognize the major graph types used on the GMAT — line graphs, bar charts, scatter plots, bubble charts, pie charts, and stacked area/bar charts — and describe what each type best represents.
Identify and interpret graph components including axes, scales, units, legends, data point labels, trend lines, and grid markings to extract accurate values from graphical displays.
Recognize the relationship between variables depicted in scatter plots, including positive correlation, negative correlation, no correlation, and non-linear relationships.

Data Extraction from Graphs

Apply reading techniques to extract approximate and exact values from line graphs and bar charts, including interpolating between grid lines when precise values are not marked.
Calculate percentage changes, differences, and ratios between data points displayed on graphs to complete fill-in-the-blank statements with dropdown answer choices.
Interpret stacked bar charts and area charts to determine both individual component values and cumulative totals, distinguishing between absolute and proportional representations.
Interpret pie charts and segmented displays to calculate sector proportions, convert between percentages and absolute values, and compare relative sizes of categories.

Scatter Plot and Bubble Chart Analysis

Apply data extraction techniques to scatter plots to identify clusters, outliers, and the approximate line of best fit, and estimate values for individual data points.
Interpret bubble charts where bubble size represents a third variable, extracting relationships among three dimensions of data to answer comparative questions.
Evaluate graphical data to select the correct dropdown value that completes a statement about trends, proportions, or comparisons, choosing from closely spaced numerical options.

Complex Graphics Analysis

Synthesize information from graphs containing multiple data series or overlaid plots to compare trends and identify relationships between different categories or time periods.
Critique conclusions drawn from graphical data by identifying potential misinterpretations due to scale manipulation, misleading axis breaks, or incomplete data representation.

5 Two-Part Analysis

4 topics

Two-Part Analysis Format and Structure

Recognize the Two-Part Analysis question format, including the two-column answer grid, and describe how two linked answer components must be selected from a shared set of options.
Identify whether a Two-Part Analysis problem is quantitative (requiring calculation of two variables) or verbal/logical (requiring identification of two argument components).

Quantitative Two-Part Analysis

Solve quantitative Two-Part Analysis problems by setting up a system of equations or constraints from the problem stem and identifying the pair of values that satisfies all conditions.
Apply ratio and proportion constraints to determine two unknown quantities that together satisfy a stated relationship, such as a cost allocation or a mixture composition.
Evaluate quantitative Two-Part Analysis problems involving optimization or trade-offs, determining which pair of values maximizes or minimizes an objective while satisfying constraints.

Verbal and Logical Two-Part Analysis

Apply logical reasoning to identify two answer components that together strengthen an argument or complete a logical framework in verbal Two-Part Analysis problems.
Determine which two statements from a set of options represent a finding that supports a hypothesis and a finding that undermines it, based on a described research scenario.
Evaluate complex verbal Two-Part Analysis problems requiring identification of both a conclusion and the assumption upon which it depends from a shared list of statements.

Advanced Two-Part Strategy

Formulate an efficient solution strategy for Two-Part Analysis by determining whether to solve for one part independently first or to evaluate answer pairs holistically.
Integrate quantitative and logical reasoning within a single Two-Part Analysis problem that requires both numerical calculation and argument evaluation to determine the correct pair.

6 Cross-Format Data Skills

3 topics

Foundational Data Literacy

Recognize common data presentation formats — tables, charts, text summaries, and hybrid displays — and describe the type of information each format conveys most effectively.
Identify units of measurement, scale markers, and labeling conventions used across different data displays to avoid misreading values by orders of magnitude.

Estimation and Mental Math

Apply estimation techniques to approximate calculations involving large numbers, percentages, and ratios when exact computation is impractical within time constraints.
Determine when estimation is sufficient versus when exact calculation is required by analyzing the spread of answer choices and the precision of the data provided.
Apply benchmarking techniques such as comparing values to common fractions, recognizing percentage equivalents, and using rounding strategically to accelerate mental arithmetic in timed conditions.

Integrated Data Insights Strategy

Evaluate which Data Insights question format (Data Sufficiency, Multi-Source Reasoning, Table Analysis, Graphics Interpretation, or Two-Part Analysis) each problem represents and apply the corresponding solution framework.
Formulate a time management strategy across the Data Insights section by estimating question difficulty, allocating time proportionally, and deciding when to make educated guesses.
Synthesize data literacy, logical reasoning, and quantitative skills to solve novel Data Insights problems that combine elements from multiple question formats.

Scope

Included Topics

GMAT Focus Edition Data Insights section — Data Sufficiency: evaluating whether one or both given statements provide sufficient information to answer a quantitative question, applying the five standard Data Sufficiency answer choices, recognizing statement independence and combined sufficiency, and testing cases to prove insufficiency across arithmetic, algebra, geometry, and number property question types.
GMAT Focus Edition Data Insights section — Multi-Source Reasoning: integrating information from two or three sources presented as tabs (emails, memos, tables, reports, charts), identifying which sources contain relevant data, synthesizing information across multiple text-based and data-based sources to answer yes/no or multiple-choice questions.
GMAT Focus Edition Data Insights section — Table Analysis: sorting and analyzing tabular data with multiple columns and rows, evaluating true/false statements about data trends and relationships, performing calculations using table values such as percentages, ratios, and rankings, and identifying outliers and patterns within structured data sets.
GMAT Focus Edition Data Insights section — Graphics Interpretation: reading and extracting information from line graphs, bar charts, scatter plots, bubble charts, stacked bar and area charts, pie charts, and segmented displays; completing fill-in-the-blank statements about graphical data using dropdown selections.
GMAT Focus Edition Data Insights section — Two-Part Analysis: solving problems with two interrelated components (such as two variables, two quantities, or two elements) where answers must satisfy all stated constraints simultaneously, covering both quantitative and verbal/logical two-part formats.
Foundational data literacy skills: reading data from tables and graphs, understanding scales and axes, interpreting legends and labels, identifying trends and outliers, and performing basic arithmetic with presented data.

Not Covered

Quantitative Reasoning section content including standalone arithmetic, algebra, geometry, and word problems not embedded in Data Insights question formats, which are covered in the separate Verbal and Quantitative domain specification.
Verbal Reasoning section content including Critical Reasoning and Reading Comprehension questions not embedded in Data Insights contexts.
Advanced statistical analysis beyond descriptive statistics, including regression analysis, hypothesis testing, confidence intervals, and inferential statistics not tested on the GMAT.
Data visualization creation and design principles; the GMAT tests interpretation of existing graphics, not creation of new ones.
Programming, spreadsheet formulas, SQL queries, and other technical data manipulation tools.