GRE data analysis topic constitutes 15% of the entire math section. You are certain to see at least 6 questions from the data analysis section out of the total 40 questions. A major advantage of the GRE data analysis questions is that you wouldn’t require any knowledge about it beforehand. It is merely extraction of data from the given visual and then applying the same to the questions which follow.
The data analysis questions may be given to you in many formats. Some of them are — charts, tables, graphs or extrapolating information from a reading passage. It is important for you to ace all the ways to interpret the data so that you score well in this section of the exam. Some important tips and strategies for solving questions in this section are given below.
Tips and Strategies:

• Spend time to examine the graph, bar, chart or table.
• Read the passage with extra care before you proceed to look at the questions.
• You will have to be on the lookout for identical patterns or figures.
• Ensure that you take notes and do the math clearly.
• Most of the graphical representations such as the line graphs, bar graphs will be displayed with scale information. You must read, estimate and compare quantities according to the corresponding scales.
• You are expected to answer the questions only on the basis of the data which you have been provided. You must not make any assumptions with the knowledge that you have, etc.
• Learn the art of approximation and be careful when handling percentages with data interpretation questions.

The different types of GRE data analysis questions are discussed in depth below :

• Column Charts – These are one of the most important graphs among the data interpretation questions. Also referred to as Bar charts, these have an illustration with rectangular bars with lengths corresponding to the values they represent. It can be plotted vertically or horizontally.  The bar charts can be segmented further into categories on each bar or on equal height columns where each segment could represent a percent.

The below bar chart denotes the vaccination status of individuals with one dose and both doses in different cities

• Box and Scatter Plots – These types of graphs are not often seen in the GRE exam. They are also a bit more difficult to comprehend when compared to the other types of graphs.  They are useful to display bivariate data (measures of two different variables measured on the same set of individuals). For example: income and expenditure of individuals or height and weight of several persons on the same graph.
• Line charts – The line charts are usually used to depict the progress between two quantities.  For example the population rate between two countries, the inflation rate in the last 2 quarters, etc.

This line graph above depicts the rising petrol prices between two countries. The GRE math data analysis questions on this graph would be:  share the percentage of change in prices of petrol in India between 2005 and 2015

• Pie charts – These remain the most popular type of data interpretation questions in the GRE exam. The pie graph is also termed as a circle graph due to its shape and is divided into sectors. Each of these sectors denote measures of a quantity, which would be defined in the question itself. The key factor of the pie chart is that you would understand the data or information given in it very easily since it is straightforward.

The above pie chart is a representation of the Vaccination status of various countries across the world. You would be given questions based on the information given above like — what is the total number of people vaccinated in India given that the total population is 1 million.
Finally, it is always easier to interpret data that is presented in a graph format than in long textual paragraphs. They assist you to learn the data in a short time period. This is what the GRE data analysis questions are aimed at evaluating in the exam. For your preparation, you can download some GRE data analysis pdf practice questions online or from the ETS site.
We trust that this article has given you a brief overview of the various data analysis topics. Prepare well ! Good Luck!