Data analysis is the process of interpreting the meaning of the data we have collected, organized, and displayed in the form of a table, bar chart, line graph, or other representation. After collecting and organizing data, the next step is to display it in a manner that makes it easy to read.
Different types of graphs are appropriate for different experiments, e.g.: Bar Graph might be appropriate for comparing different trials or different experimental groups. Scatterplot (or Scattergram) might be the proper graph if you're trying to show how two variables may be related to one another, e.g. used to determine if a correlation exists between two data sets, and how strong it is. XY-line graph shows the relationship between your dependent and independent variables when both are numerical and the dependent variable is a function of the independent variable.
Other types of graphs are Histogram used to show frequency and compare items or ideas where each bar represents an interval of values, Line Graph used to show change over time, Pictograph used to show frequency and compare items or ideas, Circle Graph (or Pie Graph) used to show parts or percentages of a whole and so on.
Analysis and Visualization software provide tools to acquire, analyze, and visualize data. Data analysis lets you manage, filter, and preprocess your data with functions for filtering and smoothing, interpolation, convolution, Fast Fourier Transforms (FFTs), curve and surface fitting, multivariate statistics, spectral analysis, image analysis, system identification and so on. Data visualization provides 2-D and 3-D plotting functions as well as volume visualization functions.