process of data analytics (Data analysis is a process ) used to determine what types of variables are present in a given dataset. By identifying the variables, we can infer what conclusions can be drawn from the data.
Process of data analytics that uses data to help make decisions. It involves taking data from a variety of sources, analyzing and interpreting the data, and using those insights to make a decision or take an action.

Today, in this article, we will learn more details about data analysis… Follow us.

what is the process of data analytics

Process of data analytics that involves collecting, analyzing, and interpreting data. The data can come from many sources: customers responding to surveys, product reviews, or even a company’s own records.
The first step in the data analytics process is to collect the data you want to analyze. Collecting the data will depend on what you’re looking for and how you plan to use it.
This means using statistical methods to see what patterns emerge in your data set and whether there is any significance (i.e., correlation) between different variables within your dataset.
Finally, once you’ve analyzed your data, you’ll want to interpret what it means—for example: did sales increase after we launched our new marketing campaign? Or did they decrease?
Interpreting what your results mean requires careful thought and analysis so that you don’t draw incorrect conclusions based on faulty assumptions about what happened when (or didn’t happen).

data analysis methods

Data analysis methods are a way of looking at data. In this context, “data” means information that has been collected and organized in a way that is designed to be analyzed.
There are many different types of data analysis methods, but they all serve the same purpose: to take a large amount of information and make it easier to understand.
For example:

  • Measures of central tendency: These are values that summarize a set of numbers by providing an average or middle value of the data set. Examples include mean, median, and mode.
  • Measures of dispersion: These are values that describe how widely dispersed a set of numbers is within a distribution. Examples include variance, standard deviation, and interquartile range (IQR).
  • Distributional statistics: These describe how data is distributed across a distribution’s statistical properties such as shape, center and spread. Examples include histograms and box plots.