Jan 2, 2019

Learn About Data Analytical Techniques

People who manage businesses are always looking for answers to improve the bottom line. Data analytical techniques can help provide those answers. Statistical data analysis goes deeper than the traditional demographic information used by companies in the development and marketing of products. It provides a solid basis for predicting customer behavior and trends. Data analysis techniques have value beyond the boardroom, too. Researchers can gather data and summarize it for the medical field, political trends, or any other field.

What Is a Data Analysis Method?

Some examples of statistical methods for data analysis include analysis of variance, or ANOVA, to compare three or more information sets, Wilcoxon's rank sum test that ranks and compares values in a test sample, and the Friedman test that detects variations among similar sample sets. After you've studied the types of data analysis tools available and know how to apply them, these data analysis methodologies get pretty exciting. They become like the brushes an archaeologist uses to uncover buried treasure or the magnifying glass a detective uses to expose fingerprints at a crime scene.

What Is the Purpose of Statistically Analyzing Study Data?

There are a lot of reasons to employ statistical techniques for data analysis. First, when you have solid information, it can be used to justify investing in research and development on a new project. Simple data analysis techniques can help you create a marketing plan that puts your message in front of the people who will benefit from it. The statistical methods used to analyze data can help medical researchers and environmentalists uncover trends that affect large segments of the population, such as identifying a cancer cluster caused by contaminated water in a geographic area.

Data Analysis Tools in Research

When you're delving into the different ways to evaluate information in your research, there are a number of software programs that can speed up your work by providing the information in relevant charts and tables. These types of software include, but aren't limited to, SPSS, SAS, R, and G*Power. Each of the programs focuses on delivering a specific informational aspect from the facts you enter. When you're deciding what data analysis to use in your work, your end goal can help direct you to the program or statistical analysis techniques that will best address your needs.

How to Explain Data Analysis

When you're trying to explain this field to others, you might talk about the ways you can apply useful statistics for data analysis to problems. You might also mention the types of data analysis methods you employ in a typical day. However, one of the best ways to explain the use of the statistical method of data analysis is that it uses numbers and facts to solve real human problems.

Statistical tools for data analysis and interpretation open doors for any organization that may benefit from having a broader understanding of mass quantities of accurate information. It also open doors for the people who know how to analyze statistical data. Data analysis tools and techniques let you gather bits of information that might otherwise seem dry and uninspired and turn it into vibrant, exciting reports that provide valuable information to your organization. Explore the Udacity Catalog today to learn about the opportunities in the wide open and exciting analytical field of data analysis methodologies.