Data Mining in Tampa, FL
What is Data Analytics and Data Mining?
Data analytics or data mining can help your organization optimize performance by scientifically analyzing raw data from your students, customers, clients, or employees. It is the process of converting your raw data into information that is useful for decision-making. Data can be collected and analyzed to answer questions, test hypotheses, or disprove theories.
Data may be numerical or categorical and can be collected from a variety of sources. Once data is collected, it must be processed, typically by putting it into a spreadsheet or statistical software program where each row represents the unit of analysis (i.e., customer, client, student, or employee). Once it is processed, it must be cleaned to ensure that it does not contain duplicates or errors that arose from problems in the way in which the data was entered and stored. Data cleansing is the process of correcting these errors.
Once the data are cleaned, two types of analysis can be conducted, descriptive and inferential.
Descriptive Data Analysis
Descriptive data analysis can help your organization understand the patterns, messages, or trends in your data, such as the average or the amount of variability in your data. It can also include data visualization techniques, which will allow you to look at your data in a graphical format.
The objectives of descriptive data analysis are to:
- Develop hypotheses about the causes of the observed patterns, messages, or trends in your data
- Support the selection of appropriate statistical tools and techniques for your data
- Provide a basis for further data collection
This is accomplished by using your data to create:
- Contingency Tables that illustrates the quantitative relationship between two variables
- Scatterplots that demonstrates the correlation between two variables
These graphical illustrations of your data can also be quantified by computing the corresponding statistic.
Inferential Data Analysis
Inferential data analysis produces statistics from your data. These are obtained by applying mathematical formulas or models to your data to identify strong, statistically significant relationships among your variables. For example, a regression model or general linear model can be used to determine what the best predictors of customer/client satisfaction are or if one group of customers or clients are more satisfied than another (e.g., age, sex, etc.).
Inferential Statistics can be used to determine:
- If there are differences in performance between different groups of people
- The best predictors for success
- If a training program has had an impact
- Who is more likely to buy your product
- What characteristics are most likely to predict satisfaction
Statistical Consulting With Over 20 Years of Experience
When you choose
Research Analytics Consulting to conduct your data analytics we will work closely with you to ensure that we fully understand your data and what you are trying to determine from your data. We will thoroughly clean your data. We will work iteratively with you because we know that oftentimes initial analyses lead to a new hypothesis requiring additional analyses. We will utilize state of the art data visualization techniques to clearly and efficiently present your results. Finally, we will provide you with our complete interpretation of your results to allow you to make the most informed decisions for your organization.
