A DNP-830A: Data Analysis course focuses on the analysis of data that is grounded in clinical practice and designed to solve practice problems or to inform practice directly. It stresses the use of analytic methods to critically assess gathered evidence to determine and implement the best evidence for practice. Additionally, learners learn to disseminate findings from evidence-based practice and research to improve health care outcomes. Learners are expected to integrate and synthesize core program competencies and specialty practice requirements necessary to demonstrate proficiency in advanced nursing practice.
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What is data analysis?
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. In other words, it is the process of collecting and organizing data in order to draw helpful conclusions from it. This process of performing data analysis involves using analytical and logical reasoning to gain information from the data collected.
Types of information analysis.
1. Descriptive type.
This involves looking at past data and drawing insights from it. This is the most common type of data analysis and is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, etc.
2. Diagnostic type.
The diagnostic analysis involves the process of determining why something happened. It aims to figure out the reason why the results from descriptive analysis happened.
3. Predictive type.
The predictive analysis involves predicting what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future.
4. Prescriptive types.
This type of data analysis involves combining the data found from the previous 3 types of data analysis and forming a plan of action for the organization to face the issue or decision.
The process of data analysis.
In order to effectively analyze your data, you should use the following guidelines. These guidelines include:
· Identify the problems.
In your health care organization data analysis, begin with the right questions. These questions should be concise, measurable, and clear. Most importantly, when designing your questions, ensure that they qualify or disqualify potential solutions to your specific problem.
· Data collection.
The next step after clearly defining your question is collecting your data. As you collect and organize your data, you should keep in mind these crucial points:
- Keep your collected data organized in a good naming convention.
- Appropriate storage system.
· Data cleaning.
After processing and organizing your data, you need to check if it is incomplete, duplicates, and errors in order to avoid problems while entering or storing the data. Data cleaning is the process of preventing and correcting these errors.
The following are the common tasks done in the data cleaning stage:
- Record matching.
- Identifying inaccuracy of data.
- The overall quality of existing data.
- Column segmentation.
· Analyze the data.
Once you have cleaned your data, you should proceed to perform a deep analysis of your data. This begins by manipulating your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in excel.
A pivot table enables you to sort and filter your data by different variables. Moreover, it also enables you to calculate the mean, minimum, maximum, and standard deviation of your data. This initial stage data analysis helps you to focus on better answering your questions and any objections others might have.
· Get insights.
This is the final stage after you have analyzed your data and conducted further research. This stage involves interpreting your results in order to get insights.