A quick and clear explanation of what is Data Analysis.
Types of Data Analysis Explained
The statistical and logical techniques to describe the data, modularize the structure of data, condense the data representation, illustrate with the help of images, tables, and graphs, and evaluate statistical inclinations, and probability data, to derive meaningful conclusions, are known as Data Analysis.
These analytical procedures enable us to provoke the underlying inference from data by eliminating the unnecessary stuff created by the rest of it. The generation of data is a continuous process. Ensuring data integrity is one of the essential components of data analysis.
There are various examples where data analysis is used ranging from transportation, risk and fraud detection, healthcare, and web search, to name a few.
As we have noticed that with the outbreak of the pandemic Coronavirus hospitals is facing the challenge of coping with the pressure of treating many patients, considering data analysis allows monitoring machine and data usage in such scenarios to achieve efficiency gain.
To perform Data Analysis the following are necessary:
- Necessary analytical skills
- Appropriate implementation of data collection methods.
- Determine statistical significance
- Ensure the reliability and validity of data, data sources, and data analysis methods.
- Account for the extent of analysis
Data Analysis Methods
1. Qualitative Analysis
This approach answers questions such as ‘why,’ ‘what’ or ‘how.’ Quantitative techniques such as attitude scaling, standard outcomes, and more. These kinds of analyses are usually in the form of texts and narratives, which might include audio and video representations.
2. Quantitative Analysis
Generally, this analysis is measured in terms of numbers. The data here present themselves in terms of measurement scales and extend themselves for more statistical manipulation.
3. Text analysis
This is a technique to analyze texts to extract machine-readable facts. It aims to create structured data from unstructured content. The process consists of slicing heaps of unstructured files into easy-to-read, and interpreting data. It is also known as text mining.
The uncertainty of human languages is the biggest challenge of text analysis. For example, humans know that “Once in a blue moon” refers to an idiom and conveys to us “to do something rarely”, but if this text is fed to a computer without background knowledge, then it would generate linguistically and sometimes people who don’t know this idiom might have trouble understanding it too.
4. Statistical analysis
Statistics involves data collection, interpretation, and validation. Statistical analysis is a technique for performing several statistical operations to quantify the data. Quantitative data involves descriptive data like surveys and observational data. It is also called a descriptive analysis. It includes various tools to perform statistical data analysis such as SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), and Stat soft, to name a few
5. Diagnostic analysis
The diagnostic analysis is a step that is carried out after statistical analysis to provide more in-depth analysis to answer the questions. It is also referred to as root cause analysis as it includes processes like data discovery, mining, and drill down and drill through.
The functions of diagnostic analytics fall into three categories Identify anomalies, Drill into the Analytics (discovery), and Determine Causal Relationships.
6. Predictive analysis
Predictive analysis uses historical data and feds it into the machine learning model to find critical patterns and trends. This model is applied to the current data to predict what will happen next. Many organizations prefer it as it gives various advantages like volume and type of data, faster and cheaper computers, easy-to-use software, tighter economic conditions, and a need for competitive differentiation.
The following are the common uses of predictive analysis Fraud Detection, Optimizing Marketing Campaigns, Improving Operations, and Reducing Risk.
7. Prescriptive Analysis
Prescriptive analysis suggests you, proceed with the next step of action and outlines what the potential implications could be reached. Prescriptive analysis generating recommendations requires a specific and unique algorithmic clear direction from those utilizing the analytical techniques.
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