Data analysis is the process of applying systematic statistical or logical techniques to describe, illustrate, recap, and test the data. It excludes from the purification analysis process that transforms and presents useful information to conclusions and supports research findings.
Generally, there are four types of data analysis processes used by the researcher from the entire data collection phase to the final observation in the research study theses are Descriptive Analysis, Diagnostic Analysis. Predictive Analysis and prescriptive Analysis.
What is a Statistical analysis of data?
Statistical analysis is the response to the research question “What happened” and these analyses cover the range of processes carried out by researchers from data collection to analysis, modeling, interpretation, and data presentation using a different tool. Statistical analysis is subdivided into two types of analysis. The first is the descriptive analysis which works with complete or summarized data numbers and indicates whether it is possible to perform an incomprehensible operation on that data using frequency and percentage. Second is the inferential analysis works with sample resulting from the complete data and find different conclusions from the same data set by choosing different sampling processes.
Methods of data analysis
There are different methods of data analysis depending upon the type of research carried out and some common methods are as follow:
Descriptive Analysis: It is the process of transformation of raw data in a systematic way that will be easy to understand and presented in an organized way to generate the descriptive result. It provides basic information about variables in a dataset and highlights the potential relationship between variables in graphic and pictorial form.
For Example: Investigate the reason behind a problem and its positive and negative impact on the data collected during a particular period of time and compared it with others.
Diagnostic Analysis: Diagnostic analysis is a reliable condition that uses analytical technologies and tools for the interpretation of data that is considered to discover and found out what happened or root cause analysis of a specific problem.
For Example: Investigate the reason behind a problem and its positive and negative impact on the data collected during a particular period and compared it with others.
Predictive Analysis: Predictive analytics is a branch of advanced analytics that is used to make predictions about unknown future events by using current data or historical data with the help of statistics, modeling, mechanical leasing, and artificial intelligence (AI). It is used to reduce risk, improve actions against the act, use for fraud detection, improving marketing plans by promoting sales and buying.
For example, All airline companies used a predictive data model to set the tick prices from time to time.
Prescriptive Analysis: In this type of analysis the main focus is to find out the best course of action for any pre-specified outcome based on different choices of action and provide a summary of data. In this action analysis, you are free to determine whether need to, then it is time for discovery. The prescriptive analysis is related to both descriptive and predictive analysis but emphasizes action instead of data monitoring
For example, The Google’s self-driving car and Waymo’s self-driving taxi services is an example of prescriptive analysis
Text Analysis: Text Analysis is a technique to analyze the unstructured data and parsing texts to extract machine-readable data from that text. The purpose of this analysis is to produce guaranteed structured data with free content text. In this press slicing and dicing heaps of undeveloped, heterogeneous documents into readable text for its meaning in the assumption, subtext, and symbolism or any other value it reveals. It is also called data mining, text analysis, and information withdrawal process.
For example, A product reviser text from the retailer website written in review comments by the customer in sentimental word or analysis any document can help a business understand what customer like or dislike about your product.
Inferential analysis: It is used to produce the results used from a random / probability sample back to the population from which the sample was collected. This analysis is only required if a sample is collected by the random procedure of sampling and the response range is very high. With Inferential statistics, and analysis one can generate data and generalize about a population.
For example, You might visit an institute and take a group of 150 students and ask them questions about online learning versus classroom learning and call answers by yes and no, use Inferential analysis and calculate the range, percentage of the population like online and off learning.
Process of data analysis used in research
Need of data: Ask yourself why have you found this analysis, where to use it, how can you use it, and can they do so?
Collection of data: Depending on the needs you have identified, it is time to seek the release of dates for data collection. There are different sources of data collection some of them are field observation, case studies, research, interviews, questionnaires, direct observation, focus groups, etc.
Cleaning of data: Not all the data you collect will be useful, so it’s time for you to do a cleaning of data this process is where you produce white spaces, duplicate records, and base errors. Export is to send information for analysis.
Analysis of collected data: This is where you use the data analysis software and other items that are more tools to help you interpret less than data and arrive and conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Caartio, Metabase, Redash, Microsoft Power BI, etc.
Interpretation of data: Now a result is in your hand after successful analysis and you need to check it out interpret it and come out with results in action based on your outcomes.
Visualization of data: Data visualization or demonstration is the fancy way of presentation of your result in a graphical way so that people can read and understand your given information can read the people and understand. You can use graphs, charts, maps, bullet points or any other methods of presentation for your data and this visualization helps you derive valuable insights which help you compare your dataset, and calculation the relationships with another dataset in the related field of study.
Importance of data analysis in research
- Data analysis is an easy way to check those students about their research matter and gives the reader an insight into what updates have been received throughout the entire data and interpretation.
- Data analysis help you to understand your customers, allowing you to change your customer service and support according to their need and built a strong relationship with them
- Data analysis helps you reduce big datasets by the implementation of new tools and technologies, currently, sellers trust a large number of data to provide their value to research and explore information through data mining
- Data analysis also enables the credibility of rediscovery data or new research and provides back to them with reliable references to stand on a theoretical base.
- Data analysis comes up with understanding and interpretation in the form of data analysis without any hum bias and the reader gets a clear and straightforward picture.
- Data analysis also supports you to update your processes and technique, save money and boost your baseline study.
This is all about data analysis and different methods of data processing and hopes these bases and information help you in your search and carrier, KRS is an online learning platform, which brings novel articles from time to time, and stays connected.
Please share and subscribe to our website, so that it can reach all people in need, and for more E-content, or research support, you can find it on our website or you can also write us at [email protected] for a free consultation.
General FAQ of Data Analysis
Q 1. How do you perform data analysis
The process of data analysis typically goes through five iterative phases: identifying the data to be analyzed, collecting the data, cleaning the data in preparation for analysis, analyzing the data, and interpreting the results of the analysis.
Q 2. What are the most important aspects of data analysis
The most important aspect of statistical data analysis is not how to process the data, but what data to use.
Q 3. What is an example of data analysis
A simple example of data analysis is when we make decisions in our daily lives by thinking about what happened last time, or what happens when we make that particular decision.
Q 4. What is the importance of data analysis
Data analysis is important in research because it makes studying data much easier and more accurate and helps researchers interpret the data easily, so they don’t miss anything that might help them derive insights from the data.