Data analysis is the process of applying systematic statistical or logical techniques to describe, illustrate, recap, and test the data. It excludes from purification analysis process that transforms and presents useful information to conclusions and support 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 into a systematic way that will 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 discovered 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, 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 buy.
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 the 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 emphasize action instead of data monitoring
For example, The Google’s self-driving car and Waymo 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, analysis one can generated 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 called 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, and how can you used 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, and 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 and 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 and less than data and arrive and conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Caartio, Metabase, Redash, and Microsoft Power BI, etc.
Interpretation of data: Now a result in your hand after successful analysis and you need to check it out interpreted 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, 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 understandings 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 reducing big dataset by the implementation of new tool and technologies, currently, sellers trust on 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 support 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, stays connected.
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