Guide to Data Analysis: Tools | Methods | Research

guide to data analysis

Data is a key to any business. Your collected data can help you to make the right decision for the present and provide a good reference for the future.

But you might be stuck on how to get started with this. The primary step is to hire a data analyst or data scientist to play with your data and retrieve the information. But, if you want to learn the basics of data analysis, this article is for you.

This article gives you an overview of data analysis and tools/methods of data analysis. Let’s see the details.

The practice of systematically applying statistical and/or logical approaches to explain and demonstrate, condense and recap, and assess data is known as data analysis. These analytical approaches allow us to derive the underlying inference from data by removing the clutter caused by the remainder.

Depending on the business and therefore the goal of the analysis, there are a variety of methodologies and strategies for doing it. While statistical approaches can be used in qualitative research, data analysis is frequently an ongoing iterative process in which data is collected and processed virtually concurrently.

Data Analysis Tools

Users can utilize data analysis tools to process and alter data, examine linkages and correlations between data sets, and uncover patterns and trends that can be interpreted.

There are different tools and software for data analysis to grow your business and we have listed a few with the download links:

  1. Xplenty
  2. Atlas.ti
  3. Azure HDInsight
  4. Skytree
  5. Splice
  6. Plotly
  7. Apache Spark

Major Data Analysis Methods

Descriptive Analysis

Analyzes all data or a selection of numerical data that has been summarized. For continuous data, it displays the mean and standard deviation, whereas, for categorical data, it displays the percentage and frequency. It accomplishes this by organizing, processing, and interpreting raw data from a variety of sources to transform it into useful business insights.

Text Analysis

It is a type of data analysis that uses databases or data mining techniques to find a pattern in massive data sets. It was once used to convert raw data into business data. Slicing and dicing piles of unstructured, heterogeneous files into easy-to-read, handle, and comprehend data components are part of the process.

Diagnostic Analysis

Diagnostic data analytics enables analysts and corporate executives by assisting them in gaining a thorough contextual picture of what occurred. This analysis helps identify data behavioural trends. The diagnostic analysis is a step beyond statistical analysis in that it provides a more detailed investigation to answer the queries.

Statistical Analysis

Data collection, interpretation, and validation are all part of statistics. Statistical analysis is the process of quantifying data and applying statistical analysis using multiple statistical processes. SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), Stat soft, and other statistical data analysis tools are used.

Predictive Analysis

To uncover significant patterns and trends, predictive analysis utilizes historical data and feeds it into a machine learning model. The model is used to anticipate what will happen next based on the current data. The common uses of predictive analysis are Fraud Detection, Optimizing Marketing Campaigns, Improving Operations, and Reducing Risk.

Prescriptive Analysis

Prescriptive data techniques differ from predictive analysis in that they focus on exploiting patterns or trends to generate realistic, responsive business strategies. Prescriptive analysis, which generates automated conclusions or suggestions, necessitates a specific and distinct algorithmic approach as well as explicit guidance from people who use the analytical tools.

Data Analysis in Research

Researchers utilize research data analysis to reduce data to a story and interpret it to gain insights. The data analysis procedure aids in the reduction of a big amount of data into smaller chunks. The use of deductive and inductive logic to research and data analysis is known as data analysis and interpretation.

During the data analysis process, three important things happen. The first is data organization. The combination of summarization and classification has resulted in the second most often used data reduction method. It aids in the discovery of patterns and themes in data, allowing for easier identification and linkage. The third and final method is data analysis, which can be done top-down or bottom-up.

Types of Data Analysis in Research

Qualitative Data

We call it qualitative data when the data is provided with words and descriptions. Although you can witness this data, analyzing it in a study, especially for comparison, is subjective and difficult. This type of information is typically gathered through focus groups, personal interviews, or surveys with open-ended questions.

Quantitative Data

Quantitative data is any information expressed in numbers of numerical values. This type of information can be categorized, aggregated, measured, calculated, or ranked. You can use graphical representations, charts, or statistical analytic approaches to convey such data.

Categorical Data

The data that’s been organized into groupings. A categorical data item, on the other hand, cannot belong to more than one group. A standard way for analyzing this data is the chi-square test.

Thus, Data Analysis is the process of extracting usable information from data and making decisions based on that knowledge.

If you have more ideas on the basics of data analytics, let us know in the comment section.

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