Data analysis is the process of dealing with data to glean useful information. Not everybody can sort the data to draw useful consequences by having only basic level information about different analytical techniques and tools. Analytics must make informed decisions and face challenges in every second step of data analysis. The most difficult part of data analysis, especially of statistical analysis, is to derive and apply the right formula at different steps. Fortunately, there are a number of data analytical tools that can help you in analysis by following simple and user-friendly guidelines. This article is all about describing the seven most sought-after data analytical tools that you can use without paying even a single penny.

Data Analytical Tools ? A Brief Overview:

Data Analytical tools simply refer to the software and applications that analysts can use to extract a scientific story from widely scattered forms of data. It helps in data organisation, sorting, and summarising so companies, businesses, and research organisations can take better decisions based on trends observed in the collected data. The following is a list, along with a brief description of data analytics tools, that you can start using now in academic writing without paying any sign in or registration fees.

Excel:

One of the most basic types of data analytical tools is Excel. Its spreadsheet helps organise raw data into a readable format that makes it easy to extract useful insights. By using built-in formulae and entering your own formula in Excel Spreadsheet, you can easily complete lengthy calculations within a fraction of time. It is easily available, widely used, and easy to learn the software to start on data analysis. The chart and graphs give clear insights to interpret data that will help you in making educated decisions.

SPSS:

SPSS is statistical analysis software that stands for ?Statistical Package for Social Sciences?. It is used for analysing quantitative data that may include descriptive analysis, numeral outcomes, identifying groups, and making predictions. It is widely accepted software around the world due to its English-Like command language helps, which facilitates users to understand it easily. Furthermore, it offers a number of functionalities such as frequencies, bivariate statistics, and cross-tabulation. Apart from all these, another exciting feature of this software is that it is absolutely free.

Minitab:

Minitab is statistical analysis software used worldwide to organise and reduce data to get valuable insights. It is the most popular tool for analysis among quality engineers, statisticians, and Six Sigma practitioners to solve real-life problems. Some of its important features include:

• Capability analysis
• Graphical analysis
• Measurement systems analysis
• Hypothesis tests
• Control charts

Social science students can use Minitab to complete chapter four of a dissertation- ?DATA ANALYSIS?. Minitab complete guides, especially to perform analysis for dissertations, are freely available on the internet. Still you have option to get dissertation help online if you can?t use it yourself for data analysis in your dissertation.

MATLAB:

It offers data types and processing capabilities to engineers and scientists. It is free to use, but technical knowledge is necessary to use it for analysing the data. You can complete the following functions by using the MATLAB:

• Time and Frequency Domain Analysis
• Impulse response
• Nyquist,
• Nichols
• Zero-pole plots.
• Singular value

Power BI:

It is important and powerful business analytical software launched by the Microsoft team. Currently, Power BI is available in three versions, namely Desktop, Pro, and Premium version. It is only the desktop version that you can sign up for free. However, the case is not the same for the Pro and Premium version. With a live dashboard and reports, this software brings life to your data. To put it simply, whenever you need to make decisions with confidence, gain insights from your largest BI deployments, and create an amazing data experience, Power IB must be your first option.

R

R is a tool popular among programmers for statistical modelling, data interpretation or visualisation, and analysis. To learn this tool, you must have some working knowledge about coding. However, the availability of packages such as plyr, dplyr, and tidy data manipulation becomes easy. Additionally, by other packages such as ggvis, lattice, and ggplot, R offersamazing services in terms of visualisation and analysis of data.

Python:

Python was originally designed as an Object-Oriented Programming language for web and software development, but now you can use it in data sciences as well. It is the fastest growing data analytical tool as it offers a great set of libraries for any aspect of scientific computing. It is free to use and easy to learn.

Final Words:

In a nutshell, data analysis is indeed technical. From the selection of the type of data and analytical tool to choose the correct analytical test to perform, everything needs expert-level understanding. However, by using step-by-step guides, you can surely master the analytical skills. The seven above-mentioned data analytical tools are most common to use in 2023, and you must also learn to use them effectively.