Data Analysis Software: Things to Know

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There was a time when “Time is money” was a popular motto. Due to the incredible speed of technology in the last two decades, it has now become "Data is currency". If you want to make a career in data analysis or any field of data, its golden age is going on right now.

Data Analysis Software Things to Know

Everything that is happening in the world at every moment is data or information.

Everything happening around us is data or information. Posting a status on Facebook, sending money through mobile, downloading a movie or reading news online – each one is a piece of data about you.

At first glance, these may seem like small unrelated tasks, but these data are influencing us by controlling the environment around us. Data analysis is the method of combining such messy, meaningless, unintelligible data to extract useful information, patterns, statistics, etc. from it.

Data analysis is a small part of data processing. After analysis, the data can be made more meaningful and interesting through sound and visualization. Also, data mining, big database creation, data computation etc. can be done with its different parts. For now, in this article I will discuss some popular software used for data analysis.


Composed of two words “Statistics” and “Data”, STATA is one of the simplest and powerful data analysis software. It is widely used in various social research, market research or health related research. Among the dedicated data analysis software available on the market today, STATA offers relatively inexpensive and straightforward data entry and processing. Also its multipurpose surface allows you to copy-paste multiple “Do-files” simultaneously, allowing you to work on multiple projects simultaneously.

Suppose you want to find a mathematical measure of what percentage of people in Dhaka city travel by public transport. Accordingly, you have information on various characteristics of a certain number of people, such as their gender, age, occupation, average number of times they travel by transport, at what time - etc. Now by entering these data into STATA software, you will get your desired results with just one click.

STATA brings new updates regularly to stay ace in the competitive market as well as solve any errors at the fastest time. Also here other code like Python code can be embedded without any problem. As a result, there is no difficulty in reproducing data and coding again from there.

Statistical Package for the Social Sciences (SPSS)

The name itself tells what this software can work with. SPSS is a unique software for social science teachers, students and researchers. In addition, SPSS is a very efficient software for market research, health sector, education and large institutional and ethnographic research and survey analysis.

Numeric output of any quantitative research can be obtained using SPSS. Some of the basics of statistics such as mean, frequency, standard deviation, relationship between variables, correlation, regression, factor analysis can be used to analyze large and complex datasets very easily and display them through graphs and charts. SPSS is also in demand for large hypothesis data testing.

For example, let's assume that an organization is doing a detailed study on whether people who have recovered from the coronavirus in Rajshahi division have developed immunity at all. They have data of 50 thousand people living in Rajshahi division. Now by analyzing such a large amount of data, SPSS can be used to find a relatively accurate answer.

One of the biggest problems with SPSS is that it does not know the details of how the software is calculating after inputting any data or dataset. Only the result has an indication of which equation is solved. That is enough for social science research. That is why the software is popular among scholars but for the same reason the demand for SPSS is somewhat less in the job market. So if you want to build a career in Analytics, only knowing SPSS is not very convenient.

R (Programming Language)

R is primarily a programming language but its use in the world of data analysis is vast. Its main difference with the above two softwares is that instructions are inputted by coding. A footprint of the code is always saved when it executes. As a result, another coder can see the record and immediately understand how it was coded.

That's why using this language it is very easy to work with someone else and reproduce data. And once the data analysis is done, R has excellent graphic image and visualization libraries to display it. From where it is possible to create interesting output of data by selecting the desired template.

Also read : python programming complete guide pdf

Another important reason why R is so popular is that as it is an open source platform, R's readymade data package is more readily available than any other language.

And with its extensive community network, help is easily available if you get stuck or have a problem. Knowing R is a must if you want to work with statistics and data. That's why the demand for R is very high in statistics, data mining, data science. But if someone learns R without any idea about programming, it will become quite difficult.

Because R programming language is more difficult than many other languages ​​such as Python. So it is better to understand basic programming and coding before starting data analysis in R.

Microsoft Excel

There are very few people who are interested in data analysis but have never used Excel. Entering data, collecting data by cutting tables, calculating large numbers - many small to large tasks can be done in an instant with Excel. But in our day-to-day work we mostly use the very basic features of Excel where its data analysis tools are rarely used.

Excel's most popular tool for data analysis is its Pivot Table. Data can be summarized and interpreted in any way from simple to complex by inputting data into this table; Data can be organized by collecting valuable information by instantly observing huge datasets. Pivot charts and slicer can also be used to create dashboards and visualize data very easily.

There is nothing new to say about the use of Excel in the corporate world. In various industries, Excel's data analysis tools are widely used for market research, market evaluation, employee evaluation and development measurement.

But Excel has a bit of a bad reputation when it comes to working with large amounts of data. In some older versions of Excel, after analyzing large datasets, in most cases, the output does not come with accurate information. Another major issue is Excel's storage capacity. In many cases, when users update Excel or change devices, there is a big risk of losing old data. Many big companies have suffered huge losses after this trouble.

Python (Programming Language)

Python is called the "language of the present" - Python alone has occupied a huge space, leaving behind all the best programming languages ​​in the market today. Coders, Scientists, App/Software Developers, Game Developers, Data and Big Data Analysts and even school-college students are creating fun programs using Python these days.

Python is said to be the most suitable programming language for beginners because of its "beginner friendly" syntax. Also, any problem can be solved very quickly by coding few lines in Python than in any other programming language. Being an open source language, all the benefits like open source syntax, shareability, portability and a large community can be found here.

But simplicity is not the only ability of Python, but because of its complex and big data analysis capabilities, big companies like Google, Netflix, BitTorrent use Python. Google coded in Python to add new features to their search engine. However, while Python has the upper hand in desktop computer programming, Python's position in mobile app development is very weak, even non-existent. So if someone is specifically interested in developing mobile applications, Python is not the right programming language for them.

But beyond that, Python has a huge market where there will be no shortage of work for a skilled analyst. So if you can master Python well with a little concentration, but the endless doors of the job market are opening for you - from the freelancing marketplace to the big companies like Apple, Microsoft!


Tableau is a popular Business Intelligence (BI) software for data exploration and visualization. Data can be analysed, visualized and shared using only software tools without any coding or IT hassles. Also, it supports data from software like Microsoft Excel, Oracle, Google Analytics, Salesforce.

However, being an open source software, Tableau also has some limitations. One of them is that the user cannot import customized visuals created in any other software according to his needs. Here he has to rely on the specific stock of Tablo.

Although Tableau is free for personal use, the software has to be purchased at a high price for powerful functionality with more features. Currently Tableau has 3 subscription features namely Tableau Desktop (open to everyone), Tableau Server (Analytics for Enterprises), Tableau Online (Analytics Hosting Software for Enterprises).


The beginning of this programming language is MATLAB, which combines the words “Matrix” and “Laboratory” to solve complex problems by matrix computation. Compared to all the data analysis software we have discussed so far, Matlab is far more complex and sophisticated in terms of capabilities and types of work. Here numerical computation and data visualization work simultaneously.

Mathematicians, scientists and engineers rely on MATLAB for its powerful programming language, despite its advantages in terms of data analytics. But the biggest hurdle for beginners to learn MATLAB is its cost.

Matlab is more expensive than all the other software mentioned here and there is no free version in the market. Also, the software is too large in size for the task, thus taking up a lot of space on the user's computer as well as slowing it down.

Finally, for people who are interested in working with data and want to pursue a career in this sector, Matlab is such a powerful platform that learning any other programming language or software will not be too difficult if mastered.

Power BI

Microsoft Power BI is a Business Intelligence (BI) platform that provides nontechnical business users with tools to collect, analyze, visualize, and share data.

Power BI's interface will look familiar to Excel users. Also, it doesn't take much to learn because of its compatibility with other Microsoft products.

Power BI has a free version for small to medium-sized businesses. And for larger organizations there is a subscription fee based professional version called Power BI Plus. This software helps to connect disparate datasets, create data models by transforming and cleaning data, and create charts or graphs by providing visuals of data. And this entire process can be shared with everyone in the organization.

Apart from the software discussed above, there are many other types of data analysis software available in the market. Each of which targets a specific task or category, packed with different benefits and features. A professional selects software according to his needs and capabilities. Therefore, to be a competent data analyst, after learning about some basic tools, one or more specialized software must be proficient.

The world of data is as vast as it is ever-changing. So keep abreast of new updates, new software and technology, so that everything is at your fingertips. So what is the delay? Start fiddling with your favorite data analysis software before interest fades.

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Oct 30, 2017 — 1. R Programming · 2. Tableau Public: · 4. SAS: · 5. Apache Spark · 6. Excel · 7. RapidMiner: · 8. KNIME · 9. QlikView.
Dec 1, 2021 — The Top 9 Data Analytics Tools for Data Analysts · 1. Microsoft Excel · 2. Python · 3. R · 4. Jupyter Notebook · 5. Apache Spark · 6. SAS · 7.
Additional Data Analysis Software — HubSpot offers a Marketing Analytics Software that can measure the performance of the marketing campaigns. It is an all- ...

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