7 Data Analysis Software Applications Every Data Analyst Should Know

Written by Coursera Staff • Updated on

You’ll see a lot of data analysis software out there. This article is designed to help you understand the most common data analysis software available and can help you gain the skills you will need to land an entry-level data analysis position.

[Featured image] Woman at multiple computer screens working on programming

Today, there is more data than ever. In fact, according to research conducted by Statista, the global volume of data created, captured, copied, and consumed is projected to reach 181 zettabytes by 2025 [1]. That’s a lot of data that must be sorted, cleaned, analysed, and visualised. 

Just as the amount of data is growing, so is the amount of software and tools available to work with it. Cloud-based data warehouses store vast amounts of business data. Some programs are specially designed for data modelling, and software visualises data in bright colours and diagrams. Faced with such an overwhelming choice, many inevitably wonder where to start.

Professionals with skills in this area are in high demand, and many experts anticipate data-related jobs will rise in popularity in the upcoming years. Data engineers are amongst the fastest-growing jobs in the UK, according to the 2022 LinkedIn Jobs on the rise report [2].

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

Whilst data analysis software helps streamline many analytic tasks, the number of available software can sometimes feel overwhelming when getting started. Whether you have a background in analytics or are switching career paths, building a strong foundation in certain data analytic software can help you build transferable skills and help you begin your professional journey in data analytics.

Rather than listing all the tools you might encounter in your professional career, we’ve compiled seven essential data analysis software you should know as you begin your data journey.

1. Excel

Microsoft Excel is one of the most common software used for data analysis. In addition to offering spreadsheet functions capable of managing and organising large data sets, Excel also features graphing tools and computing capabilities like automated summation or “AutoSum.” Excel also includes Analysis ToolPak, which has data analysis tools capable of performing variance, regression, and statistical analysis.

Excel’s simplicity and versatility make it a powerful data analysis tool suitable for managing, sorting, filtering, cleaning, analysing, and visualising data. If you’re just starting out in data science, you should consider learning more about Excel to prepare for your future career.

2. Python

Python is routinely ranked as the most popular programming language in the world today [3]. Data Professionals in the UK consider Python as one of the top languages. Unlike other programming languages, Python is relatively easy to learn, making it a good language for entry-level coders. The syntax of Python closely follows the English language, which allows non-coders to understand it more intuitively.

In the world of data, Python is used to streamline, model, visualise, and analyse data using its built-in data analytics tools. One of the key features of Python that appeals to data analytics professionals is its many libraries, such as Pandas and Numpy, which offer a variety of powerful tools for many analytics needs.

Early professionals should learn Python to ensure that they have a firm grasp of one of the leading programming languages used in data today. 

3. R 

R is an open-source programming language used for statistical computing and graphics. R is a free software programme to download and helpful for performing statistical analysis on structured and unstructured data.

Like Python, R is considered a relatively easy-to-learn programming language. Typical uses include statistical analysis, data visualisation, and data manipulation. R’s statistical focus makes it well-suited to statistical calculations, whilst the visualisation tools included within R make it a great language for creating compelling graphics like scatter plots and graphs. 

Alongside Python, R is another vital programming language used in data analysis. If you’re considering a career in data, then you might want to spend time learning R.

4. Tableau 

Tableau is a data visualisation software used primarily for business analytics and business intelligence.

Tableau is undoubtedly one of the most popular data visualisation platforms in the business world, mainly because it features an easily understood user interface and seamlessly turns data sets into comprehensible graphics. Whilst business users enjoy it because of its ease of use, one thing to note is that Tableau does not have a scripting layer, so it is not the ideal data analysis software to manipulate and analyse high-level data. Many data analysts will perform data analysis in R or Python before importing the data in Tableau, which can then be used to create high-quality visualisations. 

Data visualisation is important because it allows data analysts to convey their findings to colleagues and stakeholders who might not otherwise understand them. Knowing this software and other analytical tools can help you take your professional skill set to the next level and stand out when looking for positions in this field. If you’re considering a future in either business analytics or intelligence, then you might consider learning Tableau to prepare for the professional workplace. 

5. Apache Spark 

Apache Spark is a powerful software framework that helps data analysts quickly process and analyse large data sets. This software processes data in several languages, making it compatible with analysis tools such as Python and R. Apache Spark uses RAM instead of local memory, allowing it to process high volumes of information more quickly than many competitors. 

Because of its ability to perform heavy analytics, many data analysts use Apache Spark to develop machine learning and artificial intelligence models. Apache Spark has an existing library of machine learning data analytics algorithms, making it a convenient tool for professionals interested in using these models.

As machine learning and artificial intelligence become more mainstream, many industries are beginning to look for professionals with skills in these areas. Knowing softwares with these capabilities, such as Apache Spark, has the potential to elevate your CV and expand your career opportunities in big data. 

6. SAS 

SAS is a well-known suite of statistical analysis software developed by the SAS Institute for various analytical purposes, including business intelligence, advanced analytics, and predictive analytics.

Analysts use SAS to retrieve, report, analyse, and visualise data. Business intelligence analysts and data analysts more broadly like SAS because it combines a variety of powerful analytic tools in one place and has an intuitive graphical user interface (GUI) that makes it easy to use. Furthermore, SAS is a reliable software suite that allows data analysts to perform much of their work – from managing data to cleaning and modelling it. SAS also has machine learning and artificial intelligence abilities, adding to the versatile functions that make it a popular choice amongst statistical coders in the UK.

7. Jupyter Notebook 

Jupyter Notebook is another open-source web-based interactive environment popular amongst data analysts. It allows analysts to share computational documents or ‘notebooks’. Data analysts also use Jupyter Notebooks to write and run code, clean data, data visualisation, machine learning, statistical analysis, and many other forms of data analysis.

It can be combined with several popular languages and data analysis tools, such as Python, R, and Apache Spark, making it a great programme to learn in conjunction with these popular programmes. Furthermore, Jupyter Notebook allows users to combine data visualisations, code, comments, and numerous different programming languages in one place, allowing for an improved space to document a data analysis process and share them with others.

Whatever your professional data goals, you will likely benefit from using a tool like Jupyter Notebook to work through data problems and share your work with others.

Get started with data analytics

Data analytics is a great career that is likely to expand as the importance of data in our society continues to grow. Learning top data analysis software and tools can help you prepare for a career in this industry and set yourself up for success. Start your data journey today with one of Coursera’s many data analysis professional certificates or specialisations by industry leaders like Google. 

Designed for those without experience, Google’s Data Analytics Professional Certificate will have you job-ready in less than six months. Learn job-relevant skills like cleaning and organizing data for analysis and completing analysis and calculations using spreadsheets and other critical tools. This strong foundation in the basics can help you land your first position and set you up to continue expanding your skill set in this data analysis. 

Article sources


Statista. “Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025, https://www.statista.com/statistics/871513/worldwide-data-created/.” Accessed August 31, 2023.

Keep reading

Updated on
Written by:

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.