7 Data Analysis Software Applications You Need to Know

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]. There’s a lot of data to be sorted, cleaned, analyzed, and visualized. 

There are many software applications and tools available. Cloud-based data warehouses store huge amounts of business data. Some programs are specially designed for data modeling, and software visualizes data in bright colors and diagrams.  

We’ve compiled seven essential data analysis software applications you should know as you begin your data journey.

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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 organizing large data sets, Excel also includes graphing tools and computing capabilities like automated summation or “AutoSum.” Excel also includes Analysis ToolPak, which features 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, analyzing, and visualizing data. If you’re just starting out in data science, you should consider learning more about Excel to prepare for your future career. 

Take this Excel course to learn how to analyze data using filter, sort, and look-up functions:

2. Python 

Python is routinely ranked as the most popular programming language in the world today [2]. 

Unlike other programming languages, Python is relatively easy to learn and can be used for a wide range of tasks, including software and web development, and data analysis. In the world of data, Python is used to streamline, model, visualize, and analyze 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 most important programming languages used in data today. 

Learn how to program and analyze data with Python, one of the most popular data analysis software programs:

3. R 

R is an open-source programming language used for statistical computing and graphics. 

Like Python, R is considered a relatively easy-to-learn programming language. Typically, it’s used for statistical analysis, data visualization, and data manipulation. The statistical focus of R means that it’s well-suited to statistical calculations, while the visualization tools included within R make it a great language for creating compelling graphics like scatter plots and graphs. 

Alongside Python, R is one of the most important programming languages used in data analysis. If you’re considering a career in data, then you might want to spend time learning R. 

Read more: Python or R for Data Analysis: Which Should I Learn?

Watch this video about programming with R and get a preview of the course:

4. Tableau 

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

Tableau is undoubtedly one of the most popular data visualization platforms in the world of business, particularly because it features an easily understood user interface and seamlessly turns data sets into comprehensible graphics. While business users enjoy it because of its ease of use, data analysts like it because it packs powerful tools that can perform advanced analytics functions like segmentation, cohort analysis, and predictive analysis. 

Data visualization is important because it allows data analysts to convey their findings to colleagues and stakeholders who might not otherwise understand them. If you’re considering a future in either business analytics or intelligence, then you might consider learning Tableau to prepare for the professional workplace. 

Build key data visualization skills like storyboarding and dashboards on Tableau:

5. MySQL 

MySQL is an open-source relational database management system (RDBMS) used for storing application data, particularly web-based ones. Popular among websites, MySQL has been used by such popular websites as Facebook, Twitter, and YouTube. 

In data, a Structured Query Language (SQL) is used for managing relational database management systems, which use relational databases usually structured into tables. As a result, data professionals use MySQL to store data securely and perform routine data analysis. While the program has limitations, MySQL typically fits well within many businesses' existing data systems. 

In particular, you should consider learning MySQL if you would like to work in tech on web applications. 

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, analyze, and visualize 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 modeling it. 

Learn SAS to prepare for a career focused on business intelligence or analytics:

7. Jupyter Notebook 

Jupyter Notebook is a web-based interactive environment used to share computational documents or “notebooks.” Data analysts use Jupyter Notebooks to write and run code, clean data, data visualization, machine learning, statistical analysis, and many other forms of data analysis. Furthermore, Juypter Notebook allows users to combine data visualizations, 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. 

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Google’s Data Analytics Professional Certificate is designed for beginners to build job-relevant skills, like how to clean and organize data for analysis, analyze data with spreadsheets, and use SQL, R programming, Tableau, and more.

Or, build on your skills with the Google Advanced Data Analytics Professional Certificate, which focuses on using Python, statistics, and machine learning to analyze data efficiently.