15 Data Analysis Tools and When to Use Them

Data analysis empowers businesses and professionals to make more informed, data-driven decisions and predictions. To do it, though, they need access to a wide range of data analysis tools that can transform those raw data points into actionable insights. There's a lot to consider when choosing the right tool, but here's what you need to know at a glance:

• You can use data analyst tools throughout the data analytics process. Key purposes, however, include data mining, data visualization, and business intelligence.

• Data analyst tools range from programming languages like R, Python, and SQL to data visualization tools like Tableau, Google Charts, and Datawrapper. Some tools have multiple uses, while others are strictly for specific tasks.

• New technological capabilities are transforming how data analysts do their jobs. AI data analyst tools, in particular, offer new ways for professionals to clean, analyze, and visualize data.

In this article, we'll explore 15 data analysis tools and software, how they differ, and how you can showcase related skills to potential employers. Afterward, if you want to build job-ready data analyst skills, consider enrolling in the Google Data Analytics Professional Certificate.

What are data analysis tools?

Data analysis tools are software programs, applications, and other aids that professionals use to analyze data sets in ways that characterize the big picture of the information and provide usable information for meaningful insights, predictions, and decision-making purposes.

You can think of them as a collection of maps, diagrams, and charts designed to gather, interpret, and visualize data across diverse applications. Choosing the right data analysis tool is essential to maximize your efforts and make it easier to meet your professional goals.

15 powerful data analysis tools and software

With large amounts of data, it can become difficult to understand what it's showing you. With the right data analysis tools, however, you can easily decipher the big picture of your data and also visualize insights so that they're clearly understood by key stakeholders.

Analysts commonly use tools during the following stages of the data analysis process:

• Data mining: Data mining helps users find the key characteristics of their data so they can apply their insights to real-world problems, and data mining software helps automate this process by looking for patterns and trends within the data.

• Data visualization: Data visualization is a powerful way to transform raw data into meaningful and comprehensive visual representations. It provides us with a way to understand complex data patterns, trends, and insights that people might miss in text-based data. Data visualization tools help professionals streamline the data visualization process. You can use these tools to visually manipulate data and create basic to advanced graphical representations.

• Business intelligence: Data analysis is a powerful tool for understanding the story data tells, identifying insights, and then using them to make informed decisions. Businesses can use these insights to enhance their performance, improve customer satisfaction, gain a competitive advantage, and benefit the overall health of their company. Whether you are part of a small or large organization, learning how to effectively utilize data analytics can help you take advantage of the wide range of data-driven benefits.

AI data analysis tools: what you need to know

Generative artificial intelligence is transforming how many data analysts go about their day-to-day work. There's no one way to approach incorporating AI into you workflow but some of the ways data pros are leveraging it include to speed up the data cleaning process, identify quick insights from data sets, and generate code. You don't have to go on a lengthy search to find a data analysis tool with AI capabilities, either. In fact, many of the tools covered below incorporate AI functionality for you to test out.

Read more: Understanding AI Data Analysis

1. Python 

Primary use: Data mining and visualization

Python is routinely ranked as the most popular programming language in the world today [1]. 

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 appeal 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 they have a firm grasp of one of the most important programming languages used in data today. 

Get hands-on with analyzing and cleaning real-world datasets using NumPy and pandas through Google's Data Analysis with Python Specialization.

2. R

Primary use: Data mining and visualization

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:

3. RapidMiner

Primary use: Data mining

RapidMiner is a comprehensive package for data mining and model development. This platform allows professionals to work with data at many stages, including preparation, visualization, and review. This can be beneficial for professionals who have data that isn’t in raw format or that they have mined in the past.  

RapidMiner also offers an array of classification, regression, clustering, and association rule mining algorithms. While it has some limitations in feature engineering and selection, it compensates for its limitations with a powerful graphical programming language.  

This software is suited for people with all types of backgrounds, and you can utilize it across industries for various applications such as manufacturing, life sciences, energy, and health care. Because of its ability to work with previously mined data, this software can be particularly useful if you are a researcher or data scientist working with historical data.

Read more: Classification vs Clustering: What You Should Know

4. Orange

Primary use: Data mining

Orange is a package renowned for data visualization and analysis, especially appreciated for its user-friendly, color-coordinated interface. You can find a comprehensive selection of color-coded widgets for functions like data input, cleaning, visualization, regression, and clustering, which makes it a good choice for beginners or smaller projects. 

Despite offering fewer tools compared to other platforms, Orange is still an effective data analysis tool, hosting an array of mainstream algorithms like k-nearest neighbors, random forests, naive Bayes classification, and support vector machines. 

The platform holds particular value for certain types of professionals with its add-ons. For example, if you work in bioinformatics and molecular biology, you can find tools for gene ranking and enrichment analysis. You can also find tools for natural language processing, text mining, and network analysis that may benefit you depending on your profession.

5. KNIME

Primary use: Data mining

KNIME, short for KoNstanz Information MinEr, is a free and open-source data cleaning and analysis tool that makes data mining accessible even if you are a beginner. Along with data cleaning and analysis software, KNIME has specialized algorithms for areas like sentiment analysis and social network analysis. With KNIME, you can integrate data from various sources into a single analysis and use extensions to work with popular programming languages like R, Python, Java, and SQL. 

If you are new to data mining, KNIME might be a great choice for you. Resources on the KNIME platform can help new data professionals learn about data mining by guiding them through building, deploying, and maintaining large-scale data mining strategies. Because of this, many companies use KNIME to help their employees gain data processing and extraction experience. 

6. Tableau

Primary use: Data visualization and business intelligence

Tableau stands out as a leading data visualization software, widely utilized in business analytics and intelligence.

Tableau is a popular data visualization tool for its easy-to-use interface and powerful capabilities. Its software can connect with hundreds of different data sources and manipulate the information in many different visualization types. It holds a special appeal for both business users, who appreciate its simplicity and centralized platform, and data analysts, who can use more advanced big data tools for tasks such as clustering and regression.

If you are aiming for a career in business analytics or intelligence, Tableau may be a useful skill for asking questions and creating a deeper understanding of your organization’s data. 

Learn more about Tableau in the following video from Google's course, Share Data Through the Art of Visualization, part of the Google Data Analytics Professional Certificate:

7. Google Charts

Primary use: Data visualization

Google Charts is a free online tool that excels at producing a wide array of interactive and engaging data visualizations. Its design caters to user-friendliness, offering a comprehensive selection of pre-set chart types that can be embedded into web pages or applications. The versatile nature of Google Charts allows its integration with a multitude of web platforms, including iPhone, iPad, and Android, extending its accessibility. 

This tool's high customization and user-friendly nature make it ideal for creating compelling data visuals for web and mobile platforms. It’s also a great option for publishing your charts, as the integration makes it straightforward to publish on most web platforms by sharing a link or embedding the link into a website’s HTML code.

8. MySQL 

Primary use: Data mining

MySQL is an open-source relational database management system (RDBMS) used for storing application data, particularly web-based ones. MySQL is popular among websites and is used by such well-known sites as Facebook, X (formerly 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. 

Develop a working knowledge of the MySQL database management system:

9. Datawrapper

Primary use: Data visualization

Datawrapper is a tool primarily designed for creating online visuals, such as charts and maps. Initially conceived for journalists reporting news stories, its versatility makes it suitable for any professional in charge of website management. The tool supports a wide range of outputs, including scatterplots, line charts, stacked bar charts, pie charts, range plots, and various types of maps and tables. Depending on the intended use, Datawrapper offers both free and paid options, allowing flexibility according to your needs and budget.

While straightforward to use, Datawrapper does exhibit some limitations, especially when compared with other tools in its category. The primary constraint is its inability to integrate directly with data sources. This means users must manually input data into the tool, a process that can be tedious and susceptible to mistakes if not performed with precision.

If you’re interested in crafting web graphics and don't mind the manual data input, learning to use Datawrapper could be a worthwhile addition to your skill set.

10. Microsoft Excel and Power BI

Primary use: Business intelligence

Microsoft Excel, fundamentally a spreadsheet software, also has noteworthy data analytics capabilities. Because of the wide enterprise-level adoption of Microsoft products, many businesses find they already have access to it.

You can use Excel to construct at least 20 distinct chart types using spreadsheet data. These range from standard options such as bar charts and scatter plots to more complex options like radar charts and treemaps. Excel also has many streamlined options for businesses to find insights into their data and use modern business analytics formulas.

However, Excel does have its boundaries. If your business needs more robust data visualization tools within the Microsoft ecosystem, Power BI is a great option. Designed specifically for data analytics and visualization, Power BI can import data from an array of sources and produce visualizations in various formats.

11. Qlik

Primary use: Business intelligence

Qlik is a global company designed to help businesses utilize data for decision-making and problem-solving. It provides comprehensive, real-time data integration and analytics solutions to turn data into valuable insights. Qlik’s tools help businesses understand customer behavior, revamp business processes, uncover new revenue opportunities, and manage risk and reward effectively. 

12. Google Analytics

Primary use: Business intelligence

Google Analytics is a tool that helps businesses understand how people interact with their websites and apps. To use it, you add a special Javascript code to your web pages. This code collects information when someone visits your website, like which pages they see, what device they’re using, and how they found your site. It then sends this data to Google Analytics, where it is organized into reports. These reports help you see patterns, like which products are most popular or which ads are bringing people to your site. 

13. SAS 

Primary use: Business intelligence

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:

14. Spotfire

Primary use: Business intelligence

TIBCO Spotfire is a user-friendly platform that transforms data into actionable insights. It allows you to analyze historical and real-time data, predict trends, and visualize results in a single, scalable platform. Features include custom analytics apps, interactive AI and data science tools, real-time streaming analytics, and powerful analytics for location-based data. 

If you are a decision-maker in your organization, such as a marketing manager or data scientist, you might benefit from Spotfire’s scalable analytics platform when visually exploring your data.

15. Jupyter Notebook 

Primary use: Data mining and visualization

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 it 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. 

Who uses data analysis tools?

Professionals across several sectors use data analysis tools to help them convert raw data into valuable insights that drive decision-making and innovation. Here’s a quick snapshot of who uses these tools and why:

• Data analysts: Data analysts use data analysis tools to uncover trends and insights from complex data, which companies can use to guide better decision-making and increase profits.

• Business analysts: Business analysts leverage these tools to help businesses optimize operations and profitability based on data-driven decisions and strategies.

• Project managers: Project managers use data analysis tools for diverse tasks, from budget management to problem-solving and enhancing team productivity.

• Digital marketers: Digital marketers use data analysis tools to craft effective marketing strategies and campaigns, ensuring promotional activities hit the right target.

• Data scientists: Data scientists employ these tools to build new models and processes, enabling data analysts to perform their jobs effectively and helping businesses interpret complex data and resolve intricate problems.

Promote your experience with data analysis tools

Working with data analysis software is a highly valued skill for people who conduct data analysis. Advocating for your data analysis skills can be a strategic move to highlight your capabilities, enrich your professional image, and open up new opportunities. A few ways you can promote your knowledge and expertise in data analysis tools are:

• Add your skills to your resume: Your resume is the perfect place to highlight your proficiency in specific data analysis tools. Under the skills section, list the tools you are familiar with and provide a brief description of the tasks you’ve accomplished using these tools.

• Create a professional portfolio: Showcasing your work on a professional portfolio can be an effective way of demonstrating your skills. This could include successful projects you’ve completed or problems you’ve solved using data analysis tools.

• Utilize LinkedIn and other social profiles: Use platforms like LinkedIn to highlight your skills and knowledge in data analysis so recruiters and employers can find you. Endorsements and recommendations from colleagues or superiors can also add credibility to your skill set.

Learn data analysis tools and skills on Coursera

Data analysis tools are increasingly critical to countless organizations and industries. Build the skills and knowledge you need to thrive in the modern, data-driven world with these resources on Coursera:

• Watch on YouTube: Which Data Analyst Certificate Is Right for You? IBM, Google, Meta, or Microsoft

• Subscribe on LinkedIn: Coursera's Career Chat Newsletter

• Explore Career Academy: Data Analyst Careers

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.

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