10 Data Analysis Tools and When to Use Them

Written by Coursera Staff • Updated on

Learn about 10 data analysis tools that may benefit you in your professional career. Plus, discover key differentiators between common tools so you can find the right one for you.

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Data analysis is an important process for businesses and professionals to find key insights from their data so they can make informed decisions and predictions. In today’s data-driven world, professionals increasingly rely on data analysis to make competitive decisions, engage effectively with their target audience, and successfully lead projects. One way to meet these targets is through the use of data analysis tools. 

By understanding which tool is right for your purpose, you can meet the needs of your project while boosting your overall productivity and efficiency. In this article, we will explore 10 data analysis tools, how they differ, and how you can showcase related skills to potential employers. 

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 describe 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.


10 powerful data analysis tools

With large amounts of data, it can become difficult to understand what the data is showing and produce useful insights. With the right data analysis tools, you can decipher the big picture of your data and then represent it in an appropriate format for stakeholders and people within your professional network with broad backgrounds.

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 this knowledge to real-world problems, and data mining software helps automate this process by looking for patterns and trends within the data. Three common data mining software you may benefit from include the following.

  • 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 and using it 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.

1. 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.

2. 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 make 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.


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. 

4. 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 to help you to ask questions and create 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:

5. Google Charts

Primary use: Data visualization

Google Charts is a free online tool that excels in 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 embed 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, with its high customization and user-friendly nature, makes it ideal if you are looking to create compelling data visuals for web and mobile platforms. It’s also a great option if you need to publish your charts, as the integration makes it straightforward for you to publish on most web platforms by sharing a link or embedding the link into a website’s HTML code.

6. 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.

7. 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.

8. 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. 

9. 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. 

10. 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.

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.

Getting started with Coursera

You can learn more fundamental data analytics tools through the Google Data Analytics Professional Certificate or Google Business Intelligence Professional Certificate on Coursera. Not only will you have the opportunity to learn basic analytics skills that can prepare you for an entry-level job, but you will be able to learn these skills at your own pace in as little as six months.

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