Over the years, the holy grail of Data Analytics was mainly restricted to a few niche businesses that owned proprietary business intelligence tools. Back in the day, data generation was complex, and so was its collection and curation. Then came the big data revolution propelled by hardware bloom. This gave rise to Data Analysis Tools.
Data Analytics Tools went mainstream as insights allowed companies to gain a competitive advantage. Initially, companies that had superior Analytics with structured data differentiated themselves, but due to Machine Learning techniques, organizations that leverage insights from unstructured data are ahead of the pack. As a result, Data Analysis Tools are now integrating Artificial Intelligence for understanding natural language to provide in-depth insights in a flash.
This article provides a comprehensive guide on the best Analysis Tools in research. It also gives a brief overview of Data Analysis and Types of Data Analysis and its benefits to any organization. Moreover, you will understand some of the critical factors to choose the best Data Analysis Tools for your use case. Read along about how you can leverage these tools for your organization.
Prerequisites
Before diving into the popular Data Analysis Tools, it is important you understand the below concepts beforehand:
- Working knowledge of Analytics Dashboards.
- Difference between Structured & Unstructured Data.
What is Data Analysis?
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Data Analysis supports businesses in improving their products and services to boost client satisfaction. Data Analysis is gathering and arranging large amounts of data to extract useful information that aids in making key business choices. In general, Data Analytics examines data and generates predictions to extract useful information.
Data Analytics is concerned with analyzing and processing existing datasets statistically. Data Analysts might focus on developing ways for processing, capturing, and organizing data to uncover meaningful insights for existing business problems and use cases. This is done while determining the best approach to deliver the facts in an understandable manner. In this article, you will further discover the popular Data Analysis Tools used in the industry.
Understanding the Types of Data Analysis
Data Analysis is a process of analyzing structured and unstructured data to gain insights into in-house or public information. While structured data represents a standardized or tabular format, unstructured data are text, video, and audio. However, assessing different types of data is not straightforward due to several challenges like missing information, outliers, and noisy data.
To get started with Data Analysis Tools, companies have to ensure that they enhance the quality of data before finding insights for decision-making. Any misleading conclusion can negatively impact business operations, which can directly affect profitability. Consequently, organizations spend a lot of resources on improving the quality of data before using different types of Data Analysis techniques — Descriptive, Diagnostic, Predictive, and Prescriptive Analysis.
1) Descriptive Analysis
It focuses on finding the average, mean, and median to gain an overall idea of various business operations. Organizations implement descriptive analysis for assessing sales volume, user base, and growth by summarising the collected information.
2) Diagnostic Analysis
After obtaining an eagle-eye view with Descriptive Analysis, the next step involves finding the driving force behind the results. Analysts drill down by filtering the information based on essential categories to gain a different perspective about the outcomes.
3) Predictive Analysis
In evaluating business performance with Descriptive and Diagnostic Analysis, to focus on the future outcome, companies use Predictive Analysis. Several Machine Learning models are created to showcase the trends based on the collected information. This allows decision-makers to assimilate whether they can accomplish their business goals.
4) Prescriptive Analysis
The idea with the above three Analyses is to find insights, but with Perspective Analysis, companies go a step further and find how new possible actions can change the results in the future.
To further simplify the statistical output, analysts represent the information in the form of visualizations. The adage, a picture is worth a thousand words, perfectly describes modern data analysis, where plots and graphs are used to create dashboards and reports for simplifying insights delivery.
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Difference Between Data Analysis, Data Mining & Data Modeling
The objective of Data Analysis is to discover answers to particular problems. Data Analysis is the extraction, cleaning, transformation, modeling, and visualization of data to extract meaningful and usable information to aid in the generation of conclusions and decisions.
Data Mining is the process of identifying patterns in data. Various mathematical and computational techniques are applied to the data to generate new data and patterns. Data Mining is also known as Knowledge Discovery in Data.
Data Modeling refers to how businesses organize and manage their data. Data is processed using a variety of methods and procedures. On a traditional software project, you can utilize Data Modeling approaches like an ERD (Entity Relationship Diagram) to investigate high-level concepts and how they interact throughout the organization’s information systems.
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How to Choose a Data Analysis Tool?
As Big Data covers such a wide range of use cases and applications, it’s difficult to come up with a definite set of selection criteria for selecting the best Data Analysis Tools. Here are some important factors to consider to choosing the best Data Analysis Tools:
- Align with your Business Objectives: Your Analytics platform should be able to serve both current and future business needs. You must determine your company’s key objectives and establish a list of desired organizational outcomes. Select an Analytics platform that gives you access to data and reporting tools that will help you meet your company goals.
- Keep a track of your Budget: You must be completely informed of the budget connected with the Data Analysis Tools you are reviewing, including subscriptions, growth, and hidden fees, before picking an Analytics tool. Varied Analytics systems have different cost structures, which you should be aware of before making a purchase.
- Check the User Experience: Self-service Analytics should have a user-friendly interface that can accommodate a variety of users. Even non-technical users must be able to build and interpret dashboards and reports with ease.
- Support for Integrations: You must consider if a standalone solution or an integrated solution is best for your company when choosing an analytics tool. With standalone solutions, you have several alternatives to choose from, but with integrated solutions, you can access Analytics from apps that your clients are already acquainted with. You should also consider how readily your data can be transferred to other platforms if the need arises.
- Look for Scalability: Cloud-based Analytics tools are built to start small and scale up as your company grows. These pay-as-you-go solutions can help early-stage enterprises gain a competitive advantage and assist them during periods of rapid expansion. Because every organization has its own set of needs, you must choose an Analytics solution that suits them. You should also consider if the solution can be modified or expanded to suit both current and future requirements.
- Identify Security Standards: You must assess your Analytics provider’s and vendor’s security to verify that the required safeguards are in place to protect your data. Establish standard security controls and procedures at all levels to restrict which people or groups have access to which data.
Benefits of Data Analysis Tools
By instigating Data Analysis Tools, organizations can expedite decision-making, gain new customers, enhance customer experience, and become future-proof. Some of the benefits of Data Analysis Tools are:
- Expedite Decision-Making: Since Data Analysis Tools can empower every professional with self-service analytics, organizations can expedite decision-making with data literacy. Users can ask different questions and unearth insights that can revolutionize business operations and sales performances.
- Gain Customers: By analyzing data from public domains, including social media, organizations can identify changing needs of users. This will allow companies to stay abreast of the changing requirements by adding new products/services to keep acquiring new uses.
- Enhance Customer Experience: Data Analysis Tools help you assess shortcomings in the delivery of products/services and allow you to find what users dislike. With Data Analysis Tools, you can perform advanced analyses like sentimental analysis to discover how customers feel about your products, allowing you to serve users better.
- Become Future-Proof: With insights, organizations make informed decisions to obtain better results in the future. For years, companies relied on the intuition of decision-makers, but with data analysis, they can eliminate inaccurate assumptions and work with clarity.
Read the next section to know the best Data Analysis Tools used widely in the industry.
Top 25 Data Analysis Tools
Now that you have a basic idea of Data Analysis and its importance to any organization, it is now time to analyze the Best Data Analysis Tools. Some of these tools are:
1) Power BI
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Microsoft’s Power BI is the most widely used Data Analysis tool. Power BI has been in the market since the very beginning of the data revolution. While many Data Analysis Tools faded out, Microsoft has ensured Power BI kept on evolving and catering to changing business needs. Started as a straightforward Analytics tool, Power BI is now equipped with Machine Learning capabilities for sentimental analysis and integrates effortlessly with other platforms for streamlining the analytics workflows. Power BI also goes a step further by converting insight into action with Microsoft Power Platforms to build business applications.
With Power BI, organizations can add sensitivity labels to data, comply with data privacy rules, and enhance collaboration among departments. At Microsoft Build 2021, the company also announced a library to integrate Power BI reports in the Jupyter notebook, one of the popular data science tools. Similar evolutions of Power BI have been recognized by Gartner, which made it a leader in Analytics and Business Intelligence platforms for 14 consecutive years.
Power BI offers a free desktop version to help individuals learn, but for small organizations, it has Power BI Pro for $9.99/user/month. And large companies can opt for Power BI Premium, starting from $20/user/month, for getting access to advanced features like data management and capacity. With Power BI, companies can also choose a pricing plan based on capacity instead of per user for $4,995/capacity/month.
2) Tableau
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Founded in 2003, Tableau Software has acquired many customers, including fortune 500 organizations, as it was a pioneer in supporting online and on-premise workflows. Due to its ease of use and better user interface, Tableau has been a direct competitor for Power BI. Although Power BI has an edge due to Microsoft’s resources, Tableau is better in terms of supporting massive datasets and custom visualizations without writing code.
Tableau also offers better customization with its server architecture to fit the different needs of enterprises. However, what makes Tableau stand out for learners and other professionals is that compelling dashboards can be shared among people who do not have Tableau installed.
Although Tableau is free to use, you can open new capabilities with its premium plans. Tableau has three models called creator, explorer, and viewer. Tableau Viewer only lets you interact with dashboards and visualizations for $12/user/month. In comparison, Tableau Explorer and Tableau Creator allow you to prepare and manage data for $35 and $70 per user per month, respectively.
3) Qlik Sense
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Qlik is a part of Data Analysis Tools that is helping organizations harness the power of data since the early ’90s with its end-to-end data analytics tools. Starting with Qlik View, the company has launched Qlik Sense for supporting the strenuous analytics needs of organizations.
With features like augmented analytics for suggesting insights automatically and conversational analytics for natural language understanding, QlikSense is another top choice within organizations.
Qlik does not offer you a free basic version of QlikSense, but you can get a free trial period to get started. But, it offers its free version of QlikView, a traditional analytics tool, for students and startups with a few limitations. However, businesses can get started with QlikSense Business for $30/user/month or Qlik Sense Enterprise SaaS for heavy requirements.
4) ThoughtSpot
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ThoughtSpot is revolutionizing how organizations perform data analytics with its search-based insights. Unlike traditional Data Analysis Tools, ThoughtSpot works on natural language understanding. To visualize your data, you only have to ask the right question using natural language. ThoughtSpot automatically showcases visualizations and other unique insights, which you can use to create reports or dashboards. Data Analytics has never been as easy as a search. While other Data Analysis Tools support insights delivery with search, they fail to provide results during complex questions.
With ThoughtSpot, you can never fail to analyze data with difficult questions, even on terabytes of data, making it more effective than most analytics tools in the market. The search-only feature can eliminate any risk of data leaks for organizations to ensure compliance with the privacy rules of governments across the world.
ThoughtSpot has gained traction due to its computation-based pricing as organizations only have to pay depending upon the usage. Such flexibility makes ThoughtSpot one of the best in the market when it comes to pricing. Although you can start for free, you will have to contact their sales team to get pricing details. It offers ThoughtSpot Enterprise and ThoughtSpot Everywhere, where the latter extends the former by offering API access and embedding support.
5) Oracle Analytics
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Oracle Analytics goes beyond traditional drag and drops Data Analysis Tools with AI-based insights generation based on the data you inject. It automatically suggests the right questions to ask by predicting your requirements. With Oracle Analytics, you can also forecast future outcomes with just a few clicks. The ability to go a step further with predictive analytics makes Oracle a must-have for organizations that want to augment insight delivery with superior Machine Learning techniques.
Oracle Analytics also pulls out data from trusted public sources to create new columns like the population count of different states, thereby adding additional information to your existing data for better analysis. To further simplify the delivery of visualizations, Oracle can generate reports in text from your visualizations with just one click. Such features allow you to not only generate insights quickly but also share reports immediately for expediting decision-making.
Oracle Analytics Cloud has several pricing models to provide more flexibility to its customers. Although you can start with a 30-day free trial, the premium plans come in two categories — user per month and OCPU per hour — for enterprise and professional variants. While the pricing for OCPU ranges from $0.3226 to $2.1506 per hour, the user per month plan ranges from $16 to $80.
6) Google Looker
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Acquired by Google in 2020 for $2.6 billion, Looker is one of the promising Data Analysis Tools that is gaining popularity due to its ease of use. Since companies are now leveraging the cloud for almost every operation, Looker, being a completely cloud-based service of Google Cloud computing, provides smooth integration with other cloud services.
In addition to the simple integrations with other applications, Google Cloud Platform offers better management of data flow for users leveraging Looker to control how data is being used while ensuring data privacy and security. Additional controls like data management by Looker place it uniquely in the market as companies now have to abide by several data privacy rules of different governments.
There is no transparent pricing model with Google Looker. Whether you are a decision-maker of a small or large business, you have to contact for bespoke pricing based on requirements.
7) SAP Analytics Cloud
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Since SAP has penetrated blue-chip companies for enterprise resource planning (ERP), SAP Analytics Cloud (SAC) has become one of the natural Data Analysis Tools for gaining insights into data. SAC allows predictive and augmented analytics for companies to gain insights with ease. SAC also works with SAP Digital Boardroom and SAP Analytics Hub for expanding the functionality to represent data in different but familiar use cases like presentation and sharing dashboards across the organization.
Due to its wider adoption among tech and non-tech companies, these types of Data Analysis Tools have a better understanding of different companies, which allows offering Analytics features for scenario planning and event modeling to drive the data culture across organizations.
SAP has three models — Free 90-day trial, Business Intelligence, and Planning — for their Analytics Cloud. While the Free 90-day trial comes with limited features, Business Intelligence is priced at $36/user/month. The Planning has custom prices based on companies’ requirements.
8) Google Analytics
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Google Analytics is one of the most effective Data Analysing Tools to analyze website traffic and user behavior. Unlike other Data Analysis Tools that require data cleaning before finding insights, Google Analytics can be used for streaming analytics without the need for data engineers to create data pipelines. A simple JavaScript code pulls the data from the website to analyze information specific to business requirements.
Today, media and entertainment, E-Commerce, and Fintech companies rely on website data to improve their products and services for business growth. Google Analytics offers specific features to find insights with only a few clicks. Since it does not require a user to learn any query language, Google Analytics has become a preferred tool in several organizations.
Google Analytics comes for free, but if you want to integrate and enhance the functionality, you need Analytics 360, which supports the complete marketing requirements of organizations. For Analytics 360, you will have to get quotes from their sales team.
9) Microsoft Excel
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Microsoft Excel is still widely used across the world, despite its reputation as a standard method of analysis. It’s a reasonably versatile Data Analysis application that allows you to make your analysis by simply manipulating rows and columns. Once this section is complete, you may export your data and email it to the appropriate recipients, allowing you to utilize Excel as a reporting tool. Excel has evolved from an electronic version of the accounting worksheet to one of the most widely used Data Analysis Tools, allowing you to create pivot tables, manage a low volume of data, and experiment with the tabular form of analysis. Excel has firmly established itself in the realm of traditional data management.
10) Google Data Studio
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Google Data Studio is one of the most prominent and free Data Analysis Tools for dashboarding and data visualization. It works with almost all other Google services, including Google Analytics, Google Ads, and Google BigQuery. Because of its integration with other Google services, Data Studio is ideal for people who need to review their Google data. Marketers may, for example, construct dashboards to aid in the analysis of customer conversion and retention for their Google Ads and Analytics results.
11) IBM Cognos
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IBM Cognos is one of the robust Business Intelligence and Data Analysis Tools with built-in AI features to uncover and explain information hidden in plain English. It contains automated Data preparation software that cleans and aggregates data sources automatically, allowing for quick data integration and analysis.
12) TIBCO Spotfire
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TIBCO Spotfire is a Data Analytics service that enables AI-powered data insights and natural language search. Its feature-rich visualization tool can relay information to both mobile and desktop applications. Spotfire also has tools for creating predictive analytics models that are quite easy to use.
13) Zoho Analytics
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Zoho Analytics enables you to do Data Analysis in a time- and cost-effective manner. It helps you to analyze your data no matter where it is stored and provides a diverse set of data visualization capabilities. You can utilize its Artificial Intelligence-powered assistant to ask queries and receive intelligent responses in the form of relevant reports. It provides mobile apps for iOS and Android that allows for interactive Data Analysis of dynamic data.
14) Whatagraph
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Whatagraph combines visual Data Analysis with automated data source entry and drag-and-drop report creation. Whatagraph’s main advantage is its simple setup and user-friendly visual interface. Every data point is represented with a widget that can be customized. This enables a clear picture of the development of sponsored and organic advertising campaigns. Whatagraph provides 30+ connectors so that you can easily integrate and analyze your data.
15) Query.me
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Query.me lets you analyze and interpret your data using easy tools that don’t require any programming knowledge beyond SQL. You’ll be able to dive deeper into data using sophisticated SQL notebooks to gain insights into your business. With SQL becoming more and more relevant every day, Query.me strives to set itself apart from the competition by focusing on SQL and making your workflow more effective, powerful, and overall more efficient.
16) Sisense
Sisense is an Artificial Intelligence (AI) driven Business Intelligence software that was founded in 2004 as an Artificial Intelligence (AI) powered Business Intelligence platform. It helps organizations simplify and analyze complex data, generate visualizations, and discover and share insights with essential decision-makers. With its simple drag-and-drop interface and interactive dashboard, you can quickly evaluate large datasets.
It also has a variety of data source connectors and can integrate and analyze data from various sources. This capacity facilitates the generation of insights and the verification of predictions on critical business concerns.
Sisense is a hybrid solution that may be deployed both in the cloud and on-premise. Additional features such as embedded analytics and natural language narratives enhance the default amount, which is based on a pay-as-you-go premise.
Sisense’s price plan is straightforward and adaptable, with a procedure that is “No-Surprise.” Companies interested in using Sisense must first meet with Sisense’s employees. Sisense’s pricing plan is depicted below.
Metabase is a completely free open-source analytics and business intelligence platform. Metabase, as a business intelligence platform, is critical for sharing data and making it known among peers. The code is freely available so that it can be modified and used in different ways to help people understand the data better. Metabase allows non-technical people to construct searches using a point-and-click interface, allowing them to “ask questions” about data. Dashboards make it easier to communicate data and draw conclusions from it. This works well for simple filtering and aggregations; for more complex analysis, more technical users can go straight to raw SQL. Metabase can also send analytics data to third-party platforms such as Slack.
18) Apache Spark
The purpose of Apache Spark was to establish a new framework that was geared for quick iterative processing such as Machine Learning and interactive Data Analysis while keeping the scalability and fault tolerance of Hadoop MapReduce. Apache Spark is a multi-language, open-source data processing engine that allows you to construct distributed stream and batch processing operations for large-scale data workloads. It was first released in 2014. To put it another way, Apache Spark is a Distributed General-Purpose Computing Engine that can be used to analyze and process large data files from a variety of sources, including S3, Azure, HDFS, and others.
It combines in-memory caching and improved query execution for rapid analytic queries against any size of data. It provides development APIs in Java, Scala, Python, and R and facilitates code reuse across different workloads, including Batch Processing, Interactive Queries, Real-Time Analytics, Machine Learning, and Graph Processing.
19) Python
Python was created as an Object-Oriented Programming language for software and web development, but it has since been expanded for data research. Python is currently the most popular programming language. It is a sophisticated Data Analysis tool with a large collection of user-friendly libraries for any element of scientific computing. Python is a free, open-source programming language that is simple to learn. Pandas, Python’s data analysis library, was created on top of NumPy, one of Python’s first data science libraries. Pandas support a variety of file formats; for example, data from Excel spreadsheets can be imported into processing sets for time-series analysis. Pandas is an excellent tool for data visualization, data masking, merging, indexing and grouping data, data cleaning, and many other tasks.
For statistical modeling, mathematical methods, machine learning, and data mining, other libraries such as Scipy, Scikit-learn, and StatsModels are utilized. Packages for data visualization and graphical analysis include Matplotlib, seaborn, and vispy. Python has a large developer community and is the most extensively used programming language.
20) R
R is the most popular statistical modeling, visualization, and data analysis programming language. Statisticians mostly utilize it for statistical analysis, Big Data, and machine learning. R is a free, open-source programming language with several extensions in the form of user-written packages.
R has a steep learning curve and requires some programming experience. It is, however, a fantastic language in terms of syntax and consistency. When it comes to EDA (Exploratory data analysis (EDA) is a strategy for evaluating data sets to summarise their essential properties, generally using visual approaches), R comes out on top.
Packages like plyr, dplyr, and tidy make data processing in R simple. With tools like ggplot, lattice, ggvis, and others, R excels at data visualization and analysis. For help, R has a large developer community. Facebook uses R to analyze user behavior in relation to status updates and profile images.
21) Splunk
Splunk is a piece of software that analyses machine data and other types of big data to provide useful and actionable insights. Machine data includes information created by a web server’s CPU, IoT devices, and logs from mobile apps, among other things. This data may or may not have any economic value or be relevant to the end-user, but it is critical to understand, monitor, and improve machine performance for maximum efficiency.
Splunk can read a wide variety of data types, including unstructured, semi-structured, and rarely structured data. Splunk allows you to search, categorize, and create reports and dashboards on the data after it has been read. Splunk is now able to ingest big data from many sources, which may or may not be machine data, and execute analytics on big data, thanks to the availability of massive sources of Big Data.
Splunk has evolved from a simple log analysis tool to a generic analytical tool for unstructured machine data and many forms of big data. Splunk’s analytical productivity is improved much more when Salesforce integration is used. Product Categories offered by Splunk are Splunk Enterprise, Splunk Cloud, and, Splunk Light.
22) SAS Forecasting
SAS Forecasting for Desktop has established itself as one of the most popular sophisticated data analysis programs, with a variety of forecasting methodologies such as hierarchical reconciliation, event modeling, what-if analysis, and scenario planning. Automatic forecasting, scalability and modeling, infinite model repository, easy-to-use GUI, event-modeling console, what-if analysis, and data preparation are among the 7 major aspects of forecasting procedures that they offer. SAS will automatically select variables based on the variables you enter in the modeling process to generate forecasts to help you figure out what’s going on in your organization.
Furthermore, this data package enables customers to make a huge number of forecasts and automate their procedures by combining the SAS Forecast Server and Visual Forecasting solutions.
23) Highcharts
Highcharts is a web-based interactive JavaScript charting API solution. It supports a wide range of chart types, such as bubble, box plot, error bars, funnel, waterfall, line, spline, area, area spline, column, bar, pie, scatter, and angle gauges, to name a few.
The Highcharts library includes all of the tools you’ll need to create secure and dependable data visualizations. All of its charting libraries are written in JavaScript and TypeScript and can be used with any back-end database or server stack. Wrappers are available for the most common programming languages (.Net, PHP, Python, R, Java) as well as iOS and Android, as well as frameworks such as Angular, Vue, and React.
Highcharts offers a variety of chart types, including line, spline, area, column, bar, pie, scatter, and many others, to aid developers in their web-based projects. Additionally, their boost module, which is powered by WebGL, allows you to render millions of data points in the browser. They allow you to get the source code and make your own adjustments, regardless of whether you choose their free or commercial license.
24) Jupyter Notebook
Jupyter Notebook was created by Fernando Pérez and Brian Granger in 2014 as part of the IPython project. It’s an open-source, web-based computational tool for writing code, creating visualizations, rendering equations, and writing narrative texts. You can create end-to-end Machine Learning models and perform Data Cleaning, Transformation, and Visualization with the Jupyter Notebook. Numerical simulation, statistical modeling, complicated scientific compilations, and much more are all possible with the Jupyter Notebook. Jupyter Notebook supports around 40 programming languages, including Python, R, Julia, Java, and JavaScript, because it is a multi-language computation tool.
25) RapidMiner
RapidMiner is a data science platform for businesses that examines the influence of an organization’s workers, knowledge, and data as a whole. RapidMiner’s data science platform is designed to serve a wide range of analytics users throughout the AI lifecycle. RapidMiner says that its platform is used by over a million people.
RapidMiner is based on a client/server architecture, with the server available on-premises as well as on public and private cloud infrastructures.
RapidMiner, according to Bloor Research, provides 99 percent of an advanced analytical solution through template-based frameworks that decrease errors and speed delivery by nearly removing the need to write code. Data loading and transformation (ETL), data preparation and visualization, predictive analytics and statistical modeling, evaluation, and deployment are all included in RapidMiner’s data mining and machine learning operations. RapidMiner has a graphical user interface for creating and executing analytical workflows. In RapidMiner, these workflows are referred to as “Processes,” and they are made up of numerous “Operators.” Within the process, each operator completes a single task, and the output of each operator becomes the input of the next.
26) Hyperquery
If you’re ready to make an investment into analytics collaboration, hyperquery is your best bet. Hyperquery is a document-based analytics workspace, so you can execute queries, write queries, make visualizations, and talk about your findings in one place. Unlike traditional notebooks, they focus heavily on being extremely user-friendly, so both you and your stakeholders will love using it. If you’ve ever had trouble collaborating around, sharing, or organizing all your analytics work, hyperquery can bring order to the chaos.
Hope this list of the best Data Analysis Tools in the market makes your decision to choose the right Data Analysis Tool easy.
Conclusion
This article gave a comprehensive guide on the most popular Data Analysis Tools. It also gave a brief overview of Data Analysis and the benefits of these Data Analysis Tools. You also explored some key factors to consider while the best Data Analysis Tools for your use case. Overall, Since professionals need little to no coding for visualization with Data Analysis Tools, democratizing data across organizations to enhance self-service Analytics becomes easier.
With a wide range of options available in the market, organizations can quickly determine the best Data Analysis Tools based on their needs to start gaining insights for business growth. The free trial plan of different analytics tool providers further simplifies decision-making for organizations as they can explore for free before opting for a premium subscription.
In case you want to integrate data from data sources into your desired Database/destination and seamlessly visualize it in a BI tool of your choice, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and destinations.
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Ratan Kumar is proficient in freelance writing within the data industry, skillfully creating informative and engaging content pertaining to data science by harmonizing his problem-solving abilities.