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DATA Analysis Tools/Software ​

Data Analysis Tools and Softwares

  Tableau   Tableau Software helps people see and understand data. Tableau delivers fast analytics, visualization, and business intelligence.
  Google Cloud Platform   Cloud Platform is a modular cloud-based service that allows you to create anything from simple websites to complex applications.
  Microsoft Power BI   A business intelligence and analytics suite with reporting, data prep, and data visualization tools.
  Qlik Sense   Qlik Sense is a business intelligence (BI) and data analysis platform that helps global enterprises move faster and work smarter.
  SISense   SISense is a full-scale data analysis solution that lets business users visualize, explore & drive insights from complex data
2 STATISTICAL ANALYSIS TOOLS R PROGRAMMING / R-STUDIO R is a free software environment for statistical analysis and graphics. It runs on different platforms: UNIX platforms, Windows and MacOS.
    1. Graphpad Graphpad is an amazing statistical software that can guide you in statistical tests and graphics analysis.
    2. SPSS ​ IBM SPSS software   IBM SPSS software
    3. XLSTAT ​ ​ ​XLSTAT is the leading data analysis and statistical solution for Microsoft Excel. 3. XLSTAT ​ ​ ​ XLSTAT is the leading data analysis and statistical solution for Microsoft Excel.
      Minitab Powerful statistical software everyone can use to analyze data and find meaningful solutions to their toughest business problems
      Zoho Analytics An online data analytics platform that helps you get new insights from your diverse business data. ​ ​ ​ ​ ​


Data analysis tools is a term used to describe software and applications that data analysts use to develop and perform analytical processes that help companies to make better, informed business decisions while decreasing costs and increasing profits.
To make the best possible decision on which software to choose as an analyst, we have compiled a list of the top data analysis tools with various focus and features, organized into software categories, and represented with an example of each. Let’s get started.


Data analyst software/Tools
To make the most out of the infinite number of software currently offered on the market, we will focus on the most prominent tools needed to be an expert data analyst, starting with business intelligence.


BI tools are one of the most represented means of performing data analysis. Specializing in business analytics, these data analysis tools will benefit every data analyst who needs to analyze, monitor, and report on important findings. Features such as self-service, predictive analytics, and advanced SQL modes make these solutions easily adjustable to every level of knowledge without heavy IT involvement. Our data analytsis tools article wouldn’t be complete without business intelligence, and Datapine is one example covering most of the requirements for beginner and advanced users.
  • Visual drag-and-drop interface with an easy switch to advanced SQL mode
  • Powerful predictive analytics features and interactive charts and dashboards
  • Intelligent alarms that are triggered as soon as an anomaly occurs
datapine is a popular business intelligence software that delivers simple yet powerful analysis features to beginners and advanced users who need a fast and reliable online data analysis solution. An effective user interface will enable you to drag and drop your desired values into Datapine’s Analyzer and create numerous charts and graphs. If you’re an experienced analyst, you might want to consider the SQL mode, where you can build your queries and easily switch back to the visual mode. Another crucial feature is the predictive analytics forecast engine. While there are numerous predictive tools, datapine provides simplicity and speed. A complete chart will unfold together with predictions by simply defining the input and output of the forecast based on specified data points and desired model quality.
We should also mention robust artificial intelligence that is becoming an invaluable assistant in today’s analysis processes. Neural networks, pattern recognition, and threshold alerts will alarm you when a business anomaly occurs, so you don’t have to manually analyze large volumes of data – the data analytics software does it for you. Easily share your findings via dashboards or customized reports for anyone that needs quick answers to any business question.


Referring to computation techniques that often contain a variety of statistical methods to manipulate, explore, and generate insights, multiple programming languages exist to make (data) scientists’ work easier and more effective. With the expansion of various languages on the market today, science has rules and scenarios that need special attention regarding statistical data analysis and modeling. Here we will present one of the most popular tools for a data analyst – R programming. Although other languages focus on (scientific) data analysis, R is particularly popular in the community.
  • An ecosystem of more than 10 000 packages and extensions for various types of data analysis
  • Statistical analysis, modeling, and hypothesis testing (e.g., analysis of variance, t-test, etc.)
  • Active and communicative community of researchers, statisticians, and scientists
R is a top data analysis tool usually referred to as a language designed by statisticians. Its development dates back to 1995, and it’s one of the most used tools for statistical analysis and data science, keeping an open-source policy and running on various platforms, including Windows and macOS. R Studio is by far the most popular integrated development environment. R’s capabilities for data cleaning, reduction, and analysis report output with R markdown make this tool an invaluable analytical assistant covering general and academic data analysis. It is compiled of an ecosystem of more than 10 000 packages and extensions that you can explore by categories and perform any statistical analysis such as regression, conjoint, factor cluster analysis, etc. Easy to understand for those that don’t have a high level of programming skills, R can perform complex mathematical operations by using a single command. Several graphical libraries, such as ggplot and plotly, make this language different than others in the statistical community since it has efficient capabilities to create quality visualizations.
R was mostly used in the academic area in the past; today, it has applications across industries and large companies such as Google, Facebook, Twitter, and Airbnb, among others. Due to an enormous number of researchers, scientists, and statisticians using it, R has an extensive and active community where new technologies and ideas are presented and communicated regularly.


Programming languages are used to solve a variety of data problems. We have explained R and statistical programming; now, we will focus on general ones that use letters, numbers, and symbols to create programs and require formal syntax used by programmers. They’re often called text-based programs because you must write software that will solve a problem. Examples include C#, Java, PHP, Ruby, Julia, and Python, among others on the market. Here we will present Python as one of the best tools for data analysts with coding knowledge.
  • An open-source solution that has simple coding processes and syntax, so it’s fairly easy to learn
  • Integration with other languages such as C/C++, Java, PHP, C#, etc.
  • The advanced analysis processes through machine learning and text mining.
Python is extremely accessible to code compared to other popular languages such as Java, and its syntax is relatively easy to learn, making this tool popular among users looking for an open-source solution and simple coding processes. In data analysis, Python is used for data crawling, cleaning, modeling, and constructing analysis algorithms based on business scenarios. One of the best features is its user-friendliness: programmers don’t need to remember the system's architecture nor handle the memory – Python is considered a high-level language not subject to the computer’s local processor.
Another noticeable feature of Python is its portability. Users can run the code on several operating systems without making any changes, so writing completely new code is unnecessary. This makes Python a highly portable language since programmers can run it on Windows and macOS. An extensive number of modules, packages, and libraries make Python a respected and usable language across industries, with companies such as Spotify, Netflix, Dropbox, and Reddit as the most popular ones that use this language in their operations. With text mining and machine learning features, Python is becoming a respected authority for advanced analysis processes.


SQL is the programming language used to manage/query data held in relational databases, particularly effective in handling structured data as a database tool for analysts. It’s trendy in the data science community and one of the analyst tools used in various business cases and data scenarios. The reason is simple: as most of the data is stored in relational databases and you need to access and unlock its value, SQL is a critical component of succeeding in business, and by learning it, analysts can offer a competitive advantage to their skillset. There are different relational (SQL-based) database management systems such as MySQL, PostgreSQL, MS SQL, and Oracle, for example, and learning these data analysts’ tools would prove extremely beneficial to any serious analyst. Here we will focus on MySQL Workbench as the most popular one.
  • A unified visual tool for data modeling, SQL development, administration, backup, etc.
  • Instant access to database schema and objects via the Object Browser
  • SQL Editor that offers color syntax highlighting, reuse of SQL snippets, and execution history
​Analysts use MySQL Workbench to visually design, model, and manage databases, optimize SQL queries, administer MySQL environments, and utilize a suite of tools to improve the performance of MySQL applications. It will allow you to create and view databases and objects (triggers or stored procedures, e.g.), configure servers, and more. You can easily perform backup and recovery as well as inspect audit data. MySQL Workbench will also help in database migration. It is a complete solution for analysts working in relational database management and companies that need to keep their databases clean and effective.


Predictive analytics is one of the advanced techniques used by analysts that combine data mining, machine learning, predictive modeling, and artificial intelligence to predict future events. It deserves a special place in our list of data analysis tools. Its popularity has increased in recent years with the introduction of smart solutions that enabled analysts to simplify their predictive analytics processes. You should keep in mind that some BI tools we already discussed in this list offer easy-to-use, built-in predictive analytics solutions but in this section, we focus on standalone, advanced predictive analytics that companies use for various reasons, from detecting fraud with the help of pattern detection to optimizing marketing campaigns by analyzing consumers’ behavior and purchases. Here we will list a data analysis software that is helpful for predictive analytics processes and helps analysts to predict future scenarios.
  • Automatic forecasting for a large number of entities or products, including hierarchical forecasting
  • Scalability and modeling by combining 2 or more models and creating an ensemble
  • An unlimited model repository that includes time series and casual methods such as ARIMA and ARIMAX
SAS Forecasting for Desktop has established itself as one of the most prominent advanced data analysis software that offers a wide range of forecasting methods, including hierarchical reconciliation, event modeling, what-if analysis, and scenario planning. Their features comprise seven core areas of forecasting processes, some of which we already mentioned: automatic forecasting, scalability and modeling, unlimited model repository, easy-to-use GUI, event-modeling console, what-if analysis, and data preparation. Based on the variables you enter in the modeling process, SAS will automatically select variables to generate forecasts to unravel what happens in your business.
Moreover, with a bundle of the SAS Forecast Server and Visual Forecasting solutions, this data software enables users to produce many forecasts and automate their processes. Since the company has been on the market for decades, they have established itself as an authority figure in predictive analytics, and it certainly makes sense to try them.


Our list of software for analysts wouldn’t be complete without data modeling. Creating models to structure the database and design business systems by utilizing diagrams, symbols, and text ultimately represents how the data flows and is connected in between. Businesses use data modeling tools to determine the exact nature of the information they control and the relationship between datasets, and analysts are critical in this process. If you need to discover, analyze, and specify changes in data stored in a software system, database, or other application, chances are your skills are critical for the overall business. Here we will show one of the most popular data analyst software used to create models and design your data assets.
  • Automated data model generation to increase productivity in analytical processes
  • Single interface, no matter the location or the type of the data
  • Seven different versions of the solution you can choose from and adjust based on your business needs
Erwin DM works with structured and unstructured data in a data warehouse and the cloud. It’s used to “find, visualize, design, deploy and standardize high-quality enterprise data assets,” as their official website states. Erwin can help you reduce complexities and understand data sources to meet your business goals and needs. They also offer automated processes where you can automatically generate models and designs to minimize errors and increase productivity. This is one of the tools for analysts that focus on the architecture of the data and enable you to create logical, conceptual, and physical data models.
Additional features, such as a single interface for any data you possess, whether structured or unstructured, in a data warehouse or the cloud, make this solution highly adjustable for your analytical needs. With seven versions of the Erwin data modeler, their answer is highly flexible for companies and analysts that need various data modeling features.


ETL is a process used by companies, no matter the size, across the world, and if a business grows, chances are you will need to extract, load, and transform data into another database to be able to analyze it and build queries. There are some core types of ETL tools, such as batch ETL, real-time ETL, and cloud-based ETL, each with its specifications and features that adjust to different business needs. These are the tools used by analysts that take part in more technical processes of data management within a company, and one of the best examples is Talend.
  • Collecting and transforming data through data preparation, integration, cloud pipeline designer
  • Data governance feature to build a data hub and resolve any issues in data quality
  • Sharing data through comprehensive deliveries via APIs
Talend is a data integration platform used by experts across the globe for data management processes, cloud storage, enterprise application integration, and data quality. It’s a Java-based ETL tool analysts use to process millions of data records easily and offers comprehensive solutions for any data project you might have. Talend’s features include (big) data integration, data preparation, cloud pipeline design, and stitch data loader to cover multiple data management requirements of an organization. This analyst software is essential if you need to work on ETL processes in your analytical department.
Apart from collecting and transforming data, Talend offers a data governance solution to build a data hub and deliver it through self-service access through a unified cloud platform. Using their data quality feature, you can utilize their data catalog and inventory and produce clean data. Sharing is also part of their data portfolio; Talend’s data fabric solution will enable you to deliver your information to every stakeholder through a comprehensive API delivery platform. If you need a data analyst tool to cover ETL processes, Talend might be worth considering.


If you work for a company that produces massive datasets and needs big data management solutions, then unified data analytics engines might be the best resolution for your analytical processes. To make quality decisions in a big data environment, analysts need tools that will enable them to take full control of their company’s robust data environment. That’s where machine learning and AI play a significant role. That said, Apache Spark is one of the tools on our list that supports big-scale data processing with the help of an extensive ecosystem.
  • High performance: Spark owns the record in the large-scale data processing
  • A large ecosystem of data frames, streaming, machine learning, and graph computation
  • A collection of over 100 operators for transforming and operating on large-scale data
Apache Spark was originally developed by UC Berkeley in 2009. Since then, it has expanded across industries and companies such as Netflix, Yahoo, and eBay that have deployed Spark, processed petabytes of data, and proved that Apache is the go-to solution for big data management. Their ecosystem includes Spark SQL, streaming, machine learning, graph computation, and core Java, Scala, and Python APIs to ease the development. Already in 2014, Spark officially set a record in large-scale sorting. The engine can be 100x faster than Hadoop, an extremely crucial feature for massive volumes of data processing.
You can easily run Java, Python, Scala, R, and SQL applications, while more than 80 high-level operators that Spark offers will make your data transformation easy and effective. As a unified engine, Spark supports SQL queries, MLlib for machine learning, and GraphX for streaming data that can be combined to create additional, complex analytical workflows. Additionally, it runs on Hadoop, Kubernetes, and Apache Mesos, standalone or in the cloud, and can access diverse data sources. Spark is a powerful engine for analysts needing support in their big data environment.


Spreadsheets are one of the most traditional forms of data analysis. Quite popular in any industry, business, or organization, there is a slim chance that you haven’t created at least one spreadsheet to analyze your data. Often used by people that don’t have high technical abilities to code themselves, spreadsheets can be used for fairly easy analysis that doesn’t require considerable training, complex and large volumes of data, and databases to manage. To look at spreadsheets in more detail, we have chosen Excel as one of the most popular in business.
  • Part of the Microsoft Office family, hence, it’s compatible with other Microsoft applications
  • Pivot tables and build complex equations through designated rows and columns
  • Perfect for smaller analysis processes through workbooks and quick sharing
Excel needs a category on its own since this powerful data analysis tool has been in the hands of analysts for a very long time. Often considered a traditional form of analysis, Excel is still widely used across the globe. The reasons are fairly simple: few people have never used or encountered it at least once in their careers. It’s a fairly versatile data analyst tool where you manipulate rows and columns to create your analysis. Once this part is finished, you can export and send your data to the desired recipients. Hence, you can use Excel as a reporting tool as well. You need to update the data independently; Excel doesn’t have an automation feature similar to other devices on our list. Creating pivot tables, managing smaller amounts of data, and tinkering with the tabular form of analysis, Excel has developed as an electronic version of the accounting worksheet to one of the most spread tools for data analysts.
A wide range of functionalities accompany Excel, from arranging to manipulating, calculating, and evaluating quantitative data to building complex equations and using pivot tables, conditional formatting, adding multiple rows, and creating charts and graphs – Excel has earned its place in traditional data management.


While many tools on this list are used in various industries and are applied daily in analysts’ workflow, some solutions are specifically developed to accommodate a single sector and cannot be used in another. Therefore, we have decided to include one of these solutions on our list, although many other industry-specific data analysis programs and software exist. Here we focus on Qualtrics as one of the leading research software used by over 11000 world brands and has over 2M users across the globe, and many industry-specific features focused on market research.
  • Four main experience features: customer, brand, employee, and product
  • Additional research services by their in-house experts
  • Advanced statistical analysis with their Stats iQ analysis tool
Qualtrics is a data analysis software focused on experience management and used for market research by companies across the globe. They offer 4 product pillars: the customer experience, brand, employee, and product experience, and additional research services performed by their experts. Their XM platform consists of a directory, automated actions, Qualtrics iQ tool, and platform security features that combine automated and integrated workflows into a single access point. Users can refine each stakeholder’s experience and use their tool as an “ultimate listening system.”
Since automation is becoming increasingly important in our data-driven age, Qualtrics has also developed drag-and-drop integrations into the systems companies already use, such as CRM, ticketing, or messaging, while enabling users to deliver automatic notifications to the right people. This feature works across brand tracking, product feedback, and customer and employee experience. Other critical components, such as the directory where users can connect data from 130 channels (including web, SMS, voice, video, or social) and Qualtrics iQ to analyze unstructured data, will enable users to utilize their predictive analytics engine and build detailed customer journeys. If you’re looking for data analytic software that needs to take care of your company's market research, Qualtrics is worth the try.


Data science can be used for most software solutions on our list, but it does deserve a special category since it has developed into one of the most sought-after skills of the decade. Whether you need to utilize preparation, integration, or data analyst reporting tools, data science platforms will probably be high on your list for simplifying analytical processes and using advanced analytics models to generate in-depth data science insights. To put this into perspective, we will present RapidMiner as one of the top data analyst software that combines deep but simplified analysis.
  • A comprehensive data science and machine learning platform with more than 1500 algorithms
  • Possible to integrate with Python and R as well as support for database connections (e.g. Oracle)
  • Advanced analytics features for descriptive and prescriptive analytics
RapidMiner is a tool used by data scientists worldwide to prepare data, utilize machine learning, and model operations in more than 40 000 organizations that heavily rely on analytics in their operations. By unifying the entire data science cycle, Rapid Miner is built on five core platforms and three automated data science products that help design and deploy analytics processes. Their data exploration features, such as visualizations and descriptive statistics, will enable you to get the necessary information. At the same time, predictive analytics will help you in churn prevention, risk modeling, text mining, and customer segmentation.
With more than 1500 algorithms and data functions, support for 3rd party machine learning libraries, integration with Python or R, and advanced analytics, RapidMiner has developed into a data science platform for deep analytical purposes. Additionally, where needed, comprehensive tutorials and full automation will ensure simplified processes if your company requires them, so you don’t need to perform manual analysis. If you’re looking for analyst tools and software focused on deep data science management and machine learning, then RapidMiner should be high on your list.


Data visualization has become an indispensable tool in analysis processes. If you’re an analyst, there is probably a strong chance you had to develop a visual representation of your analysis or utilize some form of data visualization. Here we need to clarify that there are differences between professional data visualization tools often integrated through already mentioned BI tools, free available solutions, and paid charting libraries. They’re not the same. Also, if you look at data visualization broadly, Excel and PowerPoint have it on offer. Still, they cannot meet the advanced requirements of a data analyst who usually chooses professional BI or data viz tools and modern charting libraries, as mentioned. We will look closer at High Charts as one of the most popular charting libraries on the market.
  • Interactive JavaScript engine for charts used in web and mobile projects
  • Designed mostly for a technical-based audience (developers)
  • WebGL-powered boost module to render millions of data points directly in the browser
Highcharts is a multi-platform library designed for developers looking to add interactive charts to web and mobile projects. This charting library works with any back-end database; data can be given in CSV, JSON, or updated live. They also feature intelligent responsiveness that fits the desired chart into the dimensions of the specific container but also places non-graph elements in the optimal location automatically.
High charts support line, spline, area, column, bar, pie, scatter charts, and many others that help developers in their online-based projects. Their WebGL-powered boost module also enables you to render millions of data points in the browser. Regarding the source code, they allow you to download and make your edits, no matter if you use their free or commercial license. Highcharts is designed mostly for the technical target group, so familiarize yourself with developers’ workflow and their JavaScript charting engine. If you’re looking for a more easy-to-use but still powerful solution, you might want to consider an online data visualization tool like Datapine.
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