Published on

January 5, 2024

Data Science

Complete Guide To The Top 20 Data Science Tools To Use In 2022

In this guide, we will take a look at the top 20 data science tools that you can pick from in 2022.
Craig Wisneski
Co-Founder & Head of G&A, Akkio
Data Science

Data science is one of the most important fields today. With the help of data science, businesses can make better decisions, improve customer experience, and personalize their products. As the demand for data science grows, so does the need for data scientists.

There are many tools that data scientists use to do their job, from code-based libraries to visualizations and dashboards. In this guide, we will take a look at the top 20 data science tools that you can pick from in 2022.

How does data science work? What are its applications to a business?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. 

Traditionally, statisticians and data analysts have been the main users of data science techniques. However, with the proliferation of big data, there is a growing need for people with data science skills across all industries. In fact, 68% of small businesses are using analytics in operations, and 91.5% of leading businesses invest in AI on an ongoing basis.

For instance, in the fast-growing healthcare sector, data science is used to develop new treatments and track the efficacy of existing ones. It can also be used to analyze clinical trials data and understand patient behavior. Even insurance companies are using data science to detect fraud and improve customer service.

In the banking and finance industry, data science is used for a variety of purposes such as credit scoring, algorithmic trading, and fraud detection. Banks are also using data science to personalize customer service and target new customers. 

Data science is also being used in the construction industry to streamline project management, optimize resources and improve safety. And content audits are increasingly being conducted using data science in order to assess and improve the effectiveness of an organization's content strategy.

As you can see, data science is a powerful tool that can be used in a variety of industries to improve decision-making, drive growth and create value. So if you're looking to get ahead in your career, here are the top 20 data science tools you can use in 2022.

Top 20 data science tools


Akkio is a no-code data science tool that makes it easy to get started with AI. With Akkio, you can connect to any data source, select the columns you want to use, and deploy your models anywhere.

Akkio's no-code approach makes it a great tool for businesses that want to get started with AI but don't have the resources or expertise to build their own AI team. It's also an extremely robust and scalable cloud platform, so it can handle even the most demanding data science tasks and usage scenarios.

For instance, finance teams could connect a BigQuery data source with Akkio to create an automated machine learning model that predicts fraud in real-time, on any number of transactions. Retailers could use Akkio to automatically segment their customer base and identify which customers are most likely to respond to a new product launch. 

In short, Akkio is a powerful and versatile data science tool that can be used by both beginners and businesses of any size and in any industry. If you're looking for an easy way to get started with AI, Akkio is the solution for you.


Python is not only one of the most popular programming languages but also one of the best tools for data science. There are countless high-level libraries and modules available that make data wrangling, exploration, and modeling easier and more efficient. 

For instance, Python Pandas is a go-to library for many data scientists as it offers powerful data structures and analysis tools. Python’s Scikit-learn library is another favorite, as it provides a wide range of machine learning algorithms that can be applied to any dataset.

More robust libraries such as TensorFlow and Pytorch allow for deep learning applications such as computer vision and natural language processing. These libraries have been developed and maintained by some of the biggest tech companies in the world, such as Google and Facebook.

The Python programming language is not only versatile and easy to use, but also has a large and supportive community. This is evident in the many online resources that have a heavy Python component, such as Stack Overflow, which is a go-to site for many programmers. As an open-source tool, there are many Python tutorials for implementing artificial intelligence solutions, creating APIs, integrating with SQL databases, and more.

The Python community has also developed many helpful IDEs (integrated development environments), such as Jupyter Notebook and Spyder, which make data science workflows easier.

While there are many advantages of Python, there are also some drawbacks. For example, Python is not as fast as compiled languages such as C++. When it comes to deploying scalable machine learning models, Python may not be the best option.

Further, as with any programming language, relying on Python, as opposed to a no-code solution, means that your non-technical business colleagues will not be able to understand or contribute to your work. This also limits the usability and potential application of your work, as it can only be used by those with the required technical skills. Maintenance, too, can be an issue as the codebase grows and becomes more complex.

Despite these drawbacks, Python remains a very popular tool for data science, and for good reason. The many libraries available make it possible to do everything from data wrangling to deep learning, and the supportive community means that help is always close at hand. Python is a versatile tool that can be used in a wide range of data science applications, as long as the limitations are kept in mind.


Excel is the go-to spreadsheet program for many businesses, for everything from P&L calculations to inventory management. But its potential doesn't stop there - Excel can also be a handy tool for data science.

Sure, Excel isn't designed specifically for data science tasks, and it doesn't have all the bells and whistles of dedicated data science software. But with a little creativity, you can use Excel to perform many common data science tasks, including data wrangling, visualization, and even some basic machine learning.

For example, the function CONCATENATE() can be used to combine data from multiple cells into a single cell, making it easier to work with. The VLOOKUP() function can be used to quickly look up values in large data sets. And the FILTER() function can be used to subset data for analysis.

Excel also has some built-in tools for data visualization, such as the ability to create scatter plots and histograms. And with the addition of some simple macros, you can even create interactive visualizations that let you explore data sets without having to write any code.

Finally, Excel can also be used for basic machine learning tasks. For example, the program can be used to make simple predictions based on linear regression. Of course, these won't be as robust as predictions made with more powerful machine learning algorithms. But for quick-and-dirty predictions, Excel can be a helpful tool.


DataRobot is a tool that allows data scientists to automatically build and deploy machine learning models. It is a platform that provides an end-to-end workflow for data science, from data preparation to model deployment.

The AWS Marketplace shows that DataRobot’s MLDev & MLOps units cost $65,000 a year, which includes 5 models deployed. The page notes "no refunds accepted," so be sure to commit to using DataRobot if you decide to sign up.

Given its pricing and target audience, DataRobot is an enterprise-level data science tool. It is not a tool for small businesses or even most medium-sized businesses, as the annual costs are similar to hiring a data scientist.

DataRobot has many features that appeal to data scientists. It can automatically handle data preparation, including imputation of missing values and scaling of numeric variables. It also supports a wide range of machine learning algorithms, including deep learning.

DataRobot also offers model deployment options. It can deploy models to on-premises servers or to the cloud. It also supports integration with a variety of applications, such as Salesforce and Tableau.


RapidMiner is a visual data mining and machine learning tool that has been gaining popularity in the data science community. The learning curve for RapidMiner is shorter than for programming languages like R and Python, but steeper than for some point-and-click tools.

While the RapidMiner website doesn't show pricing, a TrustRadius search shows that RapidMiner Studio starts at $7,500 per user per month for a professional license, up to a whopping $54,000 per user per month for the AI Hub edition.

Clearly, RapidMiner Studio is not a tool for SMEs or startups on a budget, as these prices are significantly higher than the alternatives. After all, if you're paying $54,000 per user per month, you could also create a data science team with full-time salaries for each team member.

TrustRadius also shows that there's no free trial, premium consulting, or integration services available, which may be a dealbreaker for some organizations.


Tableau is a data visualization tool that makes it easy to create interactive, visually appealing charts and graphs. It's commonly used for tasks like exploring data sets, finding trends and patterns, and communicating findings to others.

For instance, a service team might use Tableau to quickly discover and fix a performance issue. A sales manager could use it to monitor quota attainment, or a marketing team might use it to track leads funneled through various channels.

The pretty pictures that Tableau produces make it a useful supplement for business presentations and reports, but it can also do much more. Tableau can be an important tool for data scientists.

Data science is about understanding data: what it means, how it behaves, and how to make predictions based on it. Tableau excels at helping users understand data sets through visualizations. And once you understand the data, you can use Tableau to help build predictive models.

That said, similar to Excel, the built-in predictive modeling functions use linear regression, so users are limited to what they can do out-of-the-box. Linear regression can be a useful tool, but most real-world problems can't be solved with it. 

Tableau Creator costs $70 per user, per month, billed annually, or $840 upfront for each user. The next-cheaper tier, Tableau Explorer, is less feature-rich, but costs just $42 per user, per month, while a Viewer license costs $15 per user, per month.

Assuming that an organization doesn't have serious machine learning or predictive modeling needs, Tableau could be a great, cost-effective solution for visualizing and understanding data. However, those same organizations would be wise to invest in a more robust toolset if they want to do more than just visualize data.


A Tableau competitor, Alteryx, is quickly gaining popularity as a data analysis tool. It offers more robust machine learning capabilities than Tableau, and its pricing reflects this.

It's tough to find public information on Alteryx's machine learning pricing, but their "consumer intelligence" features cost around $34,000 a year, per user. Users on PeerSpot report that "it requires buying a server, [and] if you want a server, you have to pay $80,000."

Simply put, a single-user license is similarly priced to hiring a data scientist, so it's not a tool for the small- or medium-sized business. However, for enterprises with machine learning needs, Alteryx may be a better option than Tableau.


Data is the fuel that drives today's business engine, but it can be a challenge to keep that engine running smoothly. The vast majority of data is "unstructured" and difficult to work with, making it hard to extract the valuable insights that can help businesses make better decisions.

This is where "data wrangling" comes in. Data wrangling is the process of cleaning, transforming, and preparing data for analysis. It's a crucial part of the data science workflow, but it's often time-consuming and tedious.

Trifacta is a leading data wrangling tool that makes it easy to clean, transform, and prepare data for analysis. While it's not an end-to-end data science solution, it's an important tool that can help data scientists save time and effort when preparing data for analysis.

The professional plan, which includes robust data engineering features, goes for $400 per user per month, with an annual contract, plus $0.60 per vCPU hour. It's important to calculate these costs alongside the other tools you'll need for your data science workflow.

But if you're looking for a tool to help you wrangle your data, Trifacta is a great option to consider.


H2O AI is a unicorn startup targeting the enterprise, with a focus on financial services.

Its "driverless AI" platform is used to (semi) automate the end-to-end process of data science, including feature engineering, model training, and deployment.

An IBM datasheet shows that Driverless AI costs up to $850,000 for a 5-year subscription, while a single-user 3-year subscription is $390,000.

This makes it one of the most expensive data science platforms on the market. Even larger organizations may think twice before signing up. With that said, the robustness of the platform and its ability to handle complex data science workloads may be worth the price tag for some.


Before the term "data science" was coined, statisticians turned data into insights using tools like SAS.

With features like the SAS/STAT package, SAS became a staple in business and academia for analyzing everything from marketing data to clinical trials.

Even today, SAS is used in a wide variety of industries, including banking, insurance, retail, and manufacturing.

Despite its popularity, however, SAS has some drawbacks as a data science tool.

Chiefly, it can be difficult to learn and use. The syntax is different from other programming languages, and there is a steep learning curve. Writing SAS code can also be time-consuming.

SAS Analytics Pro costs $8,700 for the first year, which, for a statistical software, is relatively expensive.

Still, SAS has its advantages. It's a trusted and well-established tool that's been around for decades. It's also very versatile; in addition to data analysis, SAS can be used for data visualization, machine learning, and deep learning.


A competitor to H2O.AI, is an enterprise software company that specializes in delivering AI-as-a-service. It's also the most expensive offering in this list, with an SEC filing reporting that its average total subscription value for contracts entered into in 2019 was over $16 million.

That's equal to a very large and very well-funded data science team. After all, the average data scientist salary is just under $100,000 a year, with software engineers, ML engineers, and data engineers earning similar salaries. That means you could build a 160-strong data science team for the price of an average 2019 contract.

Clearly, is targeting large enterprises dedicated to integrating AI throughout their business, not just those with a specific data science problem to solve. has a strong focus on the enterprise, and its customer list includes Shell, AstraZeneca, Baker Hughes, conEdison, and many more. is a strong tool for data science teams who are looking to build and deploy machine learning models at scale. Of course, its pricing limits its appeal to only the most well-funded organizations.


An SAS alternative, MATLAB is a MathWorks software environment that has been used for data analysis, feature selection, and predictive modeling.

This softare provides an interactive environment for working with data, including importing and cleaning data, visualizing data, and performing statistical analyses. MATLAB also offers a range of built-in functions for machine learning, including Classification and Regression Trees, Support Vector Machines, and Neural Networks.

MATLAB operates on whole matrices and arrays, so it's not a tool for non-technical users. Further, users with demanding machine learning needs may find that MATLAB lacks advanced features.

MATLAB costs $2,100 for a standard perpetual license, which is similar to that of other statistical software packages. A free trial is available.


BigML is a cloud-based machine-learning platform that enables users to quickly build predictive models with minimal coding required. The platform provides an intuitive drag-and-drop interface that makes it easy to upload data, select features, train models, and make predictions. 

BigML also offers a suite of tools for more advanced users, including the ability to create custom algorithms, integrate with popular programming languages, and collaborate with team members.

Its pricing depends on the size of your data and how many parallel tasks you want to run, but you can get started for free with a limited account. Firms with bigger datasets and many parallel tasks can choose the Platinum package for $10,000 per user per month, which allows up to 64 GB datasets.

If you have larger teams, you can choose BigML Enterprise, which includes a private deployment with unlimited tasks. 1 server on BigML Enterprise is $45,000 a year, with a $10,000 setup fee. For unlimited deployments, you'll need to shell out $2.25 million a year, plus a $100,000 setup fee.


Dataiku is a recently crowned AI unicorn and leading data science tool that's helping companies around the world make better use of their data.

While it offers visual features, the learning curve is still relatively steep. Its "Discover Annual Edition," which can be used by up to 5 users and includes over 20 database connectors, costs $80,000 a year.

It's a lot cheaper than some of the enterprise options out there, but it's not for companies unsure about their data strategy.


TensorFlow is an increasingly popular open source machine learning platform. Originally developed by Google Brain Team researchers, it has seen wide adoption by the wider data science community. 

A "tensor" is a mathematical object that can be thought of as an n-dimensional array. This is a powerful data structure that allows for the representation of complex relationships. TensorFlow algorithms are designed to operate on tensors. This makes it a natural tool for data scientists who are working with high-dimensional data.

There are many reasons why TensorFlow is becoming a popular choice for data science projects. First, it is open source and thus free to use. Second, it is highly scalable and can be used on both small and large datasets. Third, it has a strong community support network. Finally, TensorFlow comes with a number of pre-built models that can be used out-of-the-box. 

One of the key benefits of TensorFlow is its flexibility. It can be used for a wide variety of tasks, including image classification, natural language processing, and time series prediction. This versatility makes it a good choice for data scientists who want to build models that are specific to their problem domain.


A picture is worth a thousand words. This saying is especially true when it comes to data visualizations. Good data visualization can help us make sense of complex data sets, see patterns that we would otherwise miss, and communicate our findings to others in a clear and concise way.

ggplot2 is a powerful data visualization tool that enables technical users to create highly customized visualizations. While ggplot2 has a steep learning curve, it is worth the effort for those who want to create sophisticated data visualizations.

Non-technical users may turn to alternatives like Tableau or Microsoft Power BI, which offer drag-and-drop interfaces and require no programming. However, these tools are constrained by their lack of flexibility and customizability. ggplot2 offers a more powerful and flexible platform for data visualization.


Another tool in the data visualization toolkit is D3.js. D3.js is a JavaScript library for creating interactive data visualizations in web browsers. It is widely used by developers and data scientists to create sophisticated visualizations, such as line charts, bar charts, scatter plots, and more.

While ggplot2 is based on R, D3 is a more low level JS library that requires more coding to get started. To use D3, you'll also need to understand HTML, SVG, JavaScipt, and CSS. However, once you get over the initial learning curve, D3.js opens up a world of possibilities for data visualization.

D3.js is a particularly powerful tool for creating interactive visualizations, such as those that allow users to hover over data points to see more information, or click on elements to trigger animations.


Matplotlib is a Python-based data visualization library. It enables developers and data scientists to create a wide variety of visualizations, including line charts, bar charts, scatter plots, and more. Matplotlib is popular for its ease of use and customizability.

Much simpler than both D3.js and ggplot2, Matplotlib is a good choice for those who are just getting started with data visualization. It is also a good option for those who need to create quick visualizations, or those who do not want to deal with the overhead of learning more complex libraries.


Making use of libraries like Matplotlib, SciPy, and Numpy, Scikit-learn is a machine learning library for the Python programming language. While it can be used for a variety of tasks, such as classification, regression, and clustering, it is most commonly used for supervised learning.

Scikit-learn is popular for its ease of use and flexibility. It offers a wide variety of features, such as pre-processing data, tuning algorithms, and creating pipelines. It also has a number of built-in datasets that can be used for training and testing models.

It's not nearly as robust as some of the other options on this list, but for those who are just getting started with machine learning, Scikit-learn is a good place to start. It also lacks the end-to-end capabilities of more robust software, and it's by no means a non-technical tool.


Weka, or Waikato Environment for Knowledge Analysis, is a machine learning software suite written in Java. It is widely used in research and education due to its extensive capabilities and ease of use.

Weka offers a graphical user interface as well as command-line options. It includes a wide variety of algorithms for tasks such as classification, regression, clustering, and feature selection. Additionally, it can be used for data pre-processing, visualization, and creating predictive models.

Created in 2012, Auto-WEKA is one of the earliest automated machine learning systems. It combines the capabilities of WEKA with the ability to automatically select the best machine learning algorithm and hyperparameters for a given dataset.


Data science is more than a buzzword—it's used by the majority of companies to improve efficiency and predict changes and outcomes. Sales teams use data science to score leads, forecast sales, reduce churn, and more. Finance teams use data science to predict fraud, model costs, forecast revenue, and more.

Any team can use data science to improve operations, but selecting the right tools is critical. The best data science tools are those that are easy to use, robust, and scalable. We've covered a broad range of top data science tools in this article, from multi-million-dollar enterprise software suites to free and open-source libraries.

The best tool for the job depends on your use case, but we believe that Akkio is the best all-around data science tool currently available. Akkio is easy to use, scales effortlessly, and offers a broad range of features. If you're looking for a data science tool that can help you take your business to the next level, Akkio is a great choice.

Sign up for a free trial today to see how Akkio can help you achieve your data science goals.

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