Monday, July 26, 2021

Best Data Science Tools & Software 2021

Data science has transformed our world. The ability to extract insights from enormous sets of structured and unstructured data has revolutionized numerous fields — from marketing and medicine to agriculture and astronomy. Drawing on mathematics, statistics, computer science, information science and other areas, data science uses mathematical formulas and algorithms to transform mountains of raw data into useful information. 

Today, businesses, government, academic researchers and many others rely on data science to tackle complex tasks that push beyond the limits of human capabilities. Within the enterprise, it’s increasingly paired with machine learning (ML) and other artificial intelligence (AI) tools to ratchet up insights and drive efficiency gains. For example, it can aid in predictive analytics, making Internet of Things (IoT) data actionable, developing and modeling new products, spotting problems or anomalies during manufacturing and understanding a supply chain in deeper and broader ways.

In the past, data science has required specific expertise. However, today’s data science software platforms are increasingly designed for use among business analysts and other citizen data scientists. Nevertheless, they approach tasks in remarkably different ways — and use different methods to aggregate data, process it and generate actionable reports, graphics or simulations. 

Some software applications focus on building elaborate models and require advanced coding capabilities. These platforms may also require specialized hardware or other systems. Others use R or Python to execute model code — but don’t support other programming languages that would expand the flexibility of the platform. Still others offer only drag-and-drop functionality. It’s possible to build models simply by manipulating objects on a computer screen and that’s the limit. 

As a result, it’s important to thoroughly understand what your organization’s needs are, what type of data science methods and approaches are best suited to your requirements and which vendors are the best fit for your industry and business model. This includes whether the software will be used by business analysts, data scientists or both and what each vendor has to offer in regard to pricing, a product road map and service and support. 

How to Select the Data Science Software Platform

Here are some key questions to focus on if you’re in the market for data science software:

  • What do you need the data science software to do? Not surprisingly, some platforms are better suited to certain types of tasks or certain industries and fields. They may capture or ingest data from specific sources, process and maintain data in a specific way and include features that enable certain types of analytics or modeling or support technology frameworks such as IoT.
  • Who will be using the software and what level of technical expertise do they have? While most of today’s platforms can be used by business analysts with some level of data science knowledge, data science solutions differ greatly. Some require deeper knowledge of statistical modeling methods, machine learning and programming languages. Others are rooted more in traditional business intelligence (BI) and may require some knowledge of Oracle Data Miner or SQL. 
  • How well is the solution designed and how good of a fit is it? As with any type of software, it’s critical to focus on the user interface (UX), features and the ability to adapt models for different purposes. Make sure a product has the flexibility your organization needs and it’s a good match for your level of expertise and overall objectives. 
  • What types of data reporting and visualization tools does the solution provide? Some platforms focus on data reporting and business intelligence, while others revolve around elaborate visualizations. These tools might also touch different areas of data science, including qualitative analytics, predictive analytics, regression analysis or text mining. 
  • What does the solution cost? Pricing varies significantly among vendors. It isn’t unusual for a solution to cost $2,000 or more per month per user, and a few charge more than $50,000 a year per seat. However, many vendors are moving to a more flexible and OPEX-friendly SaaS tiered pricing model. There also are low-priced options for SMBs, such as Microsoft Excel or open source applications that aren’t included in this roundup.
  • What is the vendor’s road map and what is its commitment to support? The field of data science is evolving rapidly. Once highly technical domains such as machine learning and deep learning are appearing in solutions. It’s critical to know where the vendor is headed and what its commitment is for supporting the platform. Also, understand the service level agreement (SLA) before signing on the dotted line.

See more: Structured vs. Unstructured Data

Top 10 Data Science Software Solutions

Alteryx

The widely used platform combines powerful analytics, data science and process automation within a single low-code/no-code environment. It incorporates machine learning and other AI methods to deliver geospatial analytics, prescriptive analytics and numerous other outcomes via visual dashboards, files and apps. 

Pros

  • Offers powerful but easy-to-use features for business leaders.
  • Integrates with 80+ data sources and outputs to numerous tools from Microsoft, AWS, Snowflake, Tableau and Salesforce.
  • Provides more than 300 no-code building blocks that facilitate data models and automation.
  • Highly rated customer support.
  • Large and robust user community.

Cons

  • Low-code environment means it may not be customizable for complex data science projects.
  • Expensive.
  • Some users complain about complexity of workflows.
  • The platform doesn’t fully support mobile use, including Android and iOS. 
  • The desktop version places heavy demands on systems.

Dataiku DSS

The solution offers a platform for data science and machine learning. It’s especially suited to multidisciplinary teams comprised of both data scientists and business users. Dataiku is available in cloud/SaaS, Windows and Mac desktop versions. It incorporates strong data visualizations, deep learning, machine learning, algorithm libraries, natural language processing and predictive modeling/analytics capabilities.  

Pros

  • Powerful no-code tools are ideal for non-data scientists.
  • Ranked as a “leader” in Gartner’s 2021 Magic Quadrant for Data Science and Machine Learning Platforms.
  • High user ratings for the interface as well as collaboration features.
  • Broad and innovative support for business metrics that extend beyond model accuracy.

Cons

  • Heavy reliance on extensions and plugins can add overhead and complexity.
  • Pricing for versions without full enterprise capabilities are high and features are limited.
  • Limited support for mobile devices.
  • Some users complain that it’s difficult to configure.

H2O.ai

The vendor offers an end-to-end data science platform that’s designed to democratize artificial intelligence. H20 AI Hybrid Cloud supports “explainable” models that work across a wide array of industries and use cases. The open-source predictive analytics platform is designed for both data scientists and citizen data scientists. 

Pros

  • Intuitive interface. 
  • Powerful predictive analytics capabilities and strong data visualization features.
  • Strong automation. Includes more than 200 data connectors and 180 open-source Python scripts.
  • Open platform deployed through Kubernetes makes it possible to use models everywhere, including virtual machines, Snowflake and IoT devices.
  • Ranked as a “visionary” by Gartner in its 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Cons

  • Data access and data preparation features aren’t as robust as some competitors.
  • Some users complain about the lack of documentation and support resources.
  • Difficult to build models from scratch.
  • Can be challenging to tweak machine learning algorithms.

IBM Watson Studio

IBM’s focus is on building, managing and deploying data models through an AI-centric approach. The cloud-based platform is designed for data scientists, developers and analysts. It is built on open source technologies such as PyTorch, TensorFlow and scikit-learn—with connections to numerous code-based and visual data science tools from IBM.

Pros

  • Suitable for use by a wide range of users, from data scientists to business analysts.
  • Flexible modular design.
  • Strong data exploration and visualization features.
  • Focus on responsible AI.
  • Ranked as a “Leader” in Gartner’s 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Cons

  • Some users complain the program is slow to load at times.
  • User Interface and navigation can be confusing, especially for those who are non-technical.
  • Expensive.
  • Complaints about inadequate documentation and support materials.

KNIME Analytics Platform

Big data and predictive analytics are at the center of the vendor’s data science platform. The cloud-based solution is designed for authoring data science machine learning workflows and projects. The open source platform includes more than 4,000 nodes for connecting to various types of data sources, and transforming them into actionable models.

Pros

  • Supports an extensive array of DSML tasks and builds strong workflows. 
  • Intuitive interface.
  • Powerful data connection and ingestion capabilities, including support for most major file types and data sources.
  • Ranked as a “visionary” in Gartner’s 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Cons

  • Data visualization features not as robust and developed as many competitors.
  • Users report a sometimes steep learning curve.
  • Limited customer support for enterprise deployments.
  • Some users complain about a lack of flexibility.

MathWorks Matlab

This data science platform, from MathWorks, is designed to develop, integrate and deploy advanced AI and ML models at scale. It serves as a programming environment for algorithm development and data analysis. It includes powerful data visualization, modeling and simulation capabilities—as well as tools for building apps and other resources. 

Pros

  • Powerful deep learning, machine learning and predictive maintenance capabilities—including in areas such as robotics and signal processing.
  • Highly flexible framework that supports distributed environments ranging from the data to the cloud and edge.
  • Verifiable and reliable machine learning, which is used by organizations that need ultra-safe and secure deployments.
  • Ranked as a “leader” in Gartner’s 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Cons

  • Too complex for most citizen data scientists. Best for engineers and dedicated data scientists. 
  • No cloud or SaaS version. Available only as a desktop version for Windows, Mac and Linux.
  • No free trial and no premium consulting or integration services available from the vendor.
  • Can perform slowly with large datasets. 

Microsoft’s Azure Machine-Learning Studio

The end-to-end data science and analytics platform offers a low-code and no-code framework for developing, training and deploying data models. It accommodates classical models as well as machine learning and deep learning. It integrates with numerous other Azure cloud components and services, as well as outside data sources. 

Pros

  • Delivers a broad and powerful portfolio of features, tools and components for data science.
  • Suitable for use by data scientists and business users. 
  • Provides flexible notebook and SDK options for expert data scientists.
  • Offers an open framework with a strong network of partners, including other analytics providers that connect to Azure.
  • Ranked as a “visionary” in Gartner’s 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Cons

  • Requires a strong understanding of Azure and its associated ecosystem of modules and services.
  • Can be difficult to use for organizations requiring hybrid and multi-cloud data science environments.
  • Users rate ease of use lower than other data science solutions.
  • Limited support for third party tools and programming.
  • Large data sets sometimes run slow.

RapidMiner Studio

The vendor’s platform offers broad and rich tools for both data scientists and business users, within a visual workflow design framework. It includes more than 1,500 native algorithms, data prep and data science functions, with support for third party libraries. RapidMiner Studio also includes strong support for notebooks and programming languages such as Python and R.  

Pros

  • Connects with virtually any data source through a point and click interface.
  • Accommodates automated in-database processing for retrieving data without the need to write complex SQL.
  • Strong data visualization and exploration capabilities.
  • Collaboration features extend across multiple roles and personas.
  • Strong security features, including single sign-on.
  • Rated as a “leader” in The Forrester Wave: Multimodal Predictive Analytics & Machine Learning Solutions for 2020.

Cons

  • Receives relatively low marks among users for model publishing flexibility.
  • Some users complain about a difficult to use and inflexible interface.
  • A free edition provides limited features and capabilities. Other versions are pricey.
  • Some complaints from users about outdated looking visual output, including charts, graphs, animations and video.

SAS Visual Analytics

The vendor, a longstanding leader in data science, offers an enterprise platform focused heavily on analytics visualizations, composite AI, MLOps and decision intelligence. It supports virtually all major data sources and types, has customizable dashboards with templates, and includes robust publishing features with numerous pre-built visualization formats. 

Pros

  • Especially strong in predictive analytics, pattern recognition and machine learning.
  • SAS has established a partnership with Microsoft to support tight integration with Azure and Machine-Learning Studio.
  • Dedicated iOS and Android apps and responsive design for mobile web access. 
  • Excellent scalability with support for large numbers of users.
  • Ranked a “leader” in Gartner’s 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Cons

  • Installation and configuration can be difficult.
  • Lags behind other solutions for ease of use.
  • Limited open source support.
  • Some users complain that the user interface is somewhat drab and dated, and the platform can be difficult to learn.
  • Expensive.

Tibco Spotfire

The data visualization platform generates insights through NLQ powered search, AI-driven recommendations, and direct manipulation. It includes immersive dashboards and advanced analytics support for predictive analytics, geolocation analytics, and streaming analytics. The cloud-based platform is designed for both dedicated data scientists and other users.

Pros

  • Includes more than 60 native connectors to major data sources, along with custom connections via rich APIs.
  • Offers AI-driven recommendations and natural language search that simplify things for non-technical users.
  • Enables powerful collaboration among multiple personas and user groups.
  • Dedicated iOS and Android apps, along with responsive design for mobile browsers.

Cons

  • Citizen data scientist features and support lag behind other vendors.
  • Some users complain that the platform needs a more user-friendly interface.
  • Limited customization and scripting features can make more advanced modeling and data visualization difficult.
  • Some users complain that data loading and system performance can be slow.

See more: Top Data Visualization Tools for 2021

Top Data Science Software Comparison Chart

Data Science Software Pros Cons
Alteryx Designer
  • Powerful features and easy to use
  • Integrates well with data sources and software
  • Strong no code features
  • High customer ratings
  • Not highly customizable
  • Limited support for mobile devices
  • Workflows can be complex
  • Desktop version puts a heavy demand on hardware
Dataiku DSS
  • No-code tools are ideal for non-data scientists
  • Supports diverse business cases and metrics
  • Ranked as a “leader” by Gartner
  • High reliance on extension and plugins
  • Limited support for mobile devices
  • Limited configurability
  • Can be pricy
H2O.ai
  • Intuitive interface
  • Power predictive analytics and visualization features
  • Excellent automation
  • Open platform
  • Ranked as a “visionary” by Gartner
  • Lacks some data access and prep features
  • Documentation is sometimes lacking
  • Difficult to build models from scratch
  • Difficult to tweak machine learning algorithms
IBM Watson Studio
  • Powerful features
  • Suitable for use by non-data scientists
  • Strong data exploration and visualization
  • Focus on responsible AI
  • Ranked as a “leader” by Gartner
  • Can perform slowly with large data sets
  • User interface can be daunting
  • Expensive
  • Some user complaints about the lack of documentation and support materials
KNIME Analytics Platform
  • Supports numerous tasks and workflows
  • Intuitive interface
  • Powerful data connection and ingestion 
  • Ranked as a “visionary” by Gartner
  • Lags behind others in data visualization
  • Steep learning curve
  • Limited customer support
  • Users say the solution lacks flexibility
MathWorks Matlab
  • Strong deep learning, ML and predictive maintenance
  • Very flexible
  • Ideal for situations where reliable and accurate results are critical
  • Ranked as a “leader” by Gartner
  • Too complex for citizen data scientists
  • No cloud or SaaS version
  • Can perform slowly with large datasets
  • Limited vendor support
Microsoft Azure Machine-Learning Studio
  • Feature rich
  • Suitable for both data scientists and others
  • Highly flexible notebooks and SDK
  • Open framework
  • Ranked as a “visionary” by Gartner
  • Requires a strong understanding of the Azure ecosystem
  • Not well suited to hybrid and multi-cloud environments
  • Can be difficult to use
  • Limited third party connections
  • Large data sets sometimes run slow
RapidMiner Studio
  • Excellent connectivity with data sources
  • Strong visualization and exploration
  • Powerful collaboration features
  • Ranked as a “leader” by Gartner
  • Model publishing lags behind competitors
  • Interface can be confusing and inflexible
  • Expensive
  • User complaints about outdated visuals
SAS Visual Analytics
  • Powerful predictive analytics, pattern recognition and ML
  • Close partnership with Microsoft and Azure
  • Excellent mobile support
  • Highly scalable
  • Ranked as a “leader” by Gartner
  • Installation and configuration can be difficult
  • Can be difficult to use
  • Limited open source support
  • Expensive
  • Users complain that the interface is drab and dated
Tibco Spotfire
  • Excellent connectivity with data sources
  • Provides natural language support and AI driven recommendations and guidance
  • Strong collaboration
  • Excellent mobile support
  • Better suited for data scientists
  • Some users complain that the interface isn’t user friendly
  • Limited scripting and customization
  • Data loading and performance can be slow

 

Similar articles

Latest Articles

Data Science Market Trends...

When famed mathematician John W. Tukey postulated that advanced computing would have a profound effect on data analysis, he probably didn’t imagine the full...

Data Recovery Market Trends...

Data recovery is more important than ever in this era of constant cyber attacks and ransomware. The Verizon Data Breach Investigations Report (DBIR) looked...

Trends in Data Visualization

In a world of big data, visualization is becoming a key skill set that every business must master.  Digital technology has transformed the way businesses...

Microsoft Data Portfolio Review

With a host of analytics services for almost any situation, Microsoft Azure’s data services have got just about every base covered.   In the world...