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Top 10 Data Science Software Tools to Help Make Your Life Easier


14 min • 01 June, 2021

There’s no denying we live in a data-driven world. As a business or data scientist, you know how you collect, store, process, and ultimately analyse your data will directly impact the quality of your results and consequential decisions.

That’s why you need to do everything in your power to manage and analyse your data as well as you can.

After all, take a moment to consider how you use data to make crucial decisions and how those decisions could be misguided if the managed and analysed data is incorrect or misleading in any way. This is why you need to pay special attention to the tools you’re using for your research process.

In this post, we’ll deep dive into the top 10 data science software tools available today. We’ll discuss why you’ll consider using them and how they could be everything you need to help you solve your data problems.

Data Science Data Tools At a Glance

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1. Gyana

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Gyana leads our charge into the best of the best data science software and tools. How couldn't it: we built it!

The leading reason why Gyana wants to top your list of considerations is simply how easy it is to use with a next-to-no learning curve. Anyone can pick it up, import their data, and get analysing in just a few clicks. There are many open-source applications and professional tools out there, but so many require you to know coding to make the most of it, or spend days, if not months, learning how all the features work.

Data giving you nightmares? Not with Gyana.

Gyana is a no-code business intelligence solution that requires no prior experience whatsoever. Thanks to its built-in machine learning systems, you simply import your complex data, click a few buttons, and the software will take care of the rest. This is undoubtedly one of the most modern approaches to data science you’ll have ever seen.

It doesn’t matter where your data is currently; Gyana will handle it since it’s compatible with a broad range of file types and sources, including Google Sheets, Moz, Hubspot, Intercom, and so many more. Click, import, click again, and you’ll have data-ready reports ready to rock and roll, all Excel-ready, of course.

With a dedicated backup system to ensure you never lose your precious data, and automatic analytical insights that tell you everything you need to know at a glance, there’s little doubt Gyana is the future of data science tools.

Key Features

  • An automated data science machine learning-driven software.
  • Entirely scalable for the size of your project (only pay for what you need).
  • No data science experience or coding knowledge required.
  • An elite customer support team to guide you through the process.
  • Compatible with a vast range of data import and export sources.

Try Gyana for free.

2. DataRobot


Data Robot has long been one of the leading predictive analysis software models used by professionals in all areas of tech, including IT professionals, business executives, and data scientists. Used for numerous applications, DataRobot prides itself on being an easy to use and highly automated data science tool, meaning there’s little work for the scientists to do.

Other than letting it run and do what it does best.

DataRobot is easy to use, effortless to import data from various sources and has some very sophisticated predictive models that can give you the results you’re after in just a few clicks. Most importantly, you don’t need to know any code for this solution to work. Just plug in your data and go.

This is a web-based, cloud-based service, so everything is handled through your web browser, and the solution is scalable for businesses of all sizes (there are enterprise versions available). All your standard data formats are compatible, including Trifacta, AWS, Tamar, Tableau, Google Cloud, and so on.

Overall, Data Robot is really great value for money.

Key Features:

  • Allows for Hadoop cluster plug and play
  • Lots of visualisation tools
  • It has a vast catalogue of advanced algorithms for you to use
  • Automatically fixes potential errors in processing itself

You can check out Datarobot on their website.

3. Qubole


Qubole has been around for a long time in terms of tools for data science. Founded back in 2011, the company is an award-winning service, recognised by accolades, including the G2 Crowd Leader in 2020 and ranking 503rd in the Inc. 5000 list.

What we love about the Qubole service is the fact that it’s, in essence, a pay as you go service.

There’s no flat monthly or annual fee, but rather a small charge you pay per Qubole computing units. This means it’s easy to integrate into your data model, but you save on your cloud costs and get your results fast.

Apparently up to 10x as fast in terms of productivity as a data science administrator, but that’s not proven.

Qubole tends to focus on a concept known as ‘active data’. When you have a big data lake (a lot of data coming from many sources (data mining included), but not in one single traditional database), Qubole suggests that only around 10% of data is active during analysis. The software aims to switch things up by activating up to 90% of the data during processing.

From the results of customers, it does a reasonably decent job. The application is broken down into several nodes known as Spark, Presto, Hive, Quantum, Rubix, and Airflow, all of which handle their own dedicated tasks and processes.

However, they come together to create a comprehensive analytical solution for marketing data, AD-HOC analysis, customer micro-segmentation, multi-channel marketing analysis, real-time analytics, and more.

But be warned, Qubole is not entirely suitable for first-time users, and you will need to invest time figuring out what you’re doing and how to use the application to its full potential.

Key Features:

  • Turn your data into actionable insight results for all kinds of processing
  • Highly compatible with an extensive range of database sources
  • Cloud-based and insanely scalable due to pricing model
  • Very easy to integrate into existing models
  • It can be challenging to learn and understand how to use it without training

Here is the link to Qubole's website.

4. Trifacta


If you’re looking to use the data science tools that the professionals are using, then Trifacta is for you. Adopted by over 10,000+ international companies, some of the key brands that take advantage of this software include the likes of Autodesk, Spar, Pepsico, the Bank of America, GSK, and so many more.

Granted, Trifacta does make things easy.

It’s extensively automatable, so processing tasks that traditionally took months now just take mere minutes, and the results are fully scalable. The software is fully compatible with a vast range of cloud networks, hybrid designs, or even multi-cloud environments and creates insanely visual representations of your data to ensure the highest degree of accuracy.

It doesn’t matter if your data is on Google Cloud, Snowflake, AWS, Microsoft Azure, or a mixture of all of them; Trifacta will bring it all together and make it more accessible than it’s ever been. There’s a reason this is rated one of the number one no code data science applications by so many authorities.

Backed by machine learning that removes potential data processing bottlenecks and offering real-time, self-service reporting for your projects, Trifacta really does make life easy. The only downside is that it’s a little complicated to learn when you’re starting out. Customers review the level of support as below average, leading us to believe this company places their primary focus on their enterprise customers.

Key Features:

  • Incredibly seamless to connect to all cloud platforms and services
  • Highly interactive data reports where you can see and modify your data with ease
  • No coding required - just clicks
  • Highly automatable
  • An award-winning enterprise application

Check out Trifacta on their website.

5. Altair


Altair was formerly known as Datawatch but is now a part of the Knowledge Studio suite. Altair contains many decent, impactful features, but it is a bit of a barebones, WYSIWYG-styled application. There are a few patented applications here, as the Decision Trees feature and workflow feature.

You can import your data from all kinds of common languages, including R and Python, SAS, CSV, Excel sheets, and more, but this can feel a little basic compared with some of the leading applications we’ve spoken about already.

However, if you’re a small business and this is all you need, then there’s little doubt this could be the solution you’ve been looking for.

As you would expect, there are many machine learning features here to help you with your data. There’s standard analysis to help process what you’ve got, but also advanced predicting systems, simulation features, and complete compatibility with the cloud and cloud analytics platforms which means the visualisation opportunities here are plentiful.

If you want to keep things plain and straightforward, Altair is well worth consideration.

Key Features:

  • Built-in fraud detection
  • Automatic email and phone number extraction algorithms
  • Accepts common language imports and exports
  • Offers advanced predictive data modelling

Check Altair's website here.

6. Lumen Data


Lumen Data is a very popular, straightforward approach to data science and analytics. It’s another one of those platforms used by some of the top companies globally, including the links of Nintendo, Netflix, HP, Starbucks, many US universities, and more.

You’ll find all the features you’d expect here, including single-view data mastery that helps you see the results of anything you want and have the data for. Whether you’re looking for overviews on your customers, products, or services, Lumen Data can help make it happen.

There are features to help ensure the quality of your data, data lake integration, vast integration features that allows Lumen to connect with your existing infrastructure, cloud and hybrid deployments. Of course, there’s also AI and machine learning predictive analysis.

There’s no doubt that Lumen is one of the most accessible data science platforms to wrap your head around with a mild learning curve. Just make sure you’re taking the time to figure out whether this service is specialised towards what you want to achieve and whether you’re able to invest the time in adjusting it to suit your aims.

Key Features:

  • Offers a specialist team to help integrate the system effortlessly
  • Plenty of features available to help you manage and analyse your data
  • Used by some of the top companies in the world
  • Comes with predictive analysis to help you make sense of your data

You can visit Lumen Data's website by clicking here.

7. Paxata


Paxata is another self-service data prep and management tool that features predictive AI systems to help make your life easier. Paxata is actually a child product of Data Robot, which we spoke about earlier, which aims to make the entire analysis process as clean and as simple as possible.

There are plenty of organisations and companies who have opted for this service, but it seems mainly analysts and IT leaders are the main adopters. The service prides itself on being incredibly interactive, incredibly scalably thanks to its cloud compatibility, and intelligent due to its ability to quickly carry out complex tasks.

However, perhaps one of the main draws of this service is that it’s highly collaborative, meaning many team members can get involved. These features ensure everyone can work together with ease, which only makes managing and making the most of your data a breeze.

Key Features:

  • A very collaborative-ready platform
  • One of the most interactive data science analysis programs around
  • AI and machine learning services to help you analyse and use your data quickly
  • Used in a variety of industry from healthcare to tourism

Check out Paxata here.

8. Apache Hadoop


So far, we’ve explored a lot of closed platforms. You set up the service and get to work, but sometimes a solution won’t hit the spot with what you’re looking for. Either the service is too restrictive, and you need a little more flexibility, or it just doesn’t offer all the features you need to scale quickly and in the way you want. For those of you in this boat, this is where Apache Hadoop comes into play.

Apache Hadoop, the creators of Spark, is an open source data tool, which means it’s constantly updated and maintained by a community of developers, it’s insanely scalable for any business or data science venture, and it’s one of the most universally known data science tools, meaning literally everything is compatible with it.

Imagine you’re moving to a new house. You could buy a house ready built, and ready to go, or you could have all the materials on hand to build your own exactly how you like it. The latter solution is what Hadoop has to offer. It’s a core framework for you to build on and create what you want.

Key Features:

  • A free, open source data solution, and available to anyone!
  • Highly compatible with an extensive range of services
  • Lots of features and data science functions to take advantage of
  • Requires a lot of time and investment to learn and understand
  • Has an extensive community to help offer support

Visit Apache Hadoop's website to know more.

9. SAS


SAS isn’t so much a tool that will help you analyse your data, but rather an academy to help you learn the extensiveness of data science and to help you come to grips with how the science works and how you can use the tools to the best of your ability. After all, there’s no point in forking out for a premium analytical solution if you don’t know how to use it or what you’re trying to achieve.

There are plenty of courses and programs here that you can sign up for, including data curation, advanced analytical processing, and getting hands-on with AI and Machine Learning, an essential modern element of data science.

The platform allows you to learn in the way that’s best for you, which includes a stream of articles, guides, and videos, real-world case studies so you can see your teachings in practice, and a worldwide community that you learn alongside.

Key Features:

  • Courses cover a ton of languages, including R, Python, Hadoop, Hive, and more
  • Learn at your own pace and in your own way
  • Get full access to SAS software so you can work while you learn
  • Earn a certification to prove you know what you’re doing, ideal for a data science career

Visit SAS' website here.

10. Microsoft Excel


Last but not least, but also perhaps most surprisingly, we have Microsoft Excel. Excel has been at the forefront of data management for decades now, all the way back when businesses would use spreadsheets and formula systems for everything from stock management to accounting. However, you’d be wrong if you thought the system was a little too primitive for data science.

Many data scientists refer to Excel as one of the best 2D visualisation tools for managing data. The fact you can integrate it with Python coding and other tools, like Tableau, means you can really go above and beyond what you’d expect Excel to be capable of.

Now, that’s not to say that Excel is the best of the best. There are many other solutions we’ve spoken about that will use machine learning and make things so much easier. However, if you’re looking for a clean and straightforward way to organise and manage your data, create powerful visuals, and make your life easy, then Excel is undoubtedly a great way to go about it.

When you consider that many data science solutions effortlessly accept Excel content as a core way of importing data, then this is really a tool you should be using anyway!

Key Features:

  • Universally compatible with a ton of products and data science applications
  • Create stunning 2D visuals to represent your data accurately
  • Compatible with an extensive range of add-ons and the Python language
  • Easy to use and learn and highly collaborative

In case you didn't meet this essential piece of software before, you can check out Excel on Microsoft's website.

Final thoughts

When you first set out to manage and analyse your data, it can be hard to know where to begin and to even identify what you’re aiming for. However, as you can see, there are plenty of data science solutions out there that can help you step closer to the results you’re looking for, and therefore making the decision that will make your projects successful.