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Top 8 Data Science Platforms Tools

AlteryxDatabricksKNIMEMicrosoft Azure Machine Learning StudioIBM SPSS StatisticsRapidMinerIBM SPSS ModelerDataiku Data Science Studio
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    You get more support with Alteryx, and it's good for non-sophisticated users who can benefit from the support included in the price.I like the solution's velocity, the speed with which it processes data, and its ease of use.
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    The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes.
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    The workflows that you build in KNIME can be transferable and this process is a lot easier than with other types of technological setups.I was able to apply basic algorithms through just dragging and dropping.
  5. The most valuable feature is its compatibility with Tensorflow.Azure's AutoML feature is probably better than the competition.
  6. The most valuable features are the small learning curve and its ability to hold a lot of data.SPSS is quite robust and quicker in terms of providing you the output.
  7. We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space.
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  9. The supervised models are valuable. It is also very organized and easy to use. You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after.
  10. The solution is quite stable.I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.

Advice From The Community

Read answers to top Data Science Platforms questions. 564,643 professionals have gotten help from our community of experts.
Hello community members, There are many Data Science Platforms available. Which platform would you recommend that can handle large amounts of data? Why?
author avatarZiad Chaudhry

DakaIku is a great general purpose data science platform for both supervised and unsupervised learning. It handles Big Data very well.

author avatarAaronCooke
Real User

Sparkcognition's Darwin product can handle very large data sets. 

author avatarDjalma Gomes, Pmp, Mba

Data science platform is a vague term.  

It all depends on what you wish to accomplish. Are you talking about fast databases, ETLs, a Machine Learning tool, integration with R or Python, Self-Service Data Visualization Tool, Collaboration? No size fits all...

author avatarJinhyung Cho

Dataiku, Domino, RapidMiner are notable candidates for your purpose, I presume. 

It has been 2 years when I checked several vendors and made the list as candidates. They all support large-scale data manipulation for data analysis and machine learning development as a platform that can be used by many people in a collaborative way.

author avatarreviewer900012 (Professor of Health Services Research (now Emeritus) at a university with 1,001-5,000 employees)
Real User

I suspect that I cannot answer this. I have used Knime and RapidMiner with data sets that have had up to about 80,000 rows and 1,500 columns and both have performed well. However, I doubt whether the questioner would classify my usage as "large amounts of data". If my usage is like theirs, then both packages can be recommended.

Both Knime and RapidMiner offer the facility to link with Python or R, and those languages have modules or methods which offer better performance on large data sets (multi-processing or using GPUs, etc.), so those combinations might serve their purpose. So, they might use, say, Knime for ease of use and, say, R for the excess power or RapidMiner and Python.

author avatarHyundong Lee

If you want to handle computer vision data, I recommend the Superb AI Suite. 

author avatarYogesh PARTE

The question also needs to specify which domain, what kind of data and public or private platforms. 

For structured/tabular data driverless AI / H20.ai sparkling water is my preferred platform. 

author avatarreviewer1260093 (Professor of Health Services Research (now Emeritus) at a university with 1,001-5,000 employees)
Real User

My experience has not been on large scale systems. Not even  multi-terabytes. My mult-megabytes would not help. Sorry!

Hi peers, There are so many data science platforms to choose from. Which platform would you recommend to enterprise-level companies that want flexible and powerful data visualization capabilities to drill down into the data?  What makes the solution that you recommend a better choice than others?
author avatarGavin Robertson

Need to address basic data issues, e.g., quality, standardization and security, and MDM first, to obtain meaningful data visualization and single entity views, e.g., customer, patient and product. Ideally, a visualization tool should be able to interact with a backend actionable data catalog driven by data virtualization/federation either directly or through data provisioning. Power BI, QlikView and Tableau are excellent standard data visualization tools. Cambridge Intelligence's KeyLines is an excellent interactive graph visualization tool.

author avatarWillie Jacobs
Real User

We have been using Qlik Sense for the past 2 years and purchased but never really used Qlik View before that. We have used excel extensively and seen demos and tried Power BI and looked at demos for a couple of other BI tools.

We settled on using Qlik Sense as our Reporting, BI and Analytics tool due a very successful proof of concept delivered by our Qlik consultants.

Qlik Sense gives us the ability to visualize our data in various ways from simple bar and line charts or combined to scatter plots, mekko charts, funnel chart, pie charts, gauge charts and KPI items. Visualization options include table and pivot table that can be utilized to display detailed data. Visualizations also include a map chart that can be used to visualize various map layers with to display movement, density, are and points. 
This has been extremely valuable being from a logistics company.

I would therefore recommend Qlik Sense for the best visualization capabilities.

author avatarPeter Eerdekens
Real User

QlikSense. The associative analytics engine makes it kind of child's play to combine multi-source data and in combination with the augmented intelligence features QlikSense helps to create analytics and visualizations faster.

author avatarreviewer1450293 (Co-Founder at a computer software company with 11-50 employees)
Real User

Qlik and PowerBI are great tools. I'd say most times IT people go to these tools, Qlik for ease and PowerBI because it works with Microsoft365.  

I think non-technical users will always lean toward Tableau since it is an easy and more Agile tool meaning drag, drop and change. Particularly PowerBI requires you to know what you want first and then build bottom-up, versus Tableau you can change your way to your final dashboard.

author avatarJorge Barroso

In my case, I can recommend Power BI, that works very well with a lot of database. It shows very good visualization graphs that allows to create many dashboards easily and connect with many data sources that can work very good to present, share and compare data thought the company and with users.

author avatarJAMAL AL MAHAMID

When considering a BI tool, everyone looks for the leaders: Power-BI, QlikView and Tableau. 

They all offer ease of use. However, consider Power-BI, QlikView for the technical team and Tableau for more business-oriented users. 

Note, Power-BI is more compatible with MSFT365 than others. 

You may also consider looking at MetrixPlus that provides additional features for automating workflows to get data and to deliver Enterprise Architecture-based performance management.

author avatarVictor Feria

There are powerful options. QlikView, Tableu and PowerBi offers agile implementation.

author avatarreviewer1066977 (Solution Architect/Technical Manager - Business Intelligence at a tech services company with 5,001-10,000 employees)
Real User

Now a days lot of visualization tools coming in the market, its difficult for anyone to choose from these variety of tools. However there can be various parameters which will help choose right set of Visualization tool for your requirements.

1. User Friendliness

2. Self Service Capability

3. Connectivity / compatibility with different systems that are available in the market

4. Compatibility with Cloud service providers

5. Relational, big-data systems and data lake connections, AI-ML and predictive analytics capabilities

6. License Cost 

I would recommend Power BI and Tableau as they provide lot of features and visualizations to choose from, with reasonable cost and connectivity with major systems.

Data Science Platforms Articles

Ariful Mondal
Consulting Practice Partner - Data, Analytics & Artificial Intelligence at Wipro Ltd
Following primary steps should be followed in Predictive Modeling/AI-ML Modeling implementation process (ModelOps/MLOps/AIOps etc.) Step 1: Understand Business Objective Step 2: Define Modeling Goals Step 3: Select/Get Data Step 4: Prepare Data Step 5: Analyze and Transform Variables/Featu...
Read More »
Prithwis De, PhD, CStatNicely articulated
AtanuChakrabortyPrecise illustration
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: January 2022.
564,643 professionals have used our research since 2012.