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Top 8 Data Science Platforms Tools

AlteryxDatabricksKNIMEMicrosoft Azure Machine Learning StudioIBM SPSS StatisticsAnacondaRapidMinerIBM SPSS Modeler
  1. leader badge
    The most valuable feature for me is integration.Good data transformation.
  2. leader badge
    Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data. The solution is easy to use and has a quick start-up time due to being on the cloud.
  3. Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: September 2021.
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  4. leader badge
    I was able to apply basic algorithms through just dragging and dropping.The solution is good for teaching, since there is no need to code.
  5. Azure's AutoML feature is probably better than the competition.I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model.
  6. The most valuable features are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial.
  7. With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages.
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  9. The data science, collaboration, and IDN are very, very strong.The best part of RapidMiner is efficiency.
  10. 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.

Advice From The Community

Read answers to top Data Science Platforms questions. 542,029 professionals have gotten help from our community of experts.
Rony_Sklar
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
User

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
Vendor

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
User

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
User

If you want to handle computer vision data, I recommend the Superb AI Suite. 
https://www.superb-ai.com/

author avatarYogesh PARTE
User

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!

Rony_Sklar
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
User

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
Consultant

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
User

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
User

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
May 25 2021
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/Feature… (more)

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/Feature Engineering. Random Sampling
  • Step 6: Model Selection and Develop Models (Training)
  • Step 7: Validate Models (Testing), Optimize and Profitability
  • Step 8: Implement Models and UAT -> Go Live
  • Additional steps for continuous monitoring, audits and enhancement
    • Document Methodology and Models
    • Monitoring and Performance Tracking and improvement of the models and 
    • Make realignment according to the business requirements.
Predictive Modeling/AI-ML Modeling implementation process

I have originally published this on RPubs https://rpubs.com/arifulmondal...

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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: September 2021.
542,029 professionals have used our research since 2012.