Data Science Platforms Features

Read what people say are the most valuable features of the solutions they use.
Abhik Chakraborty says in a Dataiku Data Science Studio review
Senior Business Technology Analyst at a consultancy with 5,001-10,000 employees
* Process scheduler (called Scenario). * Cloud-based process run, which helps in not keeping the systems on while processes are running. View full review »
Michael De Groot says in an IBM Watson Studio review
Product owner at ING
We are looking for ways to automate even more. So, we are trying to avoid any manual effort currently necessary. With the whole machine learning part currently at the top of the market, we are trying to see how we can improve in the automation part. View full review »
Hilton Rossenrode says in a KNIME review
Business Analyst at a retailer with 501-1,000 employees
* Visual workflow creation * Workflow variables (parameterisation) * Automatic caching of all intermediate data sets in the workflow * Scheduling with the server View full review »
Evans Otalor says in a KNIME review
Business Intelligence Manager at Telecoms
Easy to use nodes for ETL processes. This is because, in many cases, I usually transform the data before the main task even when the data is from a structured database. View full review »
Giovanni Marano says in a KNIME review
Senior Data Scientist
* Easy to connect with every database: We use queries from SQL, Redshift, Oracle. * Easy to have a clear view of the data at every single step of the ETL process, with the consequent possibility of changing the flow according to your needs. View full review »
Wim Michielsen says in a KNIME review
Data Science Consultant
* The very easy-to-use visual interface * Help functions and clear explanations of the functionalities and the used algorithms * Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis For inexperienced analysts or data scientists, it is a very easy tool to take your first steps in modeling and analytics. View full review »
Dusty Evely says in a KNIME review
Business Analyst at a tech services company with 201-500 employees
I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data. View full review »
DerekWilson says in a RapidMiner review
President and CEO at a tech services company with 1-10 employees
I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries. View full review »
Agus Kurdiyanto says in a KNIME review
Solution Integrator at a comms service provider with 11-50 employees
Most important, it is open-source. Next is the ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database. View full review »
Prithviraj Dutta says in a KNIME review
Data Scientist at a tech services company with 1,001-5,000 employees
The most useful features are the readily available extensions that speed up the work. For instance, KNIME offers multiple document taggers, which one can use with relative ease. Similarly, the number of predefined NER taggers are also very handy. View full review »
Alberto Guisande says in an Alteryx review
Director at Decision Science
* The codeless concept and speed of processing are unique. Super intuitive to use and easy to master. No coder needed. * I did not find a data source that I could not connect to yet. * In/out, preparation, blending, parsing, and transformation tools are the fundamental ones to start analysis. * Spatial operations and analytics are super powerful for analysis which requires these types of models/operations. View full review »
Foundpa67 says in an IBM SPSS Modeler review
Founding Partner at Altdata Analytics
* Automated data cleansing, transformations and imputation of missing data. * Some basic form of feature engineering for classification models, automated binning, etc. This really quickens the model development process. * Automated modelling, classification, or clustering are very useful as well. View full review »
Scott Genzer says in a RapidMiner review
Senior Community Manager at a tech vendor with 51-200 employees
* Availability of cutting-edge data science tools and algorithms * Ease of code-optional GUI * Open Source Java core * Easy integration with APIs, Python, R, cloud storage, cloud computing, etc. View full review »
reviewer461148 says in a KNIME review
Partner, Turkey and The Netherlands at a tech vendor with 201-500 employees
* Easy ETL operations * Rich algorithm set * Integrated with other languages like R, Python, and Java. * Works together with other technologies like DeepLearning4j, H2O.ai, D3.js, and Weka. * Ease of use and being a performant solution. * Continues development and wide community support View full review »
reviewer1060461 says in an IBM Watson Studio review
Data Scientist at a tech vendor with 11-50 employees
The computer version was very helpful. It returns approximately five clusters of the projected features. The solution is very easy to use. View full review »
Volkan ÇAma? says in an IBM SPSS Statistics review
Principal Consultant at Caligo
Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files. The current features meet with the needs of our company. Our needs are not complex for the features offered. View full review »
CEO at Inosense
The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure. You just have to drag and drop the services into your pipeline, and it can be applied through the pipeline. It's very helpful for data scientists. If you don't have any special knowledge in data science, just to know that you want to consume a service, that's all you need. They have a tool for data gathering from some social networking sites such as Twitter and Facebook, which is great. View full review »
reviewer978702 says in a SAS Enterprise Miner review
Founder and CEO with 11-50 employees
Normally I use the SAS 6 Miner, it's a component of SAS Enterprise Miner. It's very useful. View full review »
reviewer1014468 says in a Dataiku Data Science Studio review
Practice Manager Data Intelligence at a tech services company with 1,001-5,000 employees
The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction. View full review »
reviewer1025025 says in a RapidMiner review
Project Engineer at a engineering company with 10,001+ employees
The documentation for this solution is very good, where each operator is explained with how to use it. There is a very clear structure between building a process and then looking at the results. View full review »
Ali-Megahed says in an IBM SPSS Statistics review
Senior Statistical Consultant at a financial services firm with 501-1,000 employees
Most of the product features are good but I particularly like the linear regression analysis. I also benefit from the import and export of data abilities. View full review »
Sebastien Peyron says in an Alteryx review
Technical director, BGFi Data & Analytics - Asia Pacific at a tech company with 1,001-5,000 employees
The most valuable feature of this solution is data preparation. Alteryx Connect (data catalog) is also really important for us. View full review »
Angus Lou says in a KNIME review
Head Of Business Solutions | Unmanned Shop | Automated Retail | AI | IoT | Robotic | Data Science with 51-200 employees
This solution is easy to use and especially good at data preparation and wrapping. It is useful for making a data pipeline to automate data processing tasks. The most valuable feature is to automate what is manually processed. View full review »
Tristan Bergh says in a Databricks review
Data Scientist at a consultancy with 10,001+ employees
Immense ease in running very large scale analytics, with a convenient and slick UI. This saved us from having to tweak, tune, dive into deeper abstractions, get involved in procurement, and also having to wait for other workloads to run. The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly. The Delta data format proved excellent. Databricks had already done the heavy lifting and optimized the format for large scale interactive querying. They saved us a lot of time. View full review »
ChrisDaly says in an Amazon SageMaker review
Vice President & CIO with 51-200 employees
The most valuable features of this solution are the Random Cut Forest and the IDE. View full review »
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