We performed a comparison between IBM SPSS Modeler and TIBCO Statistica based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."Some basic form of feature engineering for classification models. This really quickens the model development process."
"It's a very organized product. It's easy to use."
"The supervised models are valuable. It is also very organized and easy to use."
"A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."
"We are creating models and putting them into production much faster than we would if we had just gone with a strict, code-based solution, like R or Python."
"It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful."
"New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data."
"In the solution, I like the virtualization of data flow since it shows what goes where, which is mostly the strength of the tool."
"One of the best features of TIBCO Statistica is its block-based solution building."
"Dimension reduction should be classified separately."
"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."
"It's not as user friendly as it could be."
"When you are not using the product, such as during the pandemic where we had worldwide lockdowns, you still have to pay for the licensing."
"We have run into a few problems doing some entity matching/analytics."
"The product does not have a search function for tags."
"Requires more development."
"Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way."
"TIBCO Statistica could improve by expanding its neural network capabilities to support multi-layer networks, addressing user demands for advanced tasks like image analysis."
IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews while TIBCO Statistica is ranked 17th in Data Science Platforms with 1 review. IBM SPSS Modeler is rated 8.0, while TIBCO Statistica is rated 8.0. The top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". On the other hand, the top reviewer of TIBCO Statistica writes "Provides versatility in analysis and problem-solving". IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and Alteryx, whereas TIBCO Statistica is most compared with IBM SPSS Statistics, TIBCO Data Science and SAS Predictive Analytics.
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