We performed a comparison between H2O.ai and IBM SPSS Statistics based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The ease of use in connecting to our cluster machines."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."
"It has the ability to easily change any variable in our research."
"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."
"It is a modeling tool with helpful automation."
"SPSS is quite robust and quicker in terms of providing you the output."
"IBM SPSS Statistics depends on AI."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
"I would like to see more features related to deployment."
"The model management features could be improved."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."
"In some cases, the product takes time to load a large dataset. They could improve this particular area."
"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people. I found that everything was very confusing at first. Having clear documentation would help alleviate that."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
"Perhaps in terms of visualization. It's not really easy to do some data visualization, just simple, descriptive analysis in SPSS. I think that could be an area for improvement."
"SPSS is a tool that's been around since the late 60s, and it's the universal worldwide standard for quantitative social science data analysis. That said, it does seem a bit strange to me that the graphical output functions are so clunky after all these years. The output of charts and graphs that SPSS produces is hideous."
"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior. You can call it augmented analysis or predictive analysis. The bottom line is it should have more graphical and analytical capabilities."
Earn 20 points
H2O.ai is ranked 19th in Data Science Platforms while IBM SPSS Statistics is ranked 8th in Data Science Platforms with 36 reviews. H2O.ai is rated 7.6, while IBM SPSS Statistics is rated 8.0. The top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". On the other hand, the top reviewer of IBM SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and KNIME, whereas IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler and Weka.
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