Compare H2O.ai vs. IBM SPSS Statistics

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H2O.ai Logo
7,485 views|4,953 comparisons
IBM SPSS Statistics Logo
4,039 views|3,128 comparisons
Most Helpful Review
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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"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."

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"Most of the product features are good but I particularly like the linear regression analysis.""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 best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important.""You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use.""They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do.""It has the ability to easily change any variable in our research.""The most valuable feature is the user interface because you don't need to write code.""In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."

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Cons
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

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"I think the visualization and charting should be changed and made easier and more effective.""Technical support needs some improvement, as they do not respond as quickly as we would like.""The statistics should be more self-explanatory with detailed automated reports.""Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance.""The product should provide more ways to import data and export results that are user-friendly for high-level executives.""The design of the experience can be improved.""This solution is not suitable for use with Big Data.""Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."

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Pricing and Cost Advice
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"We think that IBM SPSS is expensive for this function.""The price of this solution is a little bit high, which was a problem for my company.""The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

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Top Answer: You can quickly build models because it does the work for you.
Top Answer: In comparing the price of other products, SPSS Statistics is too expensive. Even when most of the universities in the Middle East have licenses for SPSS Statistics, they do not have licenses for the… more »
Top Answer: The technical support should be improved.
Ranking
14th
Views
7,485
Comparisons
4,953
Reviews
1
Average Words per Review
475
Rating
7.0
5th
Views
4,039
Comparisons
3,128
Reviews
15
Average Words per Review
720
Rating
7.9
Popular Comparisons
Also Known As
SPSS Statistics
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Overview

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making.
Offer
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Learn more about IBM SPSS Statistics
Sample Customers
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA
Top Industries
VISITORS READING REVIEWS
Computer Software Company31%
Comms Service Provider15%
Financial Services Firm8%
Media Company5%
REVIEWERS
University29%
Financial Services Firm21%
Aerospace/Defense Firm7%
Non Profit7%
VISITORS READING REVIEWS
Comms Service Provider26%
Computer Software Company15%
Educational Organization14%
Government6%
Company Size
REVIEWERS
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
REVIEWERS
Small Business28%
Midsize Enterprise22%
Large Enterprise50%
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: June 2021.
511,521 professionals have used our research since 2012.

H2O.ai is ranked 14th in Data Science Platforms with 1 review while IBM SPSS Statistics is ranked 5th in Data Science Platforms with 15 reviews. H2O.ai is rated 7.0, while IBM SPSS Statistics is rated 8.0. The top reviewer of H2O.ai writes "Good collaboration functionality, but better integration with Python for data science is needed". On the other hand, the top reviewer of IBM SPSS Statistics writes "Offers good Bayesian and descriptive statistics". H2O.ai is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and Alteryx, whereas IBM SPSS Statistics is most compared with IBM SPSS Modeler, TIBCO Statistica, Weka, MathWorks Matlab and Alteryx.

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