#1 most frequent
United States of America
28.6%94 installations of 329
What is IBM SPSS Modeler?
IBM SPSS Modeler is a visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations can use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets.
Categories & Use Cases
Media
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Screenshot of Use a single run to test multiple modeling methods, compare results and select which model to deploy. Quickly choose the best performing algorithm based on model performance.
Top Performing Features
Connect to Multiple Data Sources
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
Category average: 8.8
Extend Existing Data Sources
Use R or Python to create custom connectors for any APIs or databases
Category average: 8.9
Areas for Improvement
Who Buys & Uses IBM SPSS Modeler
#2 most frequent
Colombia
10.3%34 installations of 329
#3 most frequent
Turkey
5.2%17 installations of 329
SPSS Modeler Review
Use Cases and Deployment Scope
IBM SPSS Modeler provides us with a no-code data science and predictive analytics platform for developing and deploying machine learning models. It give us the ability to model out a idea to test the hypotheses.
Pros
- Predictive Modeling
- Data Preparation
- Text Mining
Cons
- User Interface could improve
- More statt capablities
Return on Investment
- User Interface
- No Code / Low Code
Usability
Alternatives Considered
SAS Viya
Other Software Used
Red Hat OpenShift, IBM Cloud Pak for Data
IBM SPSS Modeler a Statistical Tool for the New Researcher
Use Cases and Deployment Scope
IBM SPSS Modeler is used mainly in the world of social sciences and psychology and other research. This product is user friendly and a new user can use for statistical analyses without much practice. This tool provides a range of statistical techniques (descriptive statistics, regression and other tests) and can be used for a variety of data sets with good outcomes. You can also use with Microsoft Excel which makes it very practical. There are good support links available to assist in the use of this tool. The only negative aspects of this tool are cost, and it may not be as flexible as some programs.
Pros
- Wide range of statistical techniques
- Good tables and charts
- Integrates with Excel
Cons
- Appears slow with very large data sets
- Lack of frequent updates
- cost reduction
Return on Investment
- Good results for shared research project data with others
Usability
Alternatives Considered
Python IDLE
SPSS Modeler is a great addition to Data Science toolkit, faster results than coding
Pros
- Fast code builder.
- No need to maintain software versioning.
- Visual code, easy to see what's happening.
Cons
- Details of models and nodes requires some "digging", "clicking".
- Customization of versions is hard to implement.
- Latest open source code packages take time to integrate into the application.
Return on Investment
- Several projects completed quickly and without substantial coding.
- Saves some time by allowing younger experienced employees to jump right into data science.
- Comes with the package for Cloud Pak for Data, so its available that way as a toolkit, saves money.
Other Software Used
Jupyter Notebook, TIBCO Data Science (including Team Studio and Statistica), RStudio
IBM SPSS Modeler is My Choice
Pros
- Combine text and data
- Provide facilities for all phases of the data mining process.
- Use a node and stream paradigm to easily and quickly create models.
Cons
- The graphics are weak.
Most Important Features
- Range of algorithms
- Easy development environment
- Ability to customize
Return on Investment
- Quick development.
- Robust environment to handle the largest dat challenges.
- Ability to incorporate freeform text.
Alternatives Considered
RapidMiner Studio and Dataminr Pulse
Other Software Used
RapidMiner Studio, Weka.IO, Python IDLE
Appreciation of Stats: Recommendation for Consumers and Producers
Pros
- Inputting data
- Test selection
- Data manipulation
- Providing sandbox data sets
- Two week trial for students considering purchase
- Reasonable rent prices
Cons
- Including MCAR in basic packages
- Standardise language across dialogue windows
- Enable 'undo' after sorting cases
- Include Bayesian options
Return on Investment
- I am able to study and work from home sustainably
- I can help others have a high quality university education experience to graduate confident and competent to meet gaps in the wider community
- Market research for my business
- Help other small businesses to create viable and high quality products and services
- Contribute to research projects: ethical, high quality data analyses and interpretation
