SAS Enterprise Miner Features
What are the features of SAS Enterprise Miner?
Statistical Tool
- Scripting
- Data Mining
- Algorithms
Data Analysis
- Analysis
- Data Interaction
Decision Making
- Modeling
- Data Visualizations
- Report Generation
- Data Unification
Top Rated SAS Enterprise Miner Alternatives
(8)
4.6 out of 5
Visit Website
Sponsored
SAS Enterprise Miner Categories on G2
Filter for Features
Statistical Tool
Scripting | Supports a variety of scripting environments 86 reviewers of SAS Enterprise Miner have provided feedback on this feature. | 81% (Based on 86 reviews) | |
Data Mining | As reported in 93 SAS Enterprise Miner reviews. Mines data from databases and prepares data for analysis | 83% (Based on 93 reviews) | |
Algorithms | Applies statistical algorithms to selected data 89 reviewers of SAS Enterprise Miner have provided feedback on this feature. | 80% (Based on 89 reviews) |
Data Analysis
Analysis | Based on 103 SAS Enterprise Miner reviews. Analyzes both structured and unstructured data | 91% (Based on 103 reviews) | |
Data Interaction | Interacts with data to prepare it for visualizations and models 103 reviewers of SAS Enterprise Miner have provided feedback on this feature. | 84% (Based on 103 reviews) |
Decision Making
Modeling | Based on 97 SAS Enterprise Miner reviews. Offers modeling capabilities | 86% (Based on 97 reviews) | |
Data Visualizations | Creates data visualizations or graphs 103 reviewers of SAS Enterprise Miner have provided feedback on this feature. | 72% (Based on 103 reviews) | |
Report Generation | Generates reports of data performance This feature was mentioned in 100 SAS Enterprise Miner reviews. | 80% (Based on 100 reviews) | |
Data Unification | Based on 17 SAS Enterprise Miner reviews. Unifies information on a singular platform | 78% (Based on 17 reviews) |
Model Development
Language Support | Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript | Not enough data | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models | Not enough data | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development | Not enough data | |
Model Training | Supplies large data sets for training individual models | Not enough data | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development | Not enough data | |
Model Training | Supplies large data sets for training individual models | Not enough data | |
Feature Engineering | Transforms raw data into features that better represent the underlying problem to the predictive models | Not enough data |
Machine/Deep Learning Services
Computer Vision | Offers image recognition services | Not enough data | |
Natural Language Processing | Offers natural language processing services | Not enough data | |
Natural Language Generation | Offers natural language generation services | Not enough data | |
Artificial Neural Networks | Offers artificial neural networks for users | Not enough data | |
Computer Vision | Offers image recognition services | Not enough data | |
Natural Language Understanding | Offers natural language understanding services | Not enough data | |
Natural Language Generation | Offers natural language generation services | Not enough data | |
Deep Learning | Provides deep learning capabilities | Not enough data |
Deployment
Managed Service | Manages the intelligent application for the user, reducing the need of infrastructure | Not enough data | |
Application | Allows users to insert machine learning into operating applications | Not enough data | |
Scalability | Provides easily scaled machine learning applications and infrastructure | Not enough data | |
Managed Service | Manages the intelligent application for the user, reducing the need of infrastructure | Not enough data | |
Application | Allows users to insert machine learning into operating applications | Not enough data | |
Scalability | Provides easily scaled machine learning applications and infrastructure | Not enough data |
Administration
Quality Control | Data quality consists of deduplication, cleansing, and appending your marketing database. | Not enough data | |
Data Sampling | Allows users to select samples of data for defined procedures. | Not enough data | |
Collaboration | Share data across your organization. | Not enough data |
Capabilities
Data Visualization | Communicate complex information clearly and effectively through advanced graphical techniques. | Not enough data | |
Survival Analysis | Supports evaluation of durations, events, and reliability in relation to statistical analysis | Not enough data | |
Risk Data Attributes | Identify risk data attributes such as description, category, owner, or hierarchy. | Not enough data | |
Cost Analysis | Tools to analyze financial data in order to gain usable insights. | Not enough data |
Methodology
ANOVA Support | Supports analysis of variance (ANOVA) to determine observed variance. | Not enough data | |
Regression | Supports various regression methods such as ordinary least squares (OLS), weighted least squares (WLS), or generalized linear model (GLM). | Not enough data | |
Time Series Analysis | Supports the analysis of time series data for predictive analytics and exploratory analysis. | Not enough data |
System
Data Ingestion & Wrangling | Gives user ability to import a variety of data sources for immediate use | Not enough data | |
Language Support | Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript | Not enough data | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models | Not enough data |
