Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.
$0
per month
SAS Enterprise Miner
Score 9.0 out of 10
N/A
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
N/A
Pricing
Anaconda
SAS Enterprise Miner
Editions & Modules
Free Tier
$0
per month
Starter Tier
$9
per month
Business Tier
$50
per month per user
Enterprise Tier
60.00+
per month per user
No answers on this topic
Offerings
Pricing Offerings
Anaconda
SAS Enterprise Miner
Free Trial
No
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Anaconda
SAS Enterprise Miner
Features
Anaconda
SAS Enterprise Miner
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Anaconda
9.3
Ratings
11% above category average
SAS Enterprise Miner
8.8
Ratings
5% above category average
Connect to Multiple Data Sources
9.80 Ratings
8.10 Ratings
Extend Existing Data Sources
8.00 Ratings
9.00 Ratings
Automatic Data Format Detection
9.70 Ratings
9.30 Ratings
MDM Integration
9.60 Ratings
9.00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Anaconda
8.5
Ratings
2% above category average
SAS Enterprise Miner
8.1
Ratings
3% below category average
Visualization
9.00 Ratings
7.10 Ratings
Interactive Data Analysis
8.00 Ratings
9.10 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Anaconda
9.0
Ratings
10% above category average
SAS Enterprise Miner
8.0
Ratings
2% below category average
Interactive Data Cleaning and Enrichment
8.80 Ratings
7.80 Ratings
Data Transformations
8.00 Ratings
8.20 Ratings
Data Encryption
9.70 Ratings
8.10 Ratings
Built-in Processors
9.60 Ratings
8.10 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Anaconda
9.2
Ratings
9% above category average
SAS Enterprise Miner
8.8
Ratings
5% above category average
Multiple Model Development Languages and Tools
9.00 Ratings
7.50 Ratings
Automated Machine Learning
8.90 Ratings
9.80 Ratings
Single platform for multiple model development
10.00 Ratings
8.50 Ratings
Self-Service Model Delivery
9.00 Ratings
9.20 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.
Installing packages is very easy with Anaconda. Anaconda comes with 'anaconda navigator', a terminal-like utility from which you can easily install R packages and python libraries.
Launching R and python IDEs as well as Jupyter notebooks from anaconda navigator is simple, and Anaconda makes it very easy to keep these packages up-to-date.
I really like the fact that if you don't want to install the full version of Anaconda, you can opt to install a lightweight version (called Miniconda) that includes less python libraries and only core conda. I've installed it when I didn't want to take up as much disk space as Anaconda requires, but it works just the same.
One of the major flaws is that the tool is basically an interface to SAS/STAT code. It generates code in the background and runs it. Because of that, some errors are warning might be a little difficult to understand for users who aren't proficient with SAS code.
R integration is nice but I would like to see the possibility to integrate even more statistical models different than SAS. That would allow for better performance optimization when really required.
The light client is java based and a little heavy on the OS. It would be nice to get a web-based version of the tool instead of the java one.
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
I have contacted SAS twice in the past year and they have been super responsive both times. They solved my problem. I am also registered for an in-person class next month and they called today to tell me that it will be an online-only session. They apologized for the change and registered me for the online version. Super helpful!
One of the main competitors to Anaconda can be Google products such as Colab. Colab gives you the flexibility to handle large datasets gives it an edge over Anaconda. But again, the ease of access and usability of Anaconda stacks up against Colab. Besides, Anaconda relies more on your machine which makes it safe to use.
For those that are used to the SAS ecosystem, SAS Enterprise Miner is a massive move in the right direction. It makes doing analytics much more enjoyable. It is more user-friendly than Spotfire or Kinesis and seems to produce better results overall. SAS Enterprise Miner seems to be written by analysts for analysts.
Positive impact - Multiple options for data presenting , visualizing and sharing. (Eg: R-Markdown).
Positive impact - Ease of access to build complex machine learning models. (I work in NLP, it has multiple built in models to analyze the various contexts).
Positive impact - Conda package let's to deal with external packages which can be used in Jupyter.
It has a positive ROI to our business, as our sales lead rate increased after we started recommending SAS EM.
Our business operation numbers improved after we introduced SAS EM and started using predictive analytics for our customer retention and customer chain prediction.
The statistical modelling for the risk controls in our financial department helped to reduce the related residual risk.
