Anaconda is an enterprise Python platform that provides access to open-source Python and R packages used in AI, data science, and machine learning. These enterprise-grade solutions are used by corporate, research, and academic institutions for competitive advantage and research.
$0
per month
ArcGIS
Score 8.0 out of 10
N/A
Esri in Redlands, California offers ArcGIS, a geographic information system.
$100
per year
Pricing
Anaconda
ArcGIS
Editions & Modules
Free Tier
$0
per month
Starter Tier
$15
per month per user
Business
$50
per month per user
Custom
Contact Sales
Viewer
$100
per year
ArcGIS for Personal Use
$100
per year
ArcGIS for Student Use
$100
per year
Editor
$200
per year
Field Worker
$350
per year
Creator
$500
per year
GIS Professional Basic
$700
per year
GIS Professional Basic
2,750
per year
GIS Professional Advanced
3,800
per year
Offerings
Pricing Offerings
Anaconda
ArcGIS
Free Trial
No
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
Users within organizations with 200+ employees/contractors (including Affiliates) require a paid Business license. Academic and non-profit research institutions may qualify for exemptions.
—
More Pricing Information
Community Pulse
Anaconda
ArcGIS
Features
Anaconda
ArcGIS
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Anaconda
9.3
25 Ratings
11% above category average
ArcGIS
-
Ratings
Connect to Multiple Data Sources
9.822 Ratings
00 Ratings
Extend Existing Data Sources
8.024 Ratings
00 Ratings
Automatic Data Format Detection
9.721 Ratings
00 Ratings
MDM Integration
9.614 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Anaconda
8.5
25 Ratings
1% above category average
ArcGIS
-
Ratings
Visualization
9.025 Ratings
00 Ratings
Interactive Data Analysis
8.024 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Anaconda
9.0
26 Ratings
10% above category average
ArcGIS
-
Ratings
Interactive Data Cleaning and Enrichment
8.823 Ratings
00 Ratings
Data Transformations
8.026 Ratings
00 Ratings
Data Encryption
9.719 Ratings
00 Ratings
Built-in Processors
9.620 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Anaconda
9.2
24 Ratings
9% above category average
ArcGIS
-
Ratings
Multiple Model Development Languages and Tools
9.023 Ratings
00 Ratings
Automated Machine Learning
8.921 Ratings
00 Ratings
Single platform for multiple model development
10.024 Ratings
00 Ratings
Self-Service Model Delivery
9.019 Ratings
00 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.
I'm very grateful to be able to use it, and I have a master's degree with a focus in Geospatial Analysis. There can be a bit of a learning curve, and I try to build user-friendly ways for volunteers to see & collect data. Meanwhile, if a colleague is less confident with building such a system, it may be more difficult for them to implement.
Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.
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.
Simply because the program deserves it. It seems to me that it is a fundamental tool for the storage, analysis, and interpretation of medium and large-scale phenomena, unmanageable with traditional engineering software. Its versatility in the handling of the different "layers" with which the data is handled and interpolation tools, make this software a powerful ally both for companies and for the educational part of the universities.
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.
Once set up, the tools are extremely easy to use. I had a staff member develop a tool for field data collection, that included an external and internal dashboards to monitor progress in days. The field workers that collected the data, barely knew how to use a computer, and within minutes they could use the application that was configured for them.
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.
Unlike other platforms (ex: EMSI), there is no "help desk" new users can easily call into for troubleshooting or errors, and so you have to spend LOTS of time trying workarounds. This is also because the help center blog posts are usually pretty confusing, and many times do not include images or videos to help you along. Any such changes would be immensely useful!
I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!
My students love the "drop" feature in Google Maps, but besides that it truly doesn't compare. I love that you can add, delete, or change layers to this map to better understand its larger affect. There are many more ways to manipulate maps on ArcGIS than on Google Maps. I can also add personal details and information if I want to create a specific map, something that I am unable to do with Google
It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.