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
MATLAB
Score 9.0 out of 10
N/A
MatLab is a predictive analytics and computing platform based on a proprietary programming language. MatLab is used across industry and academia.
$49
per student license
Pricing
Anaconda
MATLAB
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
Student
$49
per student license
Home
$149
perpetual license
Education
$250
per year
Education
$500
perpetual license
Standard
$860
per year
Standard
2,150
perpetual license
Offerings
Pricing Offerings
Anaconda
MATLAB
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
—
—
More Pricing Information
Community Pulse
Anaconda
MATLAB
Features
Anaconda
MATLAB
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Anaconda
9.3
Ratings
11% above category average
MATLAB
-
Ratings
Connect to Multiple Data Sources
9.80 Ratings
00 Ratings
Extend Existing Data Sources
8.00 Ratings
00 Ratings
Automatic Data Format Detection
9.70 Ratings
00 Ratings
MDM Integration
9.60 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Anaconda
8.5
Ratings
2% above category average
MATLAB
-
Ratings
Visualization
9.00 Ratings
00 Ratings
Interactive Data Analysis
8.00 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Anaconda
9.0
Ratings
10% above category average
MATLAB
-
Ratings
Interactive Data Cleaning and Enrichment
8.80 Ratings
00 Ratings
Data Transformations
8.00 Ratings
00 Ratings
Data Encryption
9.70 Ratings
00 Ratings
Built-in Processors
9.60 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Anaconda
9.2
Ratings
9% above category average
MATLAB
-
Ratings
Multiple Model Development Languages and Tools
9.00 Ratings
00 Ratings
Automated Machine Learning
8.90 Ratings
00 Ratings
Single platform for multiple model development
10.00 Ratings
00 Ratings
Self-Service Model Delivery
9.00 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.
Engineering, mathematical modeling, and machine learning are all fields where MATLAB will shine. It's fast, reliable, and relatively easy to use. MATLAB is the de facto standard when it comes to producing high-quality plots. If you need to deal with large data sets, and not take forever processing them, MATLAB may very well be the tool for you!
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.
Has robust and easy-to-use debugging tools that can help one identify problems in one's codes.
Rich, well-developed and efficient library of mathematical and statistical functions that one might need to develop models or perform statistical analysis.
A very active online user community that is a great resource in terms of seeking help when you hit a snag.
Great help literature (and sometimes videos too) on all tools making it possible for all to train themselves.
MATLAB should have a full free version (without time limit) in order to be more accessible and thus have a greater user community.
The idea of having toolboxes to work directly with hardware (microcontrollers, single-board computers) is great, but one can tell it isn't updated very frequently and there isn't as much documentation available as with more common resources.
Our organization had a lot of trouble getting our network licenses to work properly and there wasn't any local service provider that could help us get it to work faster.
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.
The best thing about MATLAB is the variety of research and development fields it supports. The reason for this rating is that it is best used for medical images enhancement and signal processing, it is also used for speech to text conversion. This tool server is best when the demand is for machine learning.
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.
The built-in search engine is not as performing as I wish it would be. However, the YouTube channel has a vast library of informative video that can help understanding the software. Also, many other software have a nice bridge into MATLAB, which makes it very versatile. Overall, the support for MATLAB is good.
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.
The commands and coding language of MATLAB reads a lot more in plain English as opposed to all the periods and other special characters that are needed when typing in Python or Java. Additionally MATLAB has several different function packages that can solve all different categories of problems so you don't have to make a bunch of different code scrips from scratch.
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.