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
Google Charts
Score 8.6 out of 10
N/A
Google Charts provides a way to visualize data on your website - for free. From simple line charts to complex hierarchical tree maps, the chart gallery provides a large number of ready-to-use chart types. The most common way to use Google Charts is with simple JavaScript that you embed in your web page.
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.
We can easily recommend Google Charts to any company that needs a way to visually represent their data. Another great thing about Google Charts is that it is free to use and does not require any membership fees. Although it requires a skilled used to be able to use the charts, the results are great and can be beneficial to any company who is looking to make better decisions.
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.
they're free with Google suite and they have backing in terms of powerful Google apps which can be plugged in to perform multiple actions like using Google sheets to import raw data into Google Charts
they're the most simple app to use when it comes to creating charts and visual dashboards
ease of customization
ease of using custom APIs from developers side to help make any types of charts and dashboards you want
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.
I would like a couple more introductory videos or a live chat option for when you run into an issue. I think this is a Google-wide problem, not only linked to Google Charts.
I have run into some issues with the Dynamic Data but also admittedly could potentially dive in deeper and investigate.
It would be great if Google Charts made it possible to integrate Google Chat into the platform.
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.
Google Charts is a good product. It's widely supported with deep documentation and a large community. But for me, it wasn't customizable enough. When we started with simple charts, it was great, but as we got deeper and more complex, our needs outgrew the library. If I was going forward, I would choose a more barebones library with more freedom and extensibility.
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.
Google Charts is about as easy to use as the rest of their applications. The UI is very well thought out, allowing you to add what you need, and customize it to your exact liking. The default theme is actually really nice, which helps as most of the time, customizing is not needed.
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.
As a free tool with massively powerful, infinitely customisable charts that can be dynamically updated - Google Charts is my favourite data visualisation tool. However, my hatred of JavaScript does jade my view on it. This is the price of the tool though, and I'm glad it's available for me.
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!
Google Charts stacks up better since it is free and does not have the constant pressure for cost overruns, add-ons, annual maintenance and implementation services. The speed of using Google Charts is quick, saving users potentially weeks in getting up and going. For the readers of websites with limited resources, the application shows up nicely is look and feel with charts. Great way of showing data visually.
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.
