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
Notepad++
Score 9.2 out of 10
N/A
Notepad++ is a popular free and open source text editor available under the GPL license, featuring syntax highlighting and folding, auto-complete, multi-document management, and ac customizable GUI.
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.
well suited for 1) Coding and Development - Writing and editing code, Quick prototyping and testing of code snippets, Debugging and inspecting code using syntax highlighting and line numbering, 2) web development - Creating and editing HTML, CSS, JavaScript, and other web-related files .Managing and organizing web projects with multiple files and directories. Not suited for - 1) processing huge files 2) graphic designing 3) complex gui designs 3) Data Analysis and Manipulation - Editing and cleaning up text-based data files before importing them into analytical tools. Applying regular expressions to extract, transform, and manipulate data. 4) System Administration and IT - change system configuration file
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.
Notepad++ allows us to keep open files in tabs. Like in a web browser, these tabs let us access these files quickly and easily. Furthermore, even if we forget to save the files when closing the program or shutting down the PC, Notepad++ retains them in the open tabs when we reopen it.
Notepad++ supports many different file types. We usually save our files created in Notepad as normal text files, but sometimes as JSON, PHP, and HTML files.
Notepad++ is lightweight and requires little resources. Using it is snappy and responsive.
The developer of Notepad++ frequently updates the software with bug fixes, performance improvements and new features.
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.
Sometimes the number of options are overwhelming and require a quick search to figure out where to locate a particular function.
Some way to do a diff between files would be great. Still need to resort to another paid app for that - unless it is a buried function I don't know about or there's a plugin for it.
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 haven't needed to utilize any support related to Notepad++. I guess this is a good thing because I found it to be quite intuitive. There are almost infinite features you can tweak and plugins you can download but I haven't had to do that because Notepad++ is really good right out of the box.
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!
Notepad for Windows, Microsoft Word...LibreOffice Writer....I have used all of these for code writing and editing. Once again I like the universal feel of Notepad++. Basic Notepad, is just that, basic...and kind of clunky for what it is. This is a cool that I have installed on all my computers and also keep it on a thumb drive if I need it elsewhere.
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.
