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
GitHub
Score 9.2 out of 10
N/A
GitHub is a platform that hosts public and private code and provides software development and collaboration tools. Features include version control, issue tracking, code review, team management, syntax highlighting, etc. Personal plans ($0-50), Organizational plans ($0-200), and Enterprise plans are available.
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 …
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.
GitHub is an easy to go tool when it comes to Version Controlling, CI/CD workflows, Integration with third party softwares. It's effective for any level of CI/CD implementation you would like to. Also the the cost of product is also very competitive and affordable. As of now GitHub lacks capabilities when it comes to detailed project management in comparison to tools like Jira, but overall its value for money.
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.
Version control: GitHub provides a powerful and flexible Git-based version control system that allows teams to track changes to their code over time, collaborate on code with others, and maintain a history of their work.
Code review: GitHub's pull request system enables teams to review code changes, discuss suggestions and merge changes in a central location. This makes it easier to catch bugs and ensure that code quality remains high.
Collaboration: GitHub provides a variety of collaboration tools to help teams work together effectively, including issue tracking, project management, and wikis.
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.
Not an easy tool for beginners. Prior command-line experience is expected to get started with GitHub efficiently.
Unlike other source control platforms GitHub is a little confusing. With no proper GUI tool its hard to understand the source code version/history.
Working with larger files can be tricky. For file sizes above 100MB, GitHub expects the developer to use different commands (lfs).
While using the web version of GitHub, it has some restrictions on the number of files that can be uploaded at once. Recommended action is to use the command-line utility to add and push files into the repository.
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.
GitHub's ease of use and continued investment into the Developer Experience have made it the de facto tool for our engineers to manage software changes. With new features that continue to come out, we have been able to consolidate several other SaaS solutions and reduce the number of tools required for each engineer to perform their job responsibilities.
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.
GitHub is a clean and modern interface. The underlying integrations make it smooth to couple tasks, projects, pull requests and other business functions together. The insights and reporting is really strong and is getting better with every release. GitHub's PR tooling is strong for being web based, i do believe a better code editor would rival having to pull merge conflicts into local IDE.
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.
There are a ton of resources and tutorials for GitHub online. The sheer number of people who use GitHub ensures that someone has the exact answer you are looking for. The docs on GitHub itself are very thorough as well. You will often find an official doc along with the hundreds of independent tutorials that answers your question, which is unusual for most online services.
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!
While I don't have very much experience with these 2 solutions, they're two of the most popular alternatives to GitHub. Bitbucket is from Atlassian, which may make sense for a team that is already using other Atlassian tools like Jira, Confluence, and Trello, as their integration will likely be much tighter. Gitlab on the other hand has a reputation as a very capable GitHub replacement with some features that are not available on GitHub like firewall tools.
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.
Team collaboration significantly improved as everything is clearly logged and maintained.
Maintaining a good overview of items will be delivered wrt the roadmap for example.
Knowledge management and tracking. Over time a lot of tickets, issues and comments are logged. GitHub is a great asset to go back and review why x was y.
