Anaconda vs. Microsoft Excel vs. Posit

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.

Incentivized

I don't really know another program as powerful as Excel. I've used Google Doc programs but do not feel they come close. So far, anytime I've needed a table of some sort for data, whether it's budget oriented or information off a survey, the best system has been Excel. We do web audits on occasion and we create an Excel worksheet featuring every URL of the pages we're auditing, notes, data about the content, information about files attached to the page and other information to help us determine what pages need updating, deleting or otherwise. We also use Excel primarily to export our Google Analytics to in order for us to create reports for clients that need to see specific information about their traffic.

Incentivized

In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.

Incentivized

• 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.

Incentivized

• It is very good at embedded formulas and tying cells to one another
• It allows me to compare deals terms on a side-by-side basis and talk my clients through it easily.
• It is very helpful as well in terms of allowing me to filter/sort results in many different ways depending on what specific information I am most interested in prioritizing.

Incentivized

• The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
• The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
• Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

Incentivized

• It can have a cloud interface to store the work.
• Compatible for large size files.
• 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.

Incentivized

• Excel offers collaboration features that allow multiple users to work on the same spreadsheet, but managing changes made by different users can be challenging. Excel could improve its features by offering more granular control, better tracking of changes, and more robust conflict resolution tools.
• Itcan be a barrier to productivity when importing and exporting data from other applications or file formats. To improve its features, it should offer better support for standard file formats and more robust error handling and reporting tools.
• Excel can be challenging for finance students and working professionals, but it can be improved by offering more robust tutorials, better documentation, and more user communities and support forums.

Incentivized

• Python integration is newer and still can be rough, especially with when using virtual environments.
• RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
• Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.

Incentivized

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.

Incentivized

Excel remains the industry standard for spreadsheets and has maintained simple and straight-forward formula writing methods. Although there is a learning curve to do more complex calculations, there are countless help sites and videos on the Internet for almost any need.

Incentivized

There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.

Incentivized

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.

Incentivized

I'm giving it a 7 because it is my go to. But the fact other prefer Google Sheets when working with a team does get irritating. I've used the online version of Microsoft Excel that other teams can get into and it still seems behind Google Sheets. It's a little clanky and slow? If that's even a term.

Incentivized

For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when

Incentivized

I have rarely, if ever, had issues with its availability.

Incentivized

RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses

Incentivized

Excel load and performs calculations immediately. It integrate well with all Microsoft Office applications and does not slow them down.

Incentivized

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.

Incentivized

I have not had to use it often, but it is good.

Incentivized

Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.

Incentivized

We did it at the individual level: anyone willing to code in R can use it. No real deployment involved.

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!

Incentivized

Out of Microsoft Excel, Microsoft Power BI, IBM SPSS, and Google Sheets, Microsoft Excel is by far the most common tool used for anything data-related across organizations. Accordingly, our organization has also implemented Microsoft Excel as a first-step tool. We recently adopted Microsoft Power BI (the free version), and use it occasionally (mostly for creating dashboards), but it is less commonly understood by stakeholders across our organization and by our clients. Accordingly, Microsoft Excel is more user-friendly and because of its popularity, we can easily look up how to do things in the program online. Google Sheets is a comparable alternative to Microsoft Excel, but because it's cloud-based and we have sensitive data that needs to be protected, we chose against using this software. Finally, a few users (including myself) have access to and utilize IBM's SPSS. For my role, it's a helpful tool to do more rigorous analyses. However, because of its cost and limited functionality as a simple spreadsheet, we only use it for more complex analyses.

Incentivized

RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.

Incentivized

N/A I was not involved in this process.

Incentivized

Each user can use it to whatever level of expertise they have. It remains the same so users can contribute to another's work regardless of whether they have more or less expertise

Incentivized

RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.

Incentivized

• 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.

Incentivized

• Excel has positively impacted the business as it has increased our efficiency.
• It also saves us the time that we would have spent on making the calculations that it does for us.
• Since it works on all devices and is compatible with both Windows and Mac, we do not have to invest in any other alternative.

• Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
• Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
• What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).

Incentivized