What users are saying about

Anaconda

Top Rated
33 Ratings

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Top Rated
47 Ratings

Anaconda

Top Rated
33 Ratings
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Score 8.8 out of 101

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Top Rated
47 Ratings
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Score 8.8 out of 101

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Likelihood to Recommend

Anaconda

Anaconda is great for academic and private organizations that cannot afford more expensive Python/R package managers. Also, it is more appropriate for intermediate to advanced Python users--Anaconda can be somewhat frustrating for beginners, as it takes some practice to get comfortable with the workflow. I find it particularly useful for working in teams, because if everyone uses the same package manager, it is easier to troubleshoot issues and makes for reproducible research. For wealthier organizations, a premium package management system (with tech support) would be ideal. Anaconda is also great for people working independently on code development.
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RStudio

Well suited for spatial data analysis, statistical analyses, plotting and working with collaborators through GitHub. It can also compile Latex files and supports Rmarkdown, a markup language similar to Latex. Packages are constantly being added, so it's great for using novel analytical techniques that may not be available elsewhere.Not as well suited for any big data tasks or deep learning or image processing.
Maike Holthuijzen profile photo

Pros

  • Anaconda has iPython- Notebook that facilitates code writing in Python
  • It's very easy to install tour preferred the Python version
  • The risk of messing up the libraries is completely eliminated
Mauricio Quiroga-Pascal Ortega profile photo
  • Performing Statistical Analysis is very efficient. With a lot of open source packages available in R programming, data analysis becomes very easy.
  • Publishing web applications and deploying predictive data models is very easy if you have R Server in your firm using Shiny. It can handle large sets of data.
  • Writing data science algorithms like Clustering, Classification and Apriori Analysis is very efficient. The open source nature of this programming language allows everyone to contribute packages to the environment.
Kunal Sonalkar profile photo

Cons

  • It's hard to get security updates when you leave the system packages
  • There're some PyPI packages that Anaconda doesn't have. This obligates the user to package it by herself or using pip
  • Anaconda isn't as fast as PyPI publications
Mauricio Quiroga-Pascal Ortega profile photo
  • There are some packages in RStudio which aren't very well known hence its very difficult to get help if you get stuck using them.
  • If the dataset size crosses 20 million rows, then you need extremely high RAM otherwise the processing gets very slow. So in such a case R Server is a must. Cloud storage can be a good alternative though.
  • The graphs which are plotted in the console aren't very intuitive and labels, colors, axis, etc have to be manually written to make the visuals look more appeasing.
Kunal Sonalkar profile photo

Likelihood to Renew

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RStudio10.0
Based on 1 answer
Rstudio is my go - to language for anything involving analysis. It has far more capabilities for data analysis than Python (in my opinion). New packages for novel analytical techniques are often published. Rstudio continues to implement great updates every few months as well. Finally, I really like that I can write LateX documents in Rstudio and integrate R code into them.
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Usability

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No answers yet
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RStudio8.0
Based on 1 answer
Because it is very straightforward. Sometimes, however, customizing the rendering engine, PanDoc, is difficult.
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Alternatives Considered

ANACONDA VS Alteryx Analytics: Even though I find Alteryx to be an excellent tool for managing extremely massive data, Anaconda is much better and easy for analytics.Anaconda VS. MicroStrategy Analytics: Compared with Anaconda, MicroStrategy Analytics is very difficult to use and counter-intuitiveAnaconda VS. Power BI For Office 365: One of the main advantages of BI for Office 364 is its capacity to data connectivity. However, it's very hard to edit data connections, once BI for Office is deployed in other platforms
Mauricio Quiroga-Pascal Ortega profile photo
  • Statistics is very simple to use in RStudio and not in Tableau.
  • Tableau is very expensive and RStudio is open source.
  • Geo Coding in R is much better than Tableau.
  • Tableau is better in data visualization and easy to use and much more interactive.( as it is more of a drag and drop tool).
Kunal Sonalkar profile photo

Return on Investment

  • Centralized repository for all notebooks and analysis projects.
  • Solved for some security concerns from IT.
  • Saves money by avoiding ad-hoc computer resources for analysts.
No photo available
  • Positive impact is when you automate excel reports using Shiny applications, it ends up saving a lot of time and money.
  • It's easy to catch on so with a little training and sound math background you can start coding right away.
  • Its compatibility with other platforms like SQL databases, Salesforce, Tableau , etc is amazing and makes it worth the investment. It doesn't have any negatives as such.
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Pricing Details

Anaconda

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

RStudio

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details