Anaconda vs IBM SPSS

• Installing packages is very easy with Anaconda. Anaconda comes with 'anaconda navigator', a terminal-like utility from which you can easily install R packages and python libraries.
• Launching R and python IDEs as well as Jupyter notebooks from anaconda navigator is simple, and Anaconda makes it very easy to keep these packages up-to-date.
• I really like the fact that if you don't want to install the full version of Anaconda, you can opt to install a lightweight version (called Miniconda) that includes less python libraries and only core conda. I've installed it when I didn't want to take up as much disk space as Anaconda requires, but it works just the same.

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• Biggest advantage: allows us to save all of our data manipulation and analysis instructions (SPSS calls it "Syntax") so that we can understand what we did, say, one year from now. This can be critical, especially if you are conducting year-over-year comparisons, or re-analyzing data in a new way after the primary reporting has been completed. Pretty tough to understand what was all done, say, in an Excel spreadsheet.
• Has all of the analytic capabilities we have needed so far (well, except for Random Forest).
• We use the Tables module a ton to create most of our crosstab kind of results, and SPSS gives you a lot of flexibility to create different kinds of tables with the same set of data.
• I learned SPSS when only Syntax was available, but I really appreciate being able to browse through the menus, and then just paste the Syntax you need. It also comes in handy when you're just playing, trying out different ways to look at the data.

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• Although I have generally had positive experiences with Anaconda, I have had trouble installing specific python libraries. I tried to remedy the solution by updating other packages, but in the end, things got really messed up, and I ended up having to uninstall and reinstall a total of about 4 times over the past 2 years.
• If you have the free version of Anaconda, there is not much support. Googling questions and error messages are helpful, but there were times when I wished I would have been able to ask technical support to help me troubleshoot issues.
• There were a few times when I tried to install tensorflow and tensorboard via Anaconda on a PC, but I could not get them to install properly. Anaconda allows you to create 'environments' , which allow you to install specific versions of python and associated libraries. You can keep your environments separate so they do not conflict with one another. Anyway, I ended up having to create several 'conda envrionments' just so I could use tensforflow/tensorboard and a few other utilities to avoid errors. This was somewhat annoying, because every time I wanted to run a specific model, I'd have to open up the specific conda environment with the appropriate python libraries.

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• There are other great statistical software packages that are open source
• The simplicity and user-friendliness of SPSS comes with a functionality trade-off
• The functions of SPSS lack in terms of graphical capability which can be a major obstacle to a novel data analyst

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• Extremely quick turnaround time to set up data science experiments
• Reduction of troubleshooting time when deploying new packages and dependencies
• Low risk environment due to the community edition

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• In terms of simple analysis for internal reliability statistics (e.g. Cronbach's Alpha) SPSS was very helpful.
• SPSS has been found easier to use by some colleagues than Excel, (these colleagues are on the lower end of technological capability) and using SPSS alongside RStudio and Excel has given us a variety of methods to accomplish one task.
• The lack of readable user guides and online info hindered our ability to breach into the areas of internal validity testing that we were not familiar with. Updated guides are necessary.

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