Anaconda vs IBM SPSS

• Clear install story. There are a lot of ways to install python. There's only one way to install anaconda. This makes teaching and standardizing much easier.
• Batteries included. It's easy to install things in python, but anaconda ships with most of what you need out of the box. This helps with standardization and reproducibility.
• Good integrations with Jupyter and other visual tools. Jupyter is really convenient when learning various python packages. Anaconda makes these tools easy to launch and to use.

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• SPSS has been around for quite a while and has amassed a large suite of functionality. One of its longest-running features is the ability to automate SPSS via scripting, AKA "syntax." There is a very large community of practice on the internet who can help newbies to quickly scale up their automation abilities with SPSS. And SPSS allows users to save syntax scripting directly from GUI wizards and configuration windows, which can be a real life-saver if one is not an experienced coder.
• Many statistics package users are doing scientific research with an eye to publish reproducible results. SPSS allows you to save datasets and syntax scripting in a common format, facilitating attempts by peer reviewers and other researchers to quickly and easily attempt to reproduce your results. It's very portable!
• SPSS has both legacy and modern visualization suites baked into the base software, giving users an easily mountable learning curve when it comes to outputting charts and graphs. It's very easy to start with a canned look and feel of an exported chart, and then you can tweak a saved copy to change just about everything, from colors, legends, and axis scaling, to orientation, labels, and grid lines. And when you've got a chart or graph set up the way you like, you can export it as an image file, or create a template syntax to apply to new visualizations going forward.
• SPSS makes it easy for even beginner-level users to create statistical coding fields to support multidimensional analysis, ensuring that you never need to destructively modify your dataset.
• In closing, SPSS's long and successful tenure ensures that just about any question a new user may have about it can be answered with a modicum of Google-fu. There are even several fully-fledged tutorial websites out there for newbie perusal.

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• Although some other users mentioned the installation is "simple", we did encounter some challenge in a highly controlled environment (due to security reasons).
• Jupyter Notebook is extremely slow when the client/server side of the network's speed/bandwidth is not balanced.
• Bootstrapping Anaconda takes too long, sometimes I even started doubting it would respond any more.
• If there are extra python packages you need but are not by default installed by Anaconda, then some efforts will be required to figure out how to put them in the right place.

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• SPSS has for a long time touted it's "new and improved" graphing capabilities, and while they are that, they still have flexibility issues (or I just haven't found how to correctly use the software). For instance, why are data labels in a bar chart restricted to appearing only within the bar itself? Sure, you can have them at the bottom, middle, or near the top of the bar, but why not, say, just above the bar?
• When dealing with qualitative data, say comments from survey respondents, occasionally we run into characters likely typed by the respondent, which SPSS doesn't know how to handle, so it converts it to a space. It took me a while to figure out that it wasn't a space at all, and was wondering why my Syntax wasn't working (still assuming it was a space). It would help if SPSS could convert unknown characters to something more obvious.
• Biggest headache using SPSS is dealing with the IBM hierarchy when needing customer support or even upgrading to the new version (which comes out annually). In order to upgrade to v26 in April 2019, I eventually found out I needed not just an Authorization Code, nor a Full License Code, but also a Lock Code and spent way too many hours attempting to straighten this out with IBM customer support. I handle purchasing the upgrades too, and that task is not straightforward either. I miss the SPSS support before they were acquired by IBM.

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• We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
• We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.

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• IBM SPSS has allowed me to quickly analyze data for research.
• IBM SPSS has allowed me to complete analyses in order to submit research findings to conferences and complete manuscripts.
• IBM SPSS has enabled me to meet research objectives set out in grant proposals.

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