While you can create publication-ready charts, the default outputs look like they are from 2005: gray backgrounds, basic colors, and non-intuitive editing. Compare this to the beautiful, interactive ggplot2 outputs from R or Python’s Seaborn. You will likely export SPSS data to another tool for final visualizations.
SPSS chokes on datasets over a few hundred thousand rows. It has basic machine learning (decision trees, neural nets, random forests in the add-on modules), but nothing like XGBoost, TensorFlow, or even scikit-learn. For deep learning or distributed computing (Hadoop/Spark), look elsewhere. ibm spss
SPSS is old (first released in 1968) and battle-tested. The core statistical routines (t-tests, regressions, factor analysis, GLM) are validated and produce results consistent with academic publication standards. For regulatory fields (e.g., clinical trials), this trustworthiness is non-negotiable. While you can create publication-ready charts, the default