Python in Excel or Risk Kit? Two paths, one goal With the integration of Python into Excel, Microsoft has sent a strong signal: bringing modern data science methods directly into the familiar spreadsheet. At first, this means maximum flexibility. Everything Python can do now also works in Excel. But every stochastic component has to be programmed, from drawing a normal distribution to the entire simulation loop with logging, statistics, and charts. That's interesting, but ultimately nothing new. You could already build any kind of simulation in Excel with VBA. Hardly anyone did it, though, because in practice it's simply too complicated and confusing. Risk Kit takes a different path: ● A No-Code solution by design. 99.9% of all applications can be mapped directly in Excel using Risk Kit functions. ● Only in extreme special cases is it worth taking the step to R or Python, and even then it works seamlessly as a Low-Code extension. ● The Monte Carlo engine handles the orchestration: sampling, iterations, correlations, evaluation, visualization. ● Result: readable, tested cell functions instead of scattered, untested code fragments. Conclusion Python or R are valuable for complex individual functions. As an overall solution, however, Python-in-Excel remains a High-Code world, just as VBA was in the past. For broad corporate practice, what's needed is a platform like Risk Kit that offers No-Code as the standard and only allows Low-Code where it's truly necessary.
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