There are 2 core aspects to fruitful application of statistics (data science):

- Domain knowledge.
- Statistical methodology.

Due to the highly specific nature of this field, it is difficult for any book or article to convey both a detailed and accurate description of the interplay between the two. In general, one can read material of two types:

- Broad info on statistical methods with conclusions that generalise but are not specific.
- Detailed statistical methods with conclusions that are useful only in a specific domain.

After 3 years working on my own data science projects and 3.5 years manipulating data on the trading floor, there is an additional category of learnings. It is fundamentally just as useful as the above and I take them into **every** project/side hustle/consulting gig…

**Practical Statistical Reasoning**

I made that term up because I don’t really know what to call this category. However, it covers:

- The nature and objective of applied statistics/data science.
- Principles common to all applications
- Practical steps/questions for better conclusions

If you have experience of the application of statistical methods, I encourage you to use your experience to illuminate and criticise the following principles. If you have never tried implementing a statistical model, have a go and then return. Don’t see the following as a list to memorise. You’ll get peak synthesis of information if you can relate to your own experience.

The following principles have helped me become more efficient with my analyses and clearer in my conclusions. I hope you can find value in them too.

#machine-learning #data-science #statistics #programming #data

1.55 GEEK