Are you aspiring to become a data scientist, but struggling to crack the interviews Well – you’re not alone! Getting a break in the data science field can be difficult. Doubly so, if you’re coming from a non-data science background (which in all likelihood you are). The stories you hear from other aspiring data scientists can make interviews feel more intimidating and daunting. So you better be prepared before facing the interviews. What kind of questions can be asked How can you prepare and what are the resources you should refer to What is the structure of a typical data science interview How should your body language be These are just some of the questions you’ll have in mind.
When would one use the geometric mean as opposed to arithmetic mean What is the use of the geometric mean in general
In the previous post about pricing optimization (link here), we discussed a little about linear demand and how to estimate optimal prices in that case. In this post we are going to compare three different types of demand models for homogeneous products and how to find optimal prices for each one of them.
As Bayesian models usually generate a lot of samples (iterations), one could want to plot them as well, instead (or along) the posterior “summary” (with indices like the 90% HDI). This can be done quite easily by extracting all the iterations in get_predicted from the psycho package.
I am not against SQL. Far from it, I love working with SQL and writing complex queries. The more you learn, the more you understand what can be done with SQL, and it’s incredibly powerful. But – there are definitely times when you think, “this would be a lot easier in R”.