Data types are one of those things that you don’t tend to care about until you get an error or some unexpected results. It is also one of the first things you should check once you load a new data into pandas for further analysis.
In this short tutorial, Chris shows how to the pandasdtypes map to the numpy and base Python data types.
Moreover, Chris demonstrates how to handle and convert data types so you can speed up your data analysis. Both using custom functions and anonymous lambda functions.
A very handy guide indeed, after which you will be able to read in your datasets into Python in the right format from the get-go!
How do scurvy, astronomy, alchemy and data science relate to each other?
In this goto conference presentation, Lucas Vermeer — Director of Experimentation at Booking.com — uses some amazing storytelling to demonstrate how the value of data (science) is largely by organizations capability to gather the right data — the data they actually need.
It’s a definite recommendation to watch for data scientists and data science leaders out there.
Here are the slides, and they contain some great oneliners:
Owen argues that the probability distributions relate to each other in intuitive and interesting ways that makes it easier for you to recall them. For instance, several follow naturally from the Bernoulli distribution. Having this map by hand should thus help you really understand what these distributions imply.
On top of that, it’s just a nice geeky network poster!
Now, Sean didn’t just make a fancy map. In the original blog he also explains each of the distributions and how it relates to the others. Having this knowledge is vital to being a good data scientist / analyst.
You can sometimes get away with simple analysis using R or scikit-learn without quite understanding distributions, just like you can manage a Java program without understanding hash functions. But it would soon end in tears, bugs, bogus results, or worse: sighs and eye-rolling from stats majors.
For instance, here’s Sean explaining the Binomial distribution:
The binomial distribution may be thought of as the sum of outcomes of things that follow a Bernoulli distribution. Toss a fair coin 20 times; how many times does it come up heads? This count is an outcome that follows the binomial distribution. Its parameters are n, the number of trials, and p, the probability of a “success” (here: heads, or 1). Each flip is a Bernoulli-distributed outcome, or trial. Reach for the binomial distribution when counting the number of successes in things that act like a coin flip, where each flip is independent and has the same probability of success.
Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian networks aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph. Through these relationships, one can efficiently conduct inference on the random variables in the graph through the use of factors.
As Bayes nets represent data as a probabilistic graph, it is very easy to use that structure to simulate new data that demonstrate the realistic patterns of the underlying causal system. Daniel’s post shows how to do this with bnlearn.
New data is simulated from a Bayes net (see above) by first sampling from each of the root nodes, in this case sex. Then followed by the children conditional on their parent(s) (e.g. sport | sex and hg | sex) until data for all nodes has been drawn. The numbers on the nodes below indicate the sequence in which the data is simulated, noting that rcc is the terminal node.
The original and simulated datasets are compared in a couple of ways 1) observing the distributions of the variables 2) comparing the output from various models and 3) comparing conditional probability queries. The third test is more of a sanity check. If the data is generated from the original Bayes net then a new one fit on the simulated data should be approximately the same. The more rows we generate the closer the parameters will be to the original values.
As you can see, a Bayesian network allows you to generate data that looks, feels, and behaves a lot like the data on which you based your network on in the first place.
This can be super useful if you want to generate a synthetic / fake / artificial dataset without sharing personal or sensitive data.
Moreover, the underlying Bayesian net can be very useful to compute missing values. In Daniel’s example, he left out some values on purpose (pretending they were missing) and imputed them with the Bayes net. He found that the imputed values for the missing data points were quite close to the original ones:
In the original blog, Daniel goes on to show how to further check the integrity of the simulated data using statistical models and shares all his code so you can try this out yourself. Please do give his website a visit as Daniel has many more interesting statistics blogs!