So far in this book we have taken one topic or tool at a time and looked at how we could tackle a given data problem. Now, it is time to start bringing them together to develop a deeper understanding of the nature of data problems and methods, as well as extend our reach and skillset to address new problems that may emerge. There is, of course, no way we could cover all that you would encounter in real life, but we can certainly try to go through a few examples to see where you could take your data science skills.

Python is a simple-to-use yet powerful scripting language that allows one to solve data problems of varying scale and complexity. It is also the most used tool in data science and most frequently listed in data science job postings as the requirement. Python is a very friendly and easy-to-learn language, making it ideal for the beginner. At the same time, it is very powerful and extensible, making it suitable for advanced data science needs.

While there are many powerful programming languages that one could use for solving data science problems, people forget that one of the most powerful and simplest tools to use is right under their noses. And that is UNIX. The name may generate images of old-time hackers hacking away on monochrome terminals. Or, it may hearken the idea of UNIX as a mainframe system, taking up lots of space in some warehouse. But, while UNIX is indeed one of the oldest computing platforms, it is quite sophisticated and supremely capable of handling almost any kind of computational and data problem. In fact, in many respects, UNIX is leaps and bounds ahead of other operating systems; it can do things of which others can only dream!

So far, our work on data science problems has primarily involved applying statistical techniques to analyze the data and derive some conclusions or insights. But there are times when it is not as simple as that. Sometimes we want to learn something from that data and use that learning or knowledge to solve not only the current problem but also future data problems. We might want to look at shopping data at a grocery chain, combined with farming and poultry data, and learn how supply and demand are related. This would enable us to make recommendations for investments in both the grocery store and the food industries.

In the previous chapter, we saw how to learn from data when the labels or true values associated with them are available. In other words, we knew what was right or wrong and we used that information to build a regression or classification model that could then make predictions for new data. Such a process fell under supervised learning. Now, we will consider the other big area of machine learning where we do not know true labels or values with the given data, and yet we will want to learn the underlying structure of that data and be able to explain it. This is called unsupervised learning.

Why you care: Running A/A tests is a critical part of establishing trust in an experimentation platform. The idea is so useful because the tests fail many times in practice, which leads to re-evaluating assumptions and identifying bugs.

As discussed in Chapter 1, running trustworthy controlled experiments is the scientific gold standard in evaluating many (but not all) ideas and making data-informed decisions. What may be less clear is that making controlled experiments easy to run also accelerates innovation by decreasing the cost of trying new ideas, as the quotation from Moran shows above, and learning from them in a virtuous feedback loop. In this chapter, we focus on what it takes to build a robust and trustworthy experiment platform. We start by introducing experimentation maturity models that show the various phases an organization generally goes through when starting to do experiments, and then we dive into the technical details of building an experimentation platform.

Why you care: Understanding the ethics of experiments is critical for everyone, from leadership to engineers to product managers to data scientists; all should be informed and mindful of the ethical considerations. Controlled experiments, whether in technology, anthropology, psychology, sociology, or medicine, are conducted on actual people. Here are questions and concerns to consider when determining when to seek expert counsel regarding the ethics of your experiments.