In this chapter, we talked about what A/B tests are and what are the challenges surrounding them. We went into some examples of how you actually measure the effects of variance using the t-statistic and p-value metrics, and we got into coding and measuring t-tests using Python. We then went on to discuss the short-term nature of an A/B test and its limitations, such as novelty effects or seasonal effects.

That also wraps up our time in this book. Congratulations for making it this far, that's a serious achievement and you should be proud of yourself. We've covered a lot of material here and I hope that you at least understand the concepts and have a little bit of hands-on experience with most of the techniques that are used in data science today. It's a very broad field, so we've touched on a little bit of everything there. So, you know, congratulations again.

If you want to further your career in this field, what I'd really encourage you to do is talk to your boss. If you work at a company that has access to some interesting datasets of its own, see if you can play around with them. Obviously, you want to talk to your boss first before you use any data owned by your company, because there's probably going to be some privacy restrictions surrounding it. You want to make sure that you're not violating the privacy of your company's customers, and that might mean that you might only be able to use that data or look at it within a controlled environment at your workplace. So, be careful when you're doing that.

If you can get permission to actually stay late at work a few days a week and, you know, mess around with some of these datasets and see what you can do with it, not only does show that you have the initiative to make yourself a better employee, you might actually discover something that might be valuable to your company, and that could just make you look even better, and actually lead to an internal transfer perhaps, into a field more directly related to where you want to take your career.

So, if you want some career advice from me, a common question I get is, "hey, I'm an engineer, I want to get more into data science, how do I do that?" The best way to do it is just do it, you know, actually do some side projects and show that you can do it and demonstrate some meaningful results from it. Show that to your boss and see where it leads you. Good luck.