Book Image

Learning Python

By : Romano
Book Image

Learning Python

By: Romano

Overview of this book

Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned.
Table of Contents (14 chapters)
13
Index

Where do we go from here?

Data science is indeed a fascinating subject. As I said in the introduction, those who want to delve into its meanders need to be well trained in mathematics and statistics. Working with data that has been interpolated incorrectly renders any result about it useless. The same goes for data that has been extrapolated incorrectly or sampled with the wrong frequency. To give you an example, imagine a population of individuals that are aligned in a queue. If, for some reason, the gender of that population alternated between male and female, the queue would be something like this: F-M-F-M-F-M-F-M-F...

If you sampled it taking only the even elements, you would draw the conclusion that the population was made up only of males, while sampling the odd ones would tell you exactly the opposite.

Of course, this was just a silly example, I know, but believe me it's very easy to make mistakes in this field, especially when dealing with big data where sampling is mandatory...