Hello again this is a video I recorded for my publisher about my book “Advanced Data Science and Analytics with Python”. This is a video I made for my publisher about my book “Data Science and Analytics with Python”. You can get the book here and more about the book here.
This companion to “Data Science and Analytics with Python” is the result of arguments with myself about writing something to cover a few of the areas that were not included in that first volume, largely due to space/time constraints. Like the previous book, this one exists thanks to the discussions, stand-ups, brainstorms and eventual implementations of algorithms and data science projects carried out with many colleagues and friends.
As the title suggests, this book continues to use Python as a tool to train, test and implement machine learning models and algorithms. The book is aimed at data scientists who would like to continue developing their skills and apply them in business and academic settings.
The subjects discussed in this book are complementary and a follow-up to the ones covered in Volume 1. The intended audience for this book is still composed of data analysts and early-career data scientists with some experience in programming and with a background in statistical modelling. In this case, however, the expectation is that they have already covered some areas of machine learning and data analytics. The subjects discussed in this book are complementary and a follow-up to the topics discussed in “Data Science and Analytics with Python”. Although there are some references to the previous book, this volume is written to be read independently.
I have tried to keep the same tone as in the first book, peppering the pages with some bits and bobs of popular culture, science fiction and indeed Monty Python puns. The aim is still to focus on showing the concepts and ideas behind popular algorithms and their use.
In summary, “Advanced Data Science and Analytics with Python” presents each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The material covered includes machine learning and pattern recognition algorithms including: Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.
I hope you enjoy it and if you want to know more about my other books, please check the related videos here:
Also published on Medium.