I advice you to add a reference to your book, via a pull request to: - https://github.com/JuliaLang/julialang.github.com Which should go in this file:
- https://github.com/JuliaLang/julialang.github.com/blob/master/learning/index.md El martes, 13 de septiembre de 2016, 19:58:23 (UTC-5), Zacharias Voulgaris escribió: > > Hi everyone, > > > > I’m fairly new in this group but I’ve been an avid Julia user since Ver. > 0.2. About a year ago I decided to take the next step and start using Julia > professionally, namely for data science projects (even if at that time I > was a PM in Microsoft). Shortly afterwards, I started writing a book about > it, focusing on how we can use this wonderful tool for data science > projects. My aim was to make it easy for everyone to learn to make use of > their Julia know-how for tackling data science project, but also to help > more experienced data scientists to do what they usually do but instead of > Python / R / Scala, use a more elegant tool (Julia). I’m writing this post > because this book is finally a reality. > > > > In this book, which is titled Julia for Data Science and published by > Technics Publications, I cover various data science topics. These include > data engineering, supervised and unsupervised machine learning, > statistics, and some graph analysis. Also, since the focus is on > applications rather than digging into the deeper layers of the language, I > make use of IJulia instead of Juno, while I also refrain from delving into > custom types and meta-programming. Yet, in this book I make use of tools > and metrics that are rarely, if ever, mentioned in other data science books > (e.g. the T-SNE method, some variants of Jaccard Similarity, some > alternative error averages for regression, and more). Also, I try to keep > assumptions about the reader’s knowledge to a minimum, so there are plenty > of links to references for the various concepts used in the book, from > reliable sources. Finally, the book includes plenty of reference sections > at the end, so you don’t need to remember all the packages introduced, or > all the places where you can learn more about the language. Each chapter is > accompanied by a series of questions and some exercises, to help you make > sure you comprehend everything you’ve read, while at the end I include a > full project for you to practice on (answers to all the exercises and the > project itself are at an appendix). All the code used in the book is > available on Jupyter files, while the data files are also available in .csv > and text format. > > > > The book is available in both paperback and eBook format (PDF, Kindle, and > Safari) at the publisher’s website: https://technicspub.com/analytics > > > > Please note that for some reason the Packt publishing house, which has had > the monopoly on Julia books up until now, decided to follow suit, which is > why it is releasing a book with the same title next month (clearly, > imagination is not their strongest suit!). So, please make sure that you > don’t confuse the two books. My goal is not to make a quick buck through > this book (which is why I’m not publishing it via Packt); instead, I aim to > make Julia more well-known to the data science world while at the same time > make data science more accessible to all Julia users. > > > > Thanks, > > > > Zack >
