{"product_id":"julia-for-data-science-9781634621304","title":"Julia for Data Science","description":"\u003cp\u003e • Author(s): Zacharias Voulgaris\u003cbr\u003e • Publisher: Technics Publications\u003cbr\u003e • Publisher Imprint: Technics Publications\u003cbr\u003e • BISAC: Data Science - Data Analytics\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAfter covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function.\u003c\/p\u003e\u003cp\u003eSpecialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: \u003c\/p\u003e \u003col\u003e \u003cli\u003eAn overview of the data science pipeline along with an example illustrating the key points, implemented in Julia\u003c\/li\u003e \u003cli\u003eOptions for Julia IDEs\u003c\/li\u003e \u003cli\u003eProgramming structures and functions\u003c\/li\u003e \u003cli\u003eEngineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing\u003c\/li\u003e \u003cli\u003eData visualization and some simple yet powerful statistics for data exploration purposes\u003c\/li\u003e \u003cli\u003eDimensionality reduction and feature evaluation\u003c\/li\u003e \u003cli\u003eMachine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines)\u003c\/li\u003e \u003cli\u003eGraph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights.\u003c\/li\u003e \u003c\/ol\u003e \u003cp\u003eEach chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e","brand":"Technics Publications","offers":[{"title":"Paperback","offer_id":45129895706775,"sku":"9781634621304","price":3232.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781634621304.webp?v=1767145531","url":"https:\/\/atlanticbooks.com\/products\/julia-for-data-science-9781634621304","provider":"Atlantic Books","version":"1.0","type":"link"}