Julia For Data Analysis
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Julia For Data Analysis
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Author : Bogumil Kaminski
language : en
Publisher: Simon and Schuster
Release Date : 2023-01-10
Julia For Data Analysis written by Bogumil Kaminski and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-10 with Computers categories.
Julia for Data Analysis teaches you how to handle core data analysis tasks with the Julia programming language. You'll start by reviewing language fundamentals you'll master essential data analysis skills through engaging examples. Along the way, you'll learn to easily transfer existing data pipelines to Julia.
Julia For Data Science
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Author : Zacharias Voulgaris
language : en
Publisher:
Release Date : 2016
Julia For Data Science written by Zacharias Voulgaris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Application software categories.
After 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. Specialized 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: An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia Options for Julia IDEs Programming structures and functions Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing Data visualization and some simple yet powerful statistics for data exploration purposes Dimensionality reduction and feature evaluation Machine 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) Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each 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.
Julia Programming For Data Analysi
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Author : Mark Foster
language : en
Publisher: Mark Foster
Release Date :
Julia Programming For Data Analysi written by Mark Foster and has been published by Mark Foster this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unlock the Power of Julia for Data Science, Machine Learning, and Big Data Projects Are you ready to dive into the world of data science with Julia? Whether you're a beginner eager to learn a new programming language or a data analyst looking to boost performance with modern tools, Julia Programming for Data Analysis is your ultimate hands-on guide. This practical book delivers a clear and accessible journey through the Julia programming language, designed specifically for data enthusiasts. You’ll master everything from writing your first lines of Julia code to deploying advanced machine learning models—without the steep learning curve. 💡 What You'll Learn: How to set up your environment and write your first Julia programming language examples A comprehensive Julia data science tutorial, perfect for beginners and transitioning professionals Practical techniques for manipulating, cleaning, and transforming data using the powerful DataFrames.jl package Importing and exporting datasets in popular formats like CSV, JSON, and even connecting to databases Data visualization strategies to create clear, impactful charts Step-by-step workflows for building machine learning models with Julia language machine learning tools How to harness Julia data science packages to streamline analysis and automation Deep dive into Julia language deep learning and Julia big data workflows for high-performance computing Whether you're exploring data science in Julia, looking for scalable Julia data analytics, or curious about how Julia coding compares to Python or R, this book delivers everything you need in one streamlined resource. Built for clarity and efficiency, this guide doesn’t just teach the Julia computer language—it helps you apply it with real-world data. With fully annotated examples, this is more than a tutorial—it's a practical roadmap to modern data-driven success. Why Choose Julia? Julia is fast, expressive, and designed for technical computing. For those working in data science, machine learning, or big data, Julia offers unparalleled performance without sacrificing readability. This book shows you how to take full advantage of that power. 📘 Perfect for: Aspiring and experienced data scientists Analysts looking to shift into Julia for data science Students and professionals seeking a better alternative for performance-critical data tasks Anyone curious about the future of Julia programming and high-speed analytics Don’t miss your chance to master the future of data analysis. Buy now and start your journey into Julia language success today!
Julia Programming Projects
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Author : Adrian Salceanu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-26
Julia Programming Projects written by Adrian Salceanu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-26 with Computers categories.
A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools Key FeaturesWork with powerful open-source libraries for data wrangling, analysis, and visualizationDevelop full-featured, full-stack web applications Learn to perform supervised and unsupervised machine learning and time series analysis with JuliaBook Description Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia. What you will learnLeverage Julia's strengths, its top packages, and main IDE optionsAnalyze and manipulate datasets using Julia and DataFramesWrite complex code while building real-life Julia applicationsDevelop and run a web app using Julia and the HTTP packageBuild a recommender system using supervised machine learning Perform exploratory data analysis Apply unsupervised machine learning algorithmsPerform time series data analysis, visualization, and forecastingWho this book is for Data scientists, statisticians, business analysts, and developers who are interested in learning how to use Julia to crunch numbers, analyze data and build apps will find this book useful. A basic knowledge of programming is assumed.
Julia Programming For Data Science
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Author : Mark Foster
language : en
Publisher: Mark Foster
Release Date :
Julia Programming For Data Science written by Mark Foster and has been published by Mark Foster this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
📊 Julia Programming for Data Science: From CSVs to Visual Insights Turn Raw Data Into Real Discoveries Using the Power of the Julia Programming Language Are you ready to break free from slow, bloated analytics tools and step into the future of data science? “Julia Programming for Data Science: From CSVs to Visual Insights” is the ultimate beginner-to-intermediate guide to mastering data science with Julia — the high-performance, modern solution for analysts, researchers, and machine learning developers. From reading raw CSV files to building beautiful visualizations and running powerful analytics, you’ll gain the skills you need to work smarter — not harder — using real-world Julia programming language examples and the best Julia data science packages like DataFrames.jl and Plots.jl. ✅ What You'll Learn Inside: 🔹 Import, Explore, and Clean Big Data — Fast Master the essentials of data science in Julia by importing CSVs, cleaning messy datasets, and exploring large datasets effortlessly. With the speed of Julia big data processing, you'll move from raw files to usable data in seconds. 🔹 Write Clean, Powerful Julia Code for Analytics Learn how the Julia code language simplifies complex tasks. This book teaches you how to perform filtering, transformations, aggregations, and summary statistics — all with readable and efficient Julia coding techniques. 🔹 Visualize Insights Like a Pro With Plots.jl and other top tools in the Julia statistics library, you’ll build insightful charts: from statistics histograms, scatter plots, and heatmaps, to advanced dashboards — making your data speak. 🔹 Get Started with Julia Statistical Analysis and Machine Learning Lay the groundwork for Julia statistical software, including multivariate statistics, standard deviation (std), and mode, while getting your first glimpse into Julia machine learning and Julia language deep learning. 🔹 Seamless Python Integration Already working in Python? Learn how calling Julia from Python can bring Julia’s speed and efficiency into your existing workflows without the need to start from scratch. 🎯 Perfect For: 📊 Beginners and professionals entering the world of Julia for data science 🧪 Researchers exploring Julia language statistics or scientific computing 🤖 ML engineers looking for high-speed alternatives to Python or R 📈 Analysts who need reliable, scalable solutions for data analytics with Julia 🎓 Students looking for a modern Julia data science tutorial with practical examples 🚀 Build Real Data Workflows That Deliver Results With Julia Programming for Data Science, you’re not just learning theory — you’re applying modern tools to real problems. From structured data analysis to stunning visual insights, this book gives you a complete walkthrough of the Julia programming language for today’s data-driven world. 🛒 Scroll up and get your copy now — and start mastering data science in Julia with confidence!
Julia For Data Science
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Author : Anshul Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-09-30
Julia For Data Science written by Anshul Joshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-30 with Computers categories.
Explore the world of data science from scratch with Julia by your side About This Book An in-depth exploration of Julia's growing ecosystem of packages Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets Who This Book Is For This book is aimed at data analysts and aspiring data scientists who have a basic knowledge of Julia or are completely new to it. The book also appeals to those competent in R and Python and wish to adopt Julia to improve their skills set in Data Science. It would be beneficial if the readers have a good background in statistics and computational mathematics. What You Will Learn Apply statistical models in Julia for data-driven decisions Understanding the process of data munging and data preparation using Julia Explore techniques to visualize data using Julia and D3 based packages Using Julia to create self-learning systems using cutting edge machine learning algorithms Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models Build a recommendation engine in Julia Dive into Julia's deep learning framework and build a system using Mocha.jl In Detail Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia. Style and approach This practical and easy-to-follow yet comprehensive guide will get you learning about Julia with respect to data science. Each topic is explained thoroughly and placed in context. For the more inquisitive, we dive deeper into the language and its use case. This is the one true guide to working with Julia in data science.
Julia Programming For Statistics
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Author : Mark Foster
language : en
Publisher: Mark Foster
Release Date :
Julia Programming For Statistics written by Mark Foster and has been published by Mark Foster this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unlock the Power of Julia for Modern Statistical Analysis Are you ready to harness the speed and elegance of the Julia programming language for real-world statistics and data science applications? "Julia Programming for Statistics" is your complete, hands-on guide to mastering statistical methods and applications using one of today’s fastest-growing languages in data science. Whether you're a student, researcher, or data analyst looking to expand your skills, this book will take you from the fundamentals of Julia programming to advanced techniques in data science with Julia. Learn how to write powerful Julia code and apply it directly to real-world statistical problems. 🔍 What You'll Learn Inside: ✅ Foundations of Julia Programming – Understand the syntax and structure of the Julia computer language, with practical examples tailored for data analysis. ✅ Descriptive Statistics & Hypothesis Testing – Perform core statistical tasks using Julia data science packages like StatsBase. ✅ ANOVA, Linear & Multiple Regression – Learn essential and advanced statistical modeling techniques with clear, real-world examples. ✅ Bayesian Methods & Monte Carlo Simulations – Dive into probabilistic programming using Julia language deep learning and simulation tools. ✅ Big Data & Machine Learning Foundations – Discover how Julia data analytics meets performance demands in big data environments. ✅ Step-by-Step Tutorials – Hands-on walkthroughs designed for both beginners and experienced programmers, featuring Julia programming language examples that are easy to follow. 💡 Why Learn Statistics with Julia? The Julia language combines the ease of Python with the performance of C, making it perfect for high-performance statistical computing and machine learning. This book shows you how to leverage Julia’s modern ecosystem for efficient, scalable, and elegant solutions. Whether you're exploring Julia for data science, tackling complex models, or diving into Julia machine learning, this book gives you the tools and confidence to get the job done. 🎯 Who This Book is For: Aspiring data scientists looking for an edge in Julia coding Statisticians and analysts seeking performance and clarity Engineers, students, and researchers exploring data science in Julia Anyone interested in combining statistical analysis with a powerful, modern language Start Your Journey into Statistical Mastery with Julia Don’t settle for slow or outdated tools. Unlock the potential of Julia programming for statistics and level up your data science skills with a language built for the future. ➡️ Scroll up and click “Buy Now” to start learning Julia for statistical computing today!
Ultimate Parallel And Distributed Computing With Julia For Data Science Excel In Data Analysis Statistical Modeling And Machine Learning By Leveraging Mlbase Jl And Mlj Jl To Optimize Workflows
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Author : Nabanita Dash
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-01-03
Ultimate Parallel And Distributed Computing With Julia For Data Science Excel In Data Analysis Statistical Modeling And Machine Learning By Leveraging Mlbase Jl And Mlj Jl To Optimize Workflows written by Nabanita Dash and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-03 with Computers categories.
Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence! Key Features ● Comprehensive Learning Journey from fundamentals of Julia ML to advanced techniques. ● Immersive practical approach with real-world examples, exercises, and scenarios, ensuring immediate application of acquired knowledge. ● Delve into the unique features of Julia and unlock its true potential to excel in modern ML applications. Book Description This book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results. The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning. What you will learn● Master Julia ML Basics to gain a deep understanding of Julia's syntax, variables, and functions. ● Efficient Data Handling with Julia arrays and DataFrames for streamlined and insightful analysis. ● Develop expertise in both basic and advanced statistical models for informed decision-making through Statistical Modeling. ● Achieve Machine Learning Proficiency by confidently implementing ML algorithms using MLJ.jl and MLBase.jl. ● Apply Bayesian Inference Skills with Turing.jl for advanced modeling techniques. ● Optimize workflows using Julia's Parallel Processing Capabilities and Distributed Computing for efficient and scalable data processing. Table of Contents 1. Julia In Data Science Arena 2. Getting Started with Julia 3. Features Assisting Scaling ML Projects 4. Data Structures in Julia 5. Working With Datasets In Julia 6. Basics of Statistics 7. Probability Data Distributions 8. Framing Data in Julia 9. Working on Data in DataFrames 10. Visualizing Data in Julia 11. Introducing Machine Learning in Julia 12. Data and Models 13. Bayesian Statistics and Modeling 14. Parallel Computation in Julia 15. Distributed Computation in Julia Index
Statistics With Julia
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Author : Yoni Nazarathy
language : en
Publisher: Springer Nature
Release Date : 2021-09-04
Statistics With Julia written by Yoni Nazarathy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-04 with Computers categories.
This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
Ultimate Parallel And Distributed Computing With Julia For Data Science
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Author : Nabanita Dash
language : en
Publisher: Sextil Online LLC
Release Date : 2024-01-03
Ultimate Parallel And Distributed Computing With Julia For Data Science written by Nabanita Dash and has been published by Sextil Online LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-03 with Computers categories.
This book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results. The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.