Julia for Data Analysis

Julia is a compelling option for scientists familiar with Python and R, as the language aims to make code readable and equally efficient as low-level libraries available in Python and R. The book written by Bogumił is a valuable resource for learning about Julia, as he is an expert in the language and has contributed to many of its open-source projects.The book is divided into two chapters, with the first chapter covering the fundamentals of the Julia language and the second chapter providing practical examples of using APIs, working with data in various formats, performing statistical analysis, visualizing data, and building machine learning models.

Worthy, useful.

I'm migrating to Julia from over 15 years with R. If this volume had been available earlier and came to my attention it wouldn't have taken so long.The author is responsible for the DataFrames.jl domain-specific language. After covering the basics of working in Julia, he steps us through mini-projects of bringing data into namespace by, for example, obtaining a zip file from an url, unzipping it and reading in the contained csv files. Dropping columns, adding new columns by calculations, dropping missing (NA) values--all the basics of whipping data into shape are described simply and directly. Simple descriptive statistcs and modeling using data frames is covered to sufficient detail to convince you that you can do it with your own data, as well as plotting. A real K&R grade treatment.

This book requires the reader to already be skilled at data analysis in R or Python, and it does not really teach analysis workflows so much as describe language features of Julia or packages relevant to particular tasks. The book seems more geared towards package developers than for people doing interactive data analysis. For a book about data analysis, there was surprisingly little content on data visualization. For most readers, I would instead recommend checking out the Tidier.jl documentation and other resources if their primary interest is data analysis rather than package development.

This title by mr. Kaminsky is I believe the de-facto reference for the DataFrames.jl package.The book offers a solid introduction to data analysis using the Julia language.The first part of the book (chapters 1-7) provide the reader with an overview of the "essential" Julialanguage skills, addressing syntax, data types, and from here on, it builds its way into dealing with data-intensive manipulation, mainly through collections, strings and time-series data.The second part of the book delves into the data analysis realm of the title, and shows how Julia can help the reader with the manipulation of DataFrames.The author starts with the basics of (t)his package, then in numerous examples shows how to create data frame objects, retrieeve their data, and to do important operations with the data frame objects, i.e., convert and group, mutate and transform, etc.Some of the highlights in the book: * The author not only makes extensive use of DataFrames, but also of other packages for visualization, benchmarking, and even web-service creation to serve the data.* The companion sample code repository is very thorough, and not only contains pure julia files, but there are also notebooks in Jupyter and Pluto format* There a three generous appendices in the text, so the reader can A) learn to set up his/her Julia environment for the 1st time, B) find the answers to the proposed exercises in the text, and C) have an overview of recommended Julia packages for data analysis.Some of the parts I found difficult (, or perhaps aren't my cup of tea):- Quite some of the examples offered have to do with the financial world. This perhaps comes from the formation/background of the author.- The physical text might be bulky, but then again, dealing with data, you might expect to see many tablesAll in all, this title might blend well with McNicholas' "Data Science with Julia", and/or Nazarathy's "Statistics with Julia" for the more scientific readers.

I am a beginner programmer who has previously dabbled in both R and Python. Julia has been on my radar for a few years (since the 1,0 release), but I have previously used Python and Pandas for my data wrangling needs.Kamińskis book begins outlining why Julia is a good fit for data analysis tasks with illustrative examples, and then reviews both strengths and weaknesses of the language. Part one of the book introduces Julia in general and the tools needed to work in the language in a structured manner. Part two builds upon part one and introduces data analysis in Julia.Kamiński writes in a friendly and very readable manner, both explaining different ways of accomplish tasks and also highlighting common errors or quirks. Boxes often provide additional detail, clarifies important points or links to sources providing additional information, which I appreciated. Many chapters are built around small projects (such as extracting exchange rate data or analyzing chess puzzles) as a way to introduce concepts and tools, which gives good structure when learning.All in all this is an excellent introduction to both Julia and Data Analysis. Very little programming experience is needed in order to be able to use the material, and I feel it provides a very solid foundation for future projects in using Julia.In short Kamiński has written, what I consider to be, a pedagogical masterpiece!