Search results for ""Author Bogumił Kaminski""

Master core data analysis skills using Julia. Julia for Data Analysis is a fascinating, hands-on projects guide you through time series data, predictive models, popularity ranking, and more. With this book, you will learn how to: Read and write data in various formats Work with tabular data, including subsetting, grouping, and transforming Visualise your data using plots Perform statistical analysis Build predictive models Create complex data processing pipelines Julia was designed for the unique needs of data scientists: it's expressive and easy-to-use whilst also delivering super fast code execution. Julia for Data Analysis teaches you how to perform core data science tasks with this amazing language. It is written by Bogumił Kamiński, a top contributor to Julia, #1 Julia answerer on StackOverflow, and a lead developer of Julia's core data package DataFrames.jl. You will learn how to write production-quality code in Julia, and utilize Julia's core features for data gathering, visualisation, and working with data frames. Plus, the engaging hands-on projects get you into the action quickly. About the technology Julia is a huge step forward for data science and scientific computing. It is a powerful high-performance programming language with many developer-friendly features like garbage collection, dynamic typing, just-in-time compilation, and a flexible approach to concurrent, parallel, and distributed computing. Although Julia's strong numerical programming features make it a favorite of data scientists, it is also an awesome general purpose programming language. About the reader For data scientists familiar with Python or R. No experience with Julia required.

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This book covers the latest advances in applying agent-based modelling in social sciences. The Social Simulation Conference is the major global conference devoted to this topic. It is aimed at promoting social simulation and computational social science. This year’s special theme is “Social Simulation geared towards Post-Pandemic times”, focused not only on questions raised by the current pandemic but also on future challenges related to economic recovery, such as localization, globalization, inequality, sustainable growth and social changes induced by progressive digitalization, data availability and artificial intelligence. The primary audience of this book are scholars and practitioners in computational social sciences including economics, business, sociology, politics, psychology and urban studies.

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