Bogumil Kaminski – författare

This accessible book helps readers to see the bigger picture of advanced mathematics. The book contains carefully selected, challenging problems in an easy-to-follow, step-by-step process. Neither prior preparation nor any mathematical sophistication is required. The authors guide the reader to “train their brain” to think and express themselves in a rigorous, mathematical way, and to extract facts, analyze the problem, and identify main challenges. A firm foundation in a diverse range of topics is presented. Moreover, the authors show how to draw appropriate, true conclusions. Computer support is used to better intuition into discussed problems. The book is designed for self-study. It can be used to bridge the gap between introductory calculus/linear algebra courses and more advanced courses offered at universities. It improves the ability to read, write, and think in a rigorous, mature mathematical fashion. The reader will develop a deeper understanding in preparation to succeed in more advanced course work.Features•The authors employ a six-step process: 1.SOURCE2.PROBLEM3.THEORY4.SOLUTION5.REMARK6.EXERCISES•An Appendix introduces programming in JuliaThis book is also suitable for high school students that are interested in competing in math competitions or simply for people of all ages and backgrounds who want to expand their knowledge and to challenge themselves with interesting questions.

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platforms are close friends). Link prediction (who is likely to connect to whom on such platforms). Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests). Influential node detection (which social media users would be the best ambassadors of a specific product).This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material. Bogumił Kamiński is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumił is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem.Paweł Prałat is a Professor of Mathematics in Ryerson University, whose main research interests are in random graph theory, especially in modeling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics in The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and three books with 130 plus collaborators.François Théberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 where he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platforms are close friends). Link prediction (who is likely to connect to whom on such platforms). Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests). Influential node detection (which social media users would be the best ambassadors of a specific product).This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material. Bogumił Kamiński is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumił is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem.Paweł Prałat is a Professor of Mathematics in Ryerson University, whose main research interests are in random graph theory, especially in modeling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics in The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and three books with 130 plus collaborators.François Théberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 where he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.

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.

Discover the new features and widely used packages in Julia to solve complex computational problems in your statistical applications.Key FeaturesAddress the core problems of programming in Julia with the most popular packages for common tasksTackle issues while working with Databases and Parallel data processing with JuliaExplore advanced features such as metaprogramming, functional programming, and user defined typesBook DescriptionJulia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia.Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia.By the end of the book, you will have acquired the skills to work more effectively with your dataWhat you will learnBoost your code's performance using Julia's unique featuresOrganize data in to fundamental types of collections: arrays and dictionariesOrganize data science processes within Julia and solve related problemsScale Julia computations with cloud computingWrite data to IO streams with Julia and handle web transferDefine your own immutable and mutable typesSpeed up the development process using metaprogrammingWho this book is forThe target audience of this book is data scientists or programmers that want to improve their skills in working with the Julia programming language. It is recommended that the user has a little experience with Julia or intermediate-level experience with other programming languages such as Python, R, or MATLAB.

This book constitutes the proceedings of the 17th International Workshop on Algorithms and Models for the Web Graph, WAW 2020, held in Warsaw, Poland, in September 2020.

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.

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.

This book constitutes the refereed proceedings of the 19th International Workshop on Modelling and Mining Networks, WAW 2024, held in Warsaw, Poland, during June 3–6, 2024. The 12 full papers presented in this book were carefully reviewed and selected from 19 submissions.

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—Crossroads Between Social Science and Computational Methods”, focused on modelling of social phenomena, computational aspects and the theory linking these two scientific fields.The primary audience of this book are scholars and practitioners in computational social sciences including economics, business, sociology, politics, psychology and urban studies.

This book constitutes the refereed proceedings of the 20th International Workshop on Modelling and Mining Networks, WAW 2025, held in Vilnius, Lithuania, during June 30–July 3, 2025.The 13 full papers presented in this book were carefully reviewed and selected from 20 submissions.The aim of the 20th Workshop on Modelling and Mining Networks was to further the understanding of networks that arise in theoretical as well as applied domains. The goal was also to stimulate the development of high performance and scalable algorithms that exploit these networks.

This book constitutes the refereed proceedings of the 21st International Workshop on Modelling and Mining Networks, WAW 2026, held in Toronto, ON, Canada, during June 15–19, 2026.The 7 full papers included in these volumes were carefully reviewed and selected from 10 submissions. The goal was also to stimulate the development of high performance and scalable algorithms that exploit complex networks.

This book constitutes the proceedings of the 15th International Conference on Group Decision and Negotiation, GDN 2015, held in Warsaw, Poland, in June 2015. The GDN meetings aim to bring together researchers and practitioners from a wide spectrum of fields, including economics, management, computer science, engineering, and decision science.From a total of 119 submissions, 32 papers were accepted for publication in this volume. The papers are organized into topical sections on group problem structuring and negotiation, negotiation and group processes, preference analysis and decision support, formal models, voting and collective decision making, conflict resolution in energy and environmental management, negotiation support systems and studies, online collaboration and competition, and market mechanisms and their users.