A hands-on resource designed to teach the mathematics of signals and systems with MATLAB™ In this newly revised second edition of Practical Signals Theory with MATLAB™ Applications, Richard Tervo delivers an articulate presentation of the mathematics underlying real world engineering applications and everyday electronic devices. The new edition provides extended coverage of communication systems—including digital and spread spectrum communications—as well as a new introductory chapter on using MATLAB™ as a tool to visualize the mathematics of signals and systems. The text contains numerous hands-on examples and expanded end-of-chapter exercises. It is a one-stop reference for signals and systems, explaining aspects of commonplace signal types, orthogonality and signal decomposition, transformations, and the graphical presentation of calculations and results. Readers will also find: A solid introduction to the mathematics of continuous and discrete signals represented in time and frequency domains Thorough coverage of the classic Fourier, Laplace and z-transforms, and their many applications New end-of-chapter worked exercises, a variety of in-line study questions with answers and easily reproducible MATLAB™ code demonstrations Bonus material on related applications in different fields of study and a companion website designed to support additional learning Perfect for undergraduate and graduate students of signals and systems, signals theory, and related areas of electrical engineering, Practical Signals Theory with MATLAB™ Applications will also benefit researchers and professors in the field of system design and signal processing.

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin writing programs in R. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing tests and modeling. You’ll even learn how to create impressive data visualizations with R’s graphics tools and contributed packages, like ggplot2, ggvis, and rgl. Dozens of hands-on exercises take you from theory to practice as you learn: The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling and how to execute them in R How to access R’s thousands of functions, libraries, and datasets How to draw valid and useful conclusions from your data and create publication-quality graphics of your results The Book of R brings both statistics and R to life. With clear explanations, practical examples, and hands-on exercises, this book opens the door to the evolving world of data analysis. New to this edition: The entire book has been revised and expanded, with nearly 100 pages of new content and exercises. You’ll find greater coverage of data plots and R graphics, guidance on using pipes to string together commands, and new ways to read and write external files, among many other lessons.

Share your machine learning models, create chatbots, as well as build and deploy insightful dashboards speedily using Taipy with this hands-on book featuring real-world application examples from multiple industries Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Create visually compelling, interactive data applications with Taipy Bring predictive models to end users and create data pipelines to compare scenarios with what-if analyses Go beyond prototypes to build and deploy production-ready applications using the cloud provider of your choice Purchase of the print or Kindle book includes a free PDF eBook in full color Book Description While data analysts, data scientists, and BI experts have the tools to analyze data, build models, and create compelling visuals, they often struggle to translate these insights into practical, user-friendly applications that help end users answer real-world questions, such as identifying revenue trends, predicting inventory needs, or detecting fraud, without wading through complex code. This book is a comprehensive guide to overcoming this challenge. This book teaches you how to use Taipy, a powerful open-source Python library, to build intuitive, production-ready data apps quickly and efficiently. Instead of creating prototypes that nobody uses, you'll learn how to build faster applications that process large amounts of data for multiple users and deliver measurable business impact. Taipy does the heavy lifting to enable your users to visualize their KPIs, interact with charts and maps, and compare scenarios for better decision-making. You’ll learn to use Taipy to build apps that make your data accessible and actionable in production environments like the cloud or Docker. By the end of this book, you won’t just understand Taipy, you'll be able to transform your data skills into impactful solutions that address real-world needs and deliver valuable insights. Email sign-up and proof of purchase required What you will learn Explore Taipy, its use cases, and how it's different from other projects Discover how to create visually appealing interactive apps, display KPIs, charts, and maps Understand how to compare scenarios to make better decisions Connect Taipy applications to several data sources and services Develop apps for diverse use cases, including chatbots, dashboards, ML apps, and maps Deploy Taipy applications on different types of servers and services Master advanced concepts for simplifying and accelerating your development workflow Who this book is for If you’re a data analyst, data scientist, or BI analyst looking to build production-ready data apps entirely in Python, this book is for you. If your scripts and models sit idle because non-technical stakeholders can’t use them, this book shows you how to turn them into full applications fast with Taipy, so your work delivers real business value. It’s also valuable for developers and engineers who want to streamline their data workflows and build UIs in pure Python.

Maximize your portfolio, analyze markets, and make data-driven investment decisions using Python and generative AI. Investing for Programmers shows you how you can turn your existing skills as a programmer into a knack for making sharper investment choices. You’ll learn how to use the Python ecosystem, modern analytic methods, and cutting-edge AI tools to make better decisions and improve the odds of long-term financial success. In Investing for Programmers you’ll learn how to: Build stock analysis tools and predictive models Identify market-beating investment opportunities Design and evaluate algorithmic trading strategies Use AI to automate investment research Analyze market sentiments with media data mining In Investing for Programmers you'll learn the basics of financial investment as you conduct real market analysis, connect with trading APIs to automate buy-sell, and develop a systematic approach to risk management. Don’t worry—there’s no dodgy financial advice or flimsy get-rich-quick schemes. Real-life examples help you build your own intuition about financial markets, and make better decisions for retirement, financial independence, and getting more from your hard-earned money. About the Technology A programmer has a unique edge when it comes to investing. Using open-source Python libraries and AI tools, you can perform sophisticated analysis normally reserved for expensive financial professionals. This book guides you step-by-step through building your own stock analysis tools, forecasting models, and more so you can make smart, data-driven investment decisions. About the Book Investing for Programmers shows you how to analyze investment opportunities using Python and machine learning. In this easy-to-read handbook, experienced algorithmic investor Stefan Papp shows you how to use Pandas, NumPy, and Matplotlib to dissect stock market data, uncover patterns, and build your own trading models. You’ll also discover how to use AI agents and LLMs to enhance your financial research and decision-making process. What's Inside Build stock analysis tools and predictive models Design algorithmic trading strategies Use AI to automate investment research Analyze market sentiment with media data mining About the Reader For professional and hobbyist Python programmers with basic personal finance experience. About the Author Stefan Papp combines 20 years of investment experience in stocks, cryptocurrency, and bonds with decades of work as a data engineer, architect, and software consultant. Quotes Especially valuable for anyone looking to improve their investing. - Armen Kherlopian, Covenant Venture Capital A great breadth of topics—from basic finance concepts to cutting-edge technology. - Ilya Kipnis, Quantstrat Trader A top tip for people who want to leverage development skills to improve their investment possibilities. - Michael Zambiasi, Raiffeisen Digital Bank Brilliantly bridges the worlds of coding and finance. - Thomas Wiecki, PyMC Labs

Textbook on nonlinear and parametric vibrations discussing relevant terminology and analytical and computational tools for analysis, design, and troubleshooting Introduction to Engineering Nonlinear and Parametric Vibrations with MATLAB and MAPLE is a comprehensive textbook that provides theoretical breadth and depth and analytical and computational tools needed to analyze, design, and troubleshoot related engineering problems. The text begins by introducing and providing the required math and computer skills for understanding and simulating nonlinear vibration problems. This section also includes a thorough treatment of parametric vibrations. Many illustrative examples, including software examples, are included throughout the text. A companion website includes the MATLAB and MAPLE codes for examples in the textbook, and a theoretical development for a homoclinic path to chaos. Introduction to Engineering Nonlinear and Parametric Vibrations with MATLAB and MAPLE provides information on: Natural frequencies and limit cycles of nonlinear autonomous systems, covering the multiple time scale, Krylov-Bogellubov, harmonic balance, and Lindstedt-Poincare methods Co-existing fixed point equilibrium states of nonlinear systems, covering location, type, and stability, domains of attraction, and phase plane plotting Parametric and autoparametric vibration including Floquet, Mathieu and Hill theory Numerical methods including shooting, time domain collocation, arc length continuation, and Poincare plotting Chaotic motion of nonlinear systems, covering iterated maps, period doubling and homoclinic paths to chaos, and discrete and continuous time Lyapunov exponents Extensive MATLAB and MAPLE coding for the examples presented Introduction to Engineering Nonlinear and Parametric Vibrations with MATLAB and MAPLE is an essential up-to-date textbook on the subject for upper level undergraduate and graduate engineering students as well as practicing vibration engineers. Knowledge of differential equations and basic programming skills are requisites for reading.

Simplify stats and learn how to graph, analyze, and interpret data the easy way Statistical Analysis with R For Dummies makes stats approachable by combining clear explanations with practical applications. You'll learn how to download and use R and RStudio—two free, open-source tools—to learn statistics concepts, create graphs, test hypotheses, and draw meaningful conclusions. Get started by learning the basics of statistics and R, calculate descriptive statistics, and use inferential statistics to test hypotheses. Then, visualize it all with graphs and charts. This Dummies guide is your well-marked path to sailing through statistics. Get clear explanations of the basics of statistics and data analysis Learn how to analyze and visualize data with R, step by step Create charts, graphs, and summaries to interpret results Explore hypothesis testing, and prediction techniques This is the perfect introduction to R for students, professionals, and the stat-curious.

A practical guide to reproducible and high impact mass spectrometry data analysis R Programming for Mass Spectrometry teaches a rigorous and detailed approach to analyzing mass spectrometry data using the R programming language. It emphasizes reproducible research practices and transparent data workflows and is designed for analytical chemists, biostatisticians, and data scientists working with mass spectrometry. Readers will find specific algorithms and reproducible examples that address common challenges in mass spectrometry alongside example code and outputs. Each chapter provides practical guidance on statistical summaries, spectral search, chromatographic data processing, and machine learning for mass spectrometry. Key topics include: Comprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data Processing chromatographic peaks, peak detection, and quality control in mass spectrometry data Applying machine learning techniques, using Tidymodels for supervised and unsupervised learning, as well as for feature engineering and selection, providing modern approaches to data-driven insights Methods for producing reproducible, publication-ready reports and web pages using RMarkdown R Programming for Mass Spectrometry is an indispensable guide for researchers, instructors, and students. It provides modern tools and methodologies for comprehensive data analysis. With a companion website that includes code and example datasets, it serves as both a practical guide and a valuable resource for promoting reproducible research in mass spectrometry.

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Data Without Labels you’ll learn: Fundamental building blocks and concepts of machine learning and unsupervised learning Data cleaning for structured and unstructured data like text and images Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE Association rule algorithms like aPriori, ECLAT, SPADE Unsupervised time series clustering, Gaussian Mixture models, and statistical methods Building neural networks such as GANs and autoencoders Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling Association rule algorithms like aPriori, ECLAT, and SPADE Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask How to interpret the results of unsupervised learning Choosing the right algorithm for your problem Deploying unsupervised learning to production Maintenance and refresh of an ML solution Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You’ll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don’t get bogged down in theory—the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. About the Technology Generative AI, predictive algorithms, fraud detection, and many other analysis tasks rely on cheap and plentiful unlabeled data. Machine learning on data without labels—or unsupervised learning—turns raw text, images, and numbers into insights about your customers, accurate computer vision, and high-quality datasets for training AI models. This book will show you how. About the Book Data Without Labels is a comprehensive guide to unsupervised learning, offering a deep dive into its mathematical foundations, algorithms, and practical applications. It presents practical examples from retail, aviation, and banking using fully annotated Python code. You’ll explore core techniques like clustering and dimensionality reduction along with advanced topics like autoencoders and GANs. As you go, you’ll learn where to apply unsupervised learning in business applications and discover how to develop your own machine learning models end-to-end. What's Inside Master unsupervised learning algorithms Real-world business applications Curate AI training datasets Explore autoencoders and GANs applications About the Reader Intended for data science professionals. Assumes knowledge of Python and basic machine learning. About the Author Vaibhav Verdhan is a seasoned data science professional with extensive experience working on data science projects in a large pharmaceutical company. Quotes An invaluable resource for anyone navigating the complexities of unsupervised learning. A must-have. - Ganna Pogrebna, The Alan Turing Institute Empowers the reader to unlock the hidden potential within their data. - Sonny Shergill, Astra Zeneca A must-have for teams working with unstructured data. Cuts through the fog of theory ili Explains the theory and delivers practical solutions. - Leonardo Gomes da Silva, onGRID Sports Technology The Bible for unsupervised learning! Full of real-world applications, clear explanations, and excellent Python implementations. - Gary Bake, Falconhurst Technologies

If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis—from wrangling data and generating statistics to identifying patterns and testing hypotheses. Whether you're a data scientist, software engineer, or data enthusiast, you'll get up to speed on commonly used tools including NumPy, SciPy, and Pandas. You'll explore distributions, relationships between variables, visualization, and many other concepts. And all chapters are available as Jupyter notebooks, so you can read the text, run the code, and work on exercises all in one place. Analyze data distributions and visualize patterns using Python libraries Improve predictions and insights with regression models Dive into specialized topics like time series analysis and survival analysis Integrate statistical techniques and tools for validation, inference, and more Communicate findings with effective data visualization Troubleshoot common data analysis challenges Boost reproducibility and collaboration in data analysis projects with interactive notebooks

This book is an essential guide designed to equip you with the vital tools and knowledge needed to excel in data science. Master the end-to-end process of data collection, processing, validation, and imputation using R, and understand fundamental theories to achieve transparency with literate programming, renv, and Git--and much more. Each chapter is concise and focused, rendering complex topics accessible and easy to understand. Data Insight Foundations caters to a diverse audience, including web developers, mathematicians, data analysts, and economists, and its flexible structure allows enables you to explore chapters in sequence or navigate directly to the topics most relevant to you. While examples are primarily in R, a basic understanding of the language is advantageous but not essential. Many chapters, especially those focusing on theory, require no programming knowledge at all. Dive in and discover how to manipulate data, ensure reproducibility, conduct thorough literature reviews, collect data effectively, and present your findings with clarity. What You Will Learn Data Management: Master the end-to-end process of data collection, processing, validation, and imputation using R. Reproducible Research: Understand fundamental theories and achieve transparency with literate programming, renv, and Git. Academic Writing: Conduct scientific literature reviews and write structured papers and reports with Quarto. Survey Design: Design well-structured surveys and manage data collection effectively. Data Visualization: Understand data visualization theory and create well-designed and captivating graphics using ggplot2. Who this Book is For Career professionals such as research and data analysts transitioning from academia to a professional setting where production quality significantly impacts career progression. Some familiarity with data analytics processes and an interest in learning R or Python are ideal.

Drawing on author’s 30+ years of teaching experience, ”Continuous-Time Signals and Systems: A MATLAB Integrated Approach” represents a novel and comprehensive approach to understanding signals and systems theory.

DuckDB, an open source in-process database created for OLAP workloads, provides key advantages over more mainstream OLAP solutions: It's embeddable and optimized for analytics. It also integrates well with Python and is compatible with SQL, giving you the performance and flexibility of SQL right within your Python environment. This handy guide shows you how to get started with this versatile and powerful tool. Author Wei-Meng Lee takes developers and data professionals through DuckDB's primary features and functions, best practices, and practical examples of how you can use DuckDB for a variety of data analytics tasks. You'll also dive into specific topics, including how to import data into DuckDB, work with tables, perform exploratory data analysis, visualize data, perform spatial analysis, and use DuckDB with JSON files, Polars, and JupySQL. Understand the purpose of DuckDB and its main functions Conduct data analytics tasks using DuckDB Integrate DuckDB with pandas, Polars, and JupySQL Use DuckDB to query your data Perform spatial analytics using DuckDB's spatial extension Work with a diverse range of data including Parquet, CSV, and JSON

Causal Inference in R is a comprehensive guide that introduces you to the methods and practices of determining causality in data through the lens of R programming. By navigating its pages and examples, you'll master the application of causal models and statistical approaches to real-world problems, enabling more informed data-driven decisions. What this Book will help me do Understand the principles and foundations of causal inference to identify causality in data. Apply methods like propensity score matching and instrumental variables using R. Leverage real-world case studies to analyze and resolve confounding factors and make better data claims. Harness statistical methods and R tools to address real-world data challenges innovatively. Develop a strategy for integrating causal models into decision-making workflows with confidence. Author(s) Subhajit Das, the author of Causal Inference in R, is an accomplished applied scientist with over a decade of experience in causal inference methodologies and data analysis. Subhajit is passionate about empowering learners by breaking down complex concepts into manageable, clear explanations. His expertise ensures that readers not only understand the theory behind causal inference but are also able to apply it effectively using R. Who is it for? This book is ideal for data analysts, statisticians, and researchers looking to deepen their understanding of causal inference techniques using R. Whether you're a practitioner aiming to enhance your data-driven decision-making skills or a student aspiring to tackle advanced causal analysis, this book provides pathbreaking insights. It's suitable for individuals at beginner to intermediate skill levels in data analysis, especially those in public policy, economics, and the social sciences who utilize R regularly.

Discover the power of pandas for your data analysis tasks. Pandas Cookbook provides practical, hands-on recipes for mastering pandas 2.x, guiding you through real-world scenarios quickly and effectively. What this Book will help me do Efficiently manipulate and clean data using pandas. Perform advanced grouping and aggregation operations. Handle time series data with pandas robust functions. Optimize pandas code for better performance. Integrate pandas with tools like NumPy and databases. Author(s) William Ayd and Matthew Harrison co-authored this insightful cookbook. With years of practical experience in data science and Python development, both authors aim to make data analysis accessible and efficient using pandas. Who is it for? This book is perfect for Python developers and data analysts looking to enhance their data manipulation skills. Whether you're a beginner aiming to understand pandas or a professional seeking advanced insights, this book is tailored for anyone handling structured data.

Understand how to use R and R Studio to analyse HR data and deliver insights that drive workforce and business performance.

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more. Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning. What You'll Learn Work with vectors and matrices using NumPy Review Symbolic computing with SymPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Understand statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.

Dive into the world of data analysis with the Polars Cookbook. This book, ideal for data professionals, covers practical recipes to manipulate, transform, and analyze data using the Python Polars library. You'll learn both the fundamentals and advanced techniques to build efficient and scalable data workflows. What this Book will help me do Master the basics of Python Polars including installation and setup. Perform complex data manipulation like pivoting, grouping, and joining. Handle large-scale time series data for accurate analysis. Understand data integration with libraries like pandas and numpy. Optimize workflows for both on-premise and cloud environments. Author(s) Yuki Kakegawa is an experienced data analytics consultant who has collaborated with companies such as Microsoft and Stanford Health Care. His passion for data led him to create this detailed guide on Polars. His expertise ensures you gain real-world, actionable insights from every chapter. Who is it for? This book is perfect for data analysts, engineers, and scientists eager to enhance their efficiency with Python Polars. If you are familiar with Python and tools like pandas but are new to Polars, this book will upskill you. Whether handling big data or optimizing code for performance, the Polars Cookbook has the guidance you need to succeed.

Dive into DuckDB and start processing gigabytes of data with ease—all with no data warehouse. DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you’ll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you’ll learn everything you need to get the most out of DuckDB—all through hands-on examples. Open up DuckDB in Action and learn how to: Read and process data from CSV, JSON and Parquet sources both locally and remote Write analytical SQL queries, including aggregations, common table expressions, window functions, special types of joins, and pivot tables Use DuckDB from Python, both with SQL and its "Relational"-API, interacting with databases but also data frames Prepare, ingest and query large datasets Build cloud data pipelines Extend DuckDB with custom functionality Pragmatic and comprehensive, DuckDB in Action introduces the DuckDB database and shows you how to use it to solve common data workflow problems. You won’t need to read through pages of documentation—you’ll learn as you work. Get to grips with DuckDB's unique SQL dialect, learning to seamlessly load, prepare, and analyze data using SQL queries. Extend DuckDB with both Python and built-in tools such as MotherDuck, and gain practical insights into building robust and automated data pipelines. About the Technology DuckDB makes data analytics fast and fun! You don’t need to set up a Spark or run a cloud data warehouse just to process a few hundred gigabytes of data. DuckDB is easily embeddable in any data analytics application, runs on a laptop, and processes data from almost any source, including JSON, CSV, Parquet, SQLite and Postgres. About the Book DuckDB in Action guides you example-by-example from setup, through your first SQL query, to advanced topics like building data pipelines and embedding DuckDB as a local data store for a Streamlit web app. You’ll explore DuckDB’s handy SQL extensions, get to grips with aggregation, analysis, and data without persistence, and use Python to customize DuckDB. A hands-on project accompanies each new topic, so you can see DuckDB in action. What's Inside Prepare, ingest and query large datasets Build cloud data pipelines Extend DuckDB with custom functionality Fast-paced SQL recap: From simple queries to advanced analytics About the Reader For data pros comfortable with Python and CLI tools. About the Authors Mark Needham is a blogger and video creator at @‌LearnDataWithMark. Michael Hunger leads product innovation for the Neo4j graph database. Michael Simons is a Java Champion, author, and Engineer at Neo4j. Quotes I use DuckDB every day, and I still learned a lot about how DuckDB makes things that are hard in most databases easy! - Jordan Tigani, Founder, MotherDuck An excellent resource! Unlocks possibilities for storing, processing, analyzing, and summarizing data at the edge using DuckDB. - Pramod Sadalage, Director, Thoughtworks Clear and accessible. A comprehensive resource for harnessing the power of DuckDB for both novices and experienced professionals. - Qiusheng Wu, Associate Professor, University of Tennessee Excellent! The book all we ducklings have been waiting for! - Gunnar Morling, Decodable

Analyzing Baseball Data with R Third Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps.

An accessible learning resource that develops data analysis skills for natural science students in an efficient style using the R programming language R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists is a compact, example-based, and user-friendly statistics textbook without unnecessary frills, but instead filled with engaging, relatable examples, practical tips, online exercises, resources, and references to extensions, all on a level that follows contemporary curricula taught in large parts of the world. The content structure is unique in the sense that statistical skills are introduced at the same time as software (programming) skills in R. This is by far the best way of teaching from the authors’ experience. Readers of this introductory text will find: Explanations of statistical concepts in simple, easy-to-understand language A variety of approaches to problem solving using both base R and tidyverse Boxes dedicated to specific topics and margin text that summarizes key points A clearly outlined schedule organized into 12 chapters corresponding to the 12 semester weeks of most universities While at its core a traditional printed book, R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists comes with a wealth of online teaching material, making it an ideal and efficient reference for students who wish to gain a thorough understanding of the subject, as well as for instructors teaching related courses.