Become data-savvy with the widely used data and AI software Data and analytics are essential for any business, giving insight into what's working, what can be improved, and what else needs to be done. SAS software helps you make sure you're doing data right, with a host of data management, reporting, and analysis tools. SAS For Dummies teaches you the essentials, helping you navigate this statistical software and turn information into value. In this book, learn how to gather data, create reports, and analyze results. You'll also discover how SAS machine learning and AI can help deliver decisions based on data. Even if you're brand new to data and analytics, this easy-to-follow guide will turn you into an SAS power user. Become familiar with the most popular SAS applications, including SAS 9 and SAS Viya Connect to data, organize your information, and adopt sound data security practices Get a primer on working with data sets, variables, and statistical analysis Explore and analyze data through SAS programming and rich application interfaces Create and share graphs interactive visualizations to deliver insights This is the perfect Dummies guide for new SAS users looking to improve their skills—in any industry and for any organization size.

This book is a comprehensive guide to building sophisticated and robust Power BI solutions that solve common data problems effectively. Written with hands-on professionals in mind, it provides essential insights and practical advice to help you choose the right tools and approaches for any BI task. Readers will learn to create performant, secure, and innovative business intelligence systems. What this Book will help me do Identify the scenarios where each Power BI component fits best. Apply secure and performance-conscious design principles when building BI solutions. Leverage Microsoft Fabric and other advanced integrations to maximize Power BI's capabilities. Implement AI-powered features such as Copilot and predictive modeling in Power BI. Facilitate collaboration and governance using Power BI's advanced features. Author(s) Nagaraj Venkatesan has over 17 years of professional expertise in data platform technologies and business intelligence tools. Through a rich career in data solution architecture, he has mastered the art of designing efficient and reliable Power BI implementations. This book reflects his passion for empowering professionals to make the most of Power BI. Who is it for? If you are a solution architect, data engineer, or Power BI report developer looking to elevate your skills in designing optimized Power BI solutions, this book is for you. Business analysts and data scientists can also benefit immensely from the book's coverage of self-service BI and data science integration. Some familiarity with Power BI will enhance your learning experience, but newcomers eager to learn will also find it invaluable.

This book takes an advanced dive into using Tableau for professional data visualization and analytics. You will learn techniques for crafting highly interactive dashboards, optimizing their performance, and leveraging Tableau's APIs and server features. With a focus on real-world applications, this resource serves as a guide for professionals aiming to master advanced Tableau skills. What this Book will help me do Build robust, high-performing Tableau data models for enterprise analytics. Use advanced geospatial techniques to create dynamic, data-rich mapping visualizations. Leverage APIs and developer tools to integrate Tableau with other platforms. Optimize Tableau dashboards for performance and interactivity. Apply best practices for content management and data security in Tableau implementations. Author(s) Pablo Sáenz de Tejada and Daria Kirilenko are seasoned Tableau experts with vast professional experience in implementing advanced analytics solutions. Pablo specializes in enterprise-level dashboard design and has trained numerous professionals globally. Daria focuses on integrating Tableau into complex data ecosystems, bringing a practical and innovative approach to analytics. Who is it for? This book is tailored for professionals such as Tableau developers, data analysts, and BI consultants who already have a foundational knowledge of Tableau. It is ideal for those seeking to deepen their skills and gain expertise in tackling advanced data visualization challenges. Whether you work in corporate analytics or enjoy exploring data in your own projects, this book will enhance your Tableau proficiency.

This book covers the science of measuring the invisible building blocks of thought processes that are useful for understanding humans, including technology users, media consumers, and consumers of goods and services. It provides: An explanation of what self-report measurement entails for beginners; A clear set of assumptions needed in order for self-report measures to yield valuable information; A mindset that needs to be adopted when using self-report measurement in the contexts of surveys and experiments; Guidance for extracting opinion from social media text content and integrating AI; A roadmap for quantifying the errors associated with self-report measurement.

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.

Time Series Analysis with Spark provides a practical introduction to leveraging Apache Spark and Databricks for time series analysis. You'll learn to prepare, model, and deploy robust and scalable time series solutions for real-world applications. From data preparation to advanced generative AI techniques, this guide prepares you to excel in big data analytics. What this Book will help me do Understand the core concepts and architectures of Apache Spark for time series analysis. Learn to clean, organize, and prepare time series data for big data environments. Gain expertise in choosing, building, and training various time series models tailored to specific projects. Master techniques to scale your models in production using Spark and Databricks. Explore the integration of advanced technologies such as generative AI to enhance predictions and derive insights. Author(s) Yoni Ramaswami, a Senior Solutions Architect at Databricks, has extensive experience in data engineering and AI solutions. With a focus on creating innovative big data and AI strategies across industries, Yoni authored this book to empower professionals to efficiently handle time series data. Yoni's approachable style ensures that both foundational concepts and advanced techniques are accessible to readers. Who is it for? This book is ideal for data engineers, machine learning engineers, data scientists, and analysts interested in enhancing their expertise in time series analysis using Apache Spark and Databricks. Whether you're new to time series or looking to refine your skills, you'll find both foundational insights and advanced practices explained clearly. A basic understanding of Spark is helpful but not required.

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.

"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies." The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs. This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights. ● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting. ● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. ● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting. ● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. ● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions. Who this book is for: Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.

Learn the technical and soft skills you need to succeed in your career as a data analyst. You’ve learned how to use Python, R, SQL, and the statistical skills needed to get started as a data analyst—so, what’s next? Effective Data Analysis bridges the gap between foundational skills and real-world application. This book provides clear, actionable guidance on transforming business questions into impactful data projects, ensuring you’re tracking the right metrics, and equipping you with a modern data analyst’s essential toolbox. In Effective Data Analysis, you’ll gain the skills needed to excel as a data analyst, including: Maximizing the impact of your analytics projects and deliverables Identifying and leveraging data sources to enhance organizational insights Mastering statistical tests, understanding their strengths, limitations, and when to use them Overcoming the challenges and caveats at every stage of an analytics project Applying your expertise across a variety of domains with confidence Effective Data Analysis is full of sage advice on how to be an effective data analyst in a real production environment. Inside, you’ll find methods that enhance the value of your work—from choosing the right analysis approach, to developing a data-informed organizational culture. About the Technology Data analysts need top-notch knowledge of statistics and programming. They also need to manage clueless stakeholders, navigate messy problems, and advocate for resources. This unique book covers the essential technical topics and soft skills you need to be effective in the real world. About the Book Effective Data Analysis helps you lock down those skills along with unfiltered insight into what the job really looks like. You’ll build out your technical toolbox with tips for defining metrics, testing code, automation, sourcing data, and more. Along the way, you’ll learn to handle the human side of data analysis, including how to turn vague requirements into efficient data pipelines. And you’re sure to love author Mona Khalil’s illustrations, industry examples, and a friendly writing style. What's Inside Identify and incorporate external data Communicate with non-technical stakeholders Apply and interpret statistical tests Techniques to approach any business problem About the Reader Written for early-career data analysts, but useful for all. About the Author Mona Khalil is the Senior Manager of Analytics Engineering at Justworks. Quotes Your roadmap to becoming a standout data analyst! An intriguing blend of technical expertise and practical wisdom. - Chester Ismay, MATE Seminars A thoughtful guide to delivering real-world data analysis. It will be an eye-opening read for all data professionals! - David Lee, Justworks Inc. Compelling insights into the relationship between organizations and data. The real-life examples will help you excel in your data career. - Jeremy Moulton, Greenhouse Mona’s wide range of experience shines in her thoughtful, relevant examples. - Jessica Cherny, Fivetran

Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects. API skills have become essential for AI and data science success, because they are used in a variety of ways in these fields. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit. As you complete the chapters in the book, you'll be creating portfolio projects that teach you how to: Design APIs that data scientists and AIs love Develop APIs using Python and FastAPI Deploy APIs using multiple cloud providers Create data science projects such as visualizations and models using APIs as a data source Access APIs using generative AI and LLMs

Master the art of designing and implementing analytics solutions using Microsoft Fabric with this comprehensive guide. Whether you're preparing for the DP-600 certification exam or want to advance your career, this book offers expert insights into data analytics in Microsoft environments. What this Book will help me do Confidently pass the DP-600 certification exam by mastering exam-tested skills. Acquire practical expertise in deploying data analytics solutions with Microsoft Fabric. Understand and optimize data integration, security, and performance in analytics systems. Learn advanced techniques including semantic model optimization and advanced SQL querying. Prepare for real-world challenges through mock exams and hands-on exercises. Author(s) Jagjeet Singh Makhija and Charles Odunukwe, authors of this guide, are seasoned Microsoft specialists with decades of experience in data analytics, certification training, and technology consulting. Their clear and methodical approach ensures learners at all levels can grow their expertise. Who is it for? If you're a data analyst or IT professional looking to enhance your skills in analytics and Microsoft's technologies, this book is for you. It's ideal for those pursuing the DP-600 certification or aiming to improve their data integration and analysis capabilities.

The book explores the fundamental principles and transformative advancements in cutting-edge algorithmic technologies, detailing their application and impact on revolutionizing healthcare. This book provides an in-depth account of how technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are reshaping healthcare, transitioning from traditional diagnostic and treatment approaches to data-driven solutions that improve predictive accuracy and patient outcomes. The text also addresses the challenges and considerations associated with adopting these technologies, including ethical implications, data security concerns, and the need for human-centered approaches in algorithmic medicine. After introducing digital twin technology and its potential to enhance healthcare delivery, the book examines the broader effects of digital technology on the healthcare system. Subsequent chapters explore topics such as innovations in medical imaging, predictive analytics for improved patient outcomes, and deep learning algorithms for brain tumor detection. Other topics include generative adversarial networks (GANs), convolutional neural networks (CNNs), smart wearables for remote patient monitoring, effective IoT solutions, telemedicine advancements, and blockchain security for healthcare systems. The integration of biometric systems driven by AI, securing cyber-physical systems in healthcare, and digitizing wellness through electronic health records (EHRs) and electronic medical records (EMRs) are also discussed. The book concludes with an extensive case study comparing the impacts of various healthcare applications, offering insights and encouraging further research and innovation in this dynamic field. Audience This book is suitable for academicians and professionals in health informatics, bioinformatics, biomedical science and engineering, artificial intelligence, as well as clinicians, IT specialists, and policymakers in healthcare.

Complete eight data science projects that lock in important real-world skills—along with a practical process you can use to learn any new technique quickly and efficiently. Data analysts need to be problem solvers—and The Well-Grounded Data Analyst will teach you how to solve the most common problems you'll face in industry. You'll explore eight scenarios that your class or bootcamp won’t have covered, so you can accomplish what your boss is asking for. In The Well-Grounded Data Analyst you'll learn: High-value skills to tackle specific analytical problems Deconstructing problems for faster, practical solutions Data modeling, PDF data extraction, and categorical data manipulation Handling vague metrics, deciphering inherited projects, and defining customer records The Well-Grounded Data Analyst is for junior and early-career data analysts looking to supplement their foundational data skills with real-world problem solving. As you explore each project, you'll also master a proven process for quickly learning new skills developed by author and Half Stack Data Science podcast host David Asboth. You'll learn how to determine a minimum viable answer for your stakeholders, identify and obtain the data you need to deliver, and reliably present and iterate on your findings. The book can be read cover-to-cover or opened to the chapter most relevant to your current challenges. About the Technology Real world data analysis is messy. Success requires tackling challenges like unreliable data sources, ambiguous requests, and incompatible formats—often with limited guidance. This book goes beyond the clean, structured examples you see in classrooms and bootcamps, offering a step-by-step framework you can use to confidently solve any data analysis problem like a pro. About the Book The Well-Grounded Data Analyst introduces you to eight scenarios that every data analyst is bound to face. You’ll practice author David Asboth’s results-oriented approach as you model data by identifying customer records, navigate poorly-defined metrics, extract data from PDFs, and much more! It also teaches you how to take over incomplete projects and create rapid prototypes with real data. Along the way, you’ll build an impressive portfolio of projects you can showcase at your next interview. What's Inside Deconstructing problems Handling vague metrics Data modeling Categorical data manipulation About the Reader For early-career data scientists. About the Author David Asboth is a data generalist educator, and software architect. He co-hosts the Half Stack Data Science podcast. Quotes Well reasoned and well written, with approaches to solve many sorts of data analysis problems. - Naomi Ceder, Fellow of the Python Software Foundation An excellent resource for any aspiring data scientist! - Andrew R. Freed, IBM David’s clear and repeatable framework will give you confidence to tackle open-ended stakeholder requests and reach an answer much faster! - Shaun McGirr, DevOn Software Services A book version of shadowing a senior data analyst while they explain handling frequent data problems at work, including all the ugly gotchas. - Randy Au, Google

When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning. A/B tests or randomized controlled trials are expensive and often unfeasible in a business environment. Causal Inference for Data Science reveals the techniques and methodologies you can use to identify causes from data, even when no experiment or test has been performed. In Causal Inference for Data Science you will learn how to: Model reality using causal graphs Estimate causal effects using statistical and machine learning techniques Determine when to use A/B tests, causal inference, and machine learning Explain and assess objectives, assumptions, risks, and limitations Determine if you have enough variables for your analysis It’s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You’ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions. About the Technology Why did you get a particular result? What would have lead to a different outcome? These are the essential questions of causal inference. This powerful methodology improves your decisions by connecting cause and effect—even when you can’t run experiments, A/B tests, or expensive controlled trials. About the Book Causal Inference for Data Science introduces techniques to apply causal reasoning to ordinary business scenarios. And with this clearly-written, practical guide, you won’t need advanced statistics or high-level math to put causal inference into practice! By applying a simple approach based on Directed Acyclic Graphs (DAGs), you’ll learn to assess advertising performance, pick productive health treatments, deliver effective product pricing, and more. What's Inside When to use A/B tests, causal inference, and ML Assess objectives, assumptions, risks, and limitations Apply causal inference to real business data About the Reader For data scientists, ML engineers, and statisticians. About the Author Aleix Ruiz de Villa Robert is a data scientist with a PhD in mathematical analysis from the Universitat Autònoma de Barcelona. Quotes With intuitive explanations, application-focused insights, and real-world examples, this book offers immense practical value. - Philipp Bach, Maintainer of the DoubleML libraries for Python and R An essential guide for navigating the complexities of real-world data analysis. - Adi Shavit, SWAPP A must-read! Demystifies causal inference with a blend of theory and practice. - Karan Gupta, SunPower Corporation Causal relationships can mask and distort results. This book provides a set of tools to extract insights correctly. - Peter V. Henstock, Harvard Extension

Gain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background. The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models. After reading the book, you will be able to put predictive analytics principles into practice. What You Will Learn Understand modeling, estimating, and evaluating models for forecasting Implement Partial F-Test and Variable Selection Method Demonstrate each analysis model in R Studio and SAS Understand SLR and MLR Analysis models Who This Book Is For Students and professionals in the field of data analysis and intelligence applications

CLEAR AND CONCISE TECHNIQUES FOR USING ANALYTICS TO DELIVER BUSINESS IMPACT AT ANY ORGANIZATION Organizations have more data at their fingertips than ever, and their ability to put that data to productive use should be a key source of sustainable competitive advantage. Yet, business leaders looking to tap into a steady and manageable stream of “actionable insights” often, instead, get blasted with a deluge of dashboards, chart-filled slide decks, and opaque machine learning jargon that leaves them asking, “So what?” Analytics the Right Way is a guide for these leaders. It provides a clear and practical approach to putting analytics to productive use with a three-part framework that brings together the realities of the modern business environment with the deep truths underpinning statistics, computer science, machine learning, and artificial intelligence. The result: a pragmatic and actionable guide for delivering clarity, order, and business impact to an organization’s use of data and analytics. The book uses a combination of real-world examples from the authors’ direct experiences—working inside organizations, as external consultants, and as educators—mixed with vivid hypotheticals and illustrations—little green aliens, petty criminals with an affinity for ice cream, skydiving without parachutes, and more—to empower the reader to put foundational analytical and statistical concepts to effective use in a business context.

Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance. This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models. By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will Learn Understand the fundamentals of linear regression and its applications in financial data analysis and prediction Apply generalized linear models for handling various types of data distributions and enhancing model flexibility Gain insights into regime switching models to capture different market conditions and improve financial forecasting Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications Who This Book Is For Data scientists, machine learning engineers, finance professionals, and software engineers

As businesses increasingly rely on data to drive decisions, the role of advanced analytics and AI in enhancing data interpretation is becoming crucial. For professionals tasked with optimizing data analytics platforms like Tableau, staying ahead of the curve with the latest tools isn't just beneficial—it's essential. This insightful guide takes you through the integration of Tableau Pulse and Einstein Copilot, explaining their roles within the broader Tableau and Salesforce ecosystems. Author Ann Jackson, an esteemed analytics professional with a deep expertise in Tableau, offers a step-by-step exploration of these tools, backed by real-world use cases that demonstrate their impact across various industries. By the end of this book, you will: Understand the functionalities of Tableau Pulse and Einstein Copilot and how to use them Learn to deploy Tableau Pulse effectively, ensuring it aligns with your business objectives Navigate discussions on AI's role within Tableau, enhancing your strategic conversations Visualize how Tableau Pulse operates through detailed images and scenarios Utilize Einstein Copilot in Tableau Desktop/Prep to streamline and enhance data analysis

Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia’s APIs, libraries, and packages. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents. The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners. What You Will Learn Work with Julia types and the different containers for rapid development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Use C/C++, Python or R libraries in Julia and embed Julia in other code. Optimize performance with GPU programming, profiling and more. Manage, prepare, analyse and visualise your data with DataFrames and Plots Implement complete ML workflows with BetaML, from data coding to model evaluation, and more. Who This Book Is For Experienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.