‘Kiko Llaneras is one of the most consistently interesting data journalists to have emerged in the last decade’John Burn-Murdoch, Chief data reporter, Financial TimesIt’s almost impossible to feel confident we’ve made the right choice when we are constantly faced with conflicting data. From choosing the right school for our children to understanding major social and political events, making informed decisions can feel overwhelming. In a world overflowing with data, it is more important than ever to decipher what the numbers tell us.Drawing on years of work interpreting and explaining data about almost anything, renowned data journalist Kiko Llaneras offers practical tools and shortcuts to help you understand the numbers and make better decisions using a quantitative lens. Through a series of compelling examples, he reveals some of the surprising insights that a data-driven perspective can offer such as:What caused the Chernobyl disasterHow Barack Obama made decisions and slept so peacefully during his presidencyWhy so many footballers are born in JanuaryAn essential guide for decoding the complexities of our modern world, Think Clearly will transform the way you view and use data in every aspect of your life. Read more

Make better decisions with this deconstructed guide to data analyticsWant to add data analytics to your skill stack? Having trouble finding where to start?Cell by cell, bit by bit, this book teaches you the vocabulary, tools, and basic algorithms to think like a data scientist. Using the "Lego set" approach, each chapter adds and connects individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems.Who is This Book For?This book is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this book is for you.Practical ApproachThis book takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the book, you will have the practical knowledge to tackle real data problems in your organization or daily life.What You Will Learn• How to recognize the common data types every data scientist needs to master• Where to store your data, including Big Data• New trends in data analytics, including what is Alternative Data and why few people know about it!• How to explain the distinction between Data Mining, Machine Learning, and Analytics to your colleagues• When and how to use Regression Analysis, Classification, Clustering, Association Analysis, and Natural Language Processing• How to make better business decisions using Data Visualization and Business IntelligenceJoin this introductory course by buying a copy now or sending a free sample to your chosen device. Read more

Transform how you think about problems, prepare to outsmart misinformation, and avoid common pitfalls.Unlock the secrets to smarter decisions and uncover hidden data traps by developing an analytical mindset that cuts through the noise.In an age where data and statistical claims surround us, the ability to critique them is essential. Thinking Analytically guides you through the complexities of real-world information. Offering more than just interpretation, it focuses on broader issues of how to think about and solve problems effectively.Thinking Analytically is for anyone who wants to be an informed consumer of information. Decision-makers, data analysts, scientists, and interested readers will find its insights valuable. The book offers practical techniques and examples for critiquing findings, understanding the processes behind data, and unraveling problems.This book introduces the Thinking Analytically pyramid, covering:Cognitive Foundation: Understand how cognitive limitations impact data interpretation.Probability Mindset: Improve probabilistic thinking and avoid common errors.Data Quality: Learn how subtle problems can distort the results.Experimental Design: Explore how context affects the conclusions you can draw.Analytics: Produce sound results by integrating all pyramid levels.You'll develop a critical eye for evaluating data and the skills to ask the right questions. Whether you're making business decisions, interpreting studies, or navigating everyday information, this book improves your analytical understanding.The author has analyzed data for decades and has written an engaging, accessible book to help anyone think analytically and actively critique data and analyses. Read more

THE SUNDAY TIMES BESTSELLER AND A FINANCIAL TIMES, SUNDAY TIMES AND TELEGRAPH BOOK OF THE YEAR 2024'The best book on football I have ever read' Daniel Finkelstein'Fascinating and educational. An enjoyable and informative read' Sir Kenny Dalglish'Deserves a place among the great modern books on football' Sam Wallace, chief football writer, Telegraph______________________________The insider account of the data revolution that has swept through the modern football world written by one of its key architects, Ian Graham.Between 2012 and 2023, Ian Graham worked as Liverpool FC's Director of Research. His tenure coincided with the club’s greatest period of success since the 1980s, including winning the Premier League in 2020 – Liverpool’s first league title after an agonising three decades.Here for the first time, Graham reveals the fascinating data that informed some of the club’s most pivotal moments of the past decade, from the appointment of Jurgen Klopp as manager in 2015 to the signing of Mohamed Salah in 2017. Along the way, he shares groundbreaking insight into the modern game, including how a season largely played behind closed doors transformed our understanding of home team advantage, or why the GOAT (greatest of all time) might not be who you think. And, in a game that is increasingly dominated by an elite few, Graham charts a path for the future where a data-savvy competitor will always find the edge.______________________________'A fascinating, witty and remarkably insightful account of Liverpool's reinvention and a clear-eyed, detailed explanation of the inner workings of modern football. Nobody is better qualified to tell either of those stories than Ian Graham, who stands as one of the most (quietly) significant figures in the recent history of the club and the sport' Rory Smith, author of Expected Goals and Chief Soccer Correspondent, New York Times'Never before has the data revolution in football been described so well from the inside.' Christoph Biermann, author of Football Hackers‘How to Win the Premier League gives you three books for the price of one: a book about all of the silly inefficiencies plaguing European football, a book about what it's like to be empowered to use outside-the-box thinking to help turn your boyhood club into the best team in the world, and a book about how football actually works. If he really wanted to, Ian could easily put me out of a job.’Ryan O’Hanlon, author of Net Gains and staff writer ESPN*A Sunday Times bestseller August 2024 Read more

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology.This book will help you:Get a concise overview of the entire data engineering landscapeAssess data engineering problems using an end-to-end framework of best practicesCut through marketing hype when choosing data technologies, architecture, and processesUse the data engineering lifecycle to design and build a robust architectureIncorporate data governance and security across the data engineering lifecycle Read more

THE THOUGHT BOOK OF THE YEAR, THE TIMESA GUARDIAN BOOK OF THE YEAR'Game of Thrones-style intra-elite conflict meets big data' TLS'Extraordinary. . . the culmination of many years of highly original and innovative work' BloombergOne of the most iconoclastic thinkers of our time offers a brilliant new theory of how society works What leads to political turbulence and social breakdown? How do elites maintain their dominant position? And why do ruling classes sometimes suddenly lose their grip on power?For decades, complexity scientist Peter Turchin has been studying world history like no-one else. Assembling vast databases mined from 10,000 years of human activity, and then developing new models, he has transformed the way we learn from the past. End Times is the result: a ground-breaking account of how society works.The lessons, he argues, are clear. When the balance of power between the ruling class and the majority tips too far in favour of elites, income inequality surges. The rich get richer, the poor further impoverished. As more people try to join the elite, frustration with the establishment brims over, often with disastrous consequences. Elite overproduction led to state breakdown in imperial China, in medieval France, in the American Civil War - and it is happening now.But while we are far along the path toward violent political rupture, Turchin's models also light the way to a brighter future. Drawing insight from those occasions in history where the balance was restored, End Times also points towards a different future: an escape from the patterns of the past.BEST BOOKS OF SUMMER 2023: THE GUARDIAN * THE TIMES * SUNDAY TIMES Read more

A practical guide to making good decisions in a world of missing dataIn the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see.Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don’t know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions.Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones. Read more

Packed with hands-on exercises and real-world examples, "Python for Beginners 2024" is the ultimate guide to mastering Python programming from scratch. This comprehensive resource is designed to help new programmers gain a solid foundation in Python, covering essential concepts, data types, functions, and object-oriented programming.Benefits to buyers:- Learn Python from the ground up with a practical, easy-to-follow approach- Develop problem-solving skills with hands-on exercises and projects- Understand how to work with data, files, and external libraries- Discover how to build robust, efficient, and well-documented code- Gain a competitive edge in the job market with Python skills- Stay up-to-date with the latest Python developments and best practices- Join a community of millions of Python developers worldwideWhat's inside:- Setting up your Python environment and tools- Basic syntax, data types, and operators- Control structures, functions, and modules- Object-oriented programming and data structures- File input/output, persistence, and networking- Best practices, debugging, and testingPerfect for:- Complete beginners with no prior programming experience- Aspiring developers, data scientists, and analysts- Students, researchers, and professionals seeking a new skill- Anyone looking to automate tasks, build projects, or explore AI and machine learningStart your Python journey today and unlock a world of possibilities! Read more

Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.In Data Analysis with Python and PySpark you will learn how to:     Manage your data as it scales across multiple machines     Scale up your data programs with full confidence     Read and write data to and from a variety of sources and formats     Deal with messy data with PySpark’s data manipulation functionality     Discover new data sets and perform exploratory data analysis     Build automated data pipelines that transform, summarize, and get insights from data     Troubleshoot common PySpark errors     Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. About the technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the book Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's inside     Organizing your PySpark code     Managing your data, no matter the size     Scale up your data programs with full confidence     Troubleshooting common data pipeline problems     Creating reliable long-running jobs About the reader Written for data scientists and data engineers comfortable with Python. About the author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Table of Contents 1 Introduction PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK 2 Your first data program in PySpark 3 Submitting and scaling your first PySpark program 4 Analyzing tabular data with pyspark.sql 5 Data frame gymnastics: Joining and grouping PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE 6 Multidimensional data frames: Using PySpark with JSON data 7 Bilingual PySpark: Blending Python and SQL code 8 Extending PySpark with Python: RDD and UDFs 9 Big data is just a lot of small data: Using pandas UDFs 10 Your data under a different lens: Window functions 11 Faster PySpark: Understanding Spark’s query planning PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK 12 Setting the stage: Preparing features for machine learning 13 Robust machine learning with ML Pipelines 14 Building custom ML transformers and estimators Read more

This constantly evolving and updated book continues to fill the need for a concise and conversational book on the hot and growing field of Data Science. Easy to read and informative, this lucid and constantly updated book covers everything important, with concrete examples, and invites the reader to join this field.This edition, revised last in April 2023, includes Data Lakes, Recommendation Engines, ChatGPT, types of AI system such as Transformer systems, and a Sample Data Mining Project report. it also has a brand new chapter on Data Wrangling, which takes up 80-90% of a Data Science Project.Many top public US universities (e.g. Texas, North Carolina, Minnesota) call it #1 read for Data Analysts.https://techbootcamps.utexas.edu/blog/4-books-every-data-analyst-read/https://bootcamp.unc.edu/blog/7-data-analytics-books-you-should-read-in-2019/https://bootcamp.umn.edu/blog/7-data-analytics-books-you-should-read-in-2019/University of California at Berkeley lists it in top 10.https://bootcamp.berkeley.edu/blog/17-data-analytics-books-you-should-read/The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid tool-set of the major data mining techniques and platforms. The 2025 edition has a summary chapter that encapsulates the entire book in just main 50 points in a few pages. Finally, it includes a tutorial for R and a tutorial for Python. It contains expanded primers on Big Data, Artificial Intelligence, Data Science careers, and Data Ownership and Privacy. The 2025 edition is updated with relationship to Artificial Intelligence in many ways. It includes topics such as Data Lakes, and Data sharing practices. This constantly evolving book has proved very popular throughout the world. Dozens of universities around the world have adopted it as a textbook for their courses. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again as a reference book for insights and techniques. Thank you!Table of Contents Chapter 1: Wholeness of Data Analytics Chapter 2: Business Intelligence Concepts & Applications Chapter 3: Data Warehousing Chapter 4: Data Mining Chapter 5: Data Visualization Chapter 6: Decision Trees Chapter 7: Regression Models Chapter 8: Artificial Neural Networks Chapter 9: Cluster Analysis Chapter 10: Association Rule Mining Chapter 11: Text Mining Chapter 12: Naïve Bayes Analysis Chapter 13: Support Vector Machines Chapter 14: Web Mining Chapter 15: Social Network Analysis Chapter 16: Big Data Chapter 17: Data Modeling Chapter 18: Statistics Chapter 19: Artificial Intelligence Chapter 20: Data Ownership and Privacy Chapter 21: Data Science Careers 22: Main Points of Data Analytics 23. Data Wrangling. Appendix R: Data Mining Tutorial using R Appendix P: Data Mining Tutorial using Python Appendix T Sample Data Mining Project Read more

Data API Management: Unlocking the Power of Data Integration and Efficiency is a comprehensive guide to mastering the complexities of managing data APIs in modern digital ecosystems. As organizations increasingly rely on data-driven decision-making and interconnected systems, the ability to efficiently manage APIs becomes crucial for ensuring seamless data flow, security, and scalability.This book explores the essential concepts, best practices, and tools required to design, implement, and optimize data APIs, from the basics of API architecture to advanced topics such as API security, performance tuning, and integration with cloud services. Whether you're a developer, architect, or IT manager, you'll gain valuable insights into the role of APIs in enabling business agility, connecting disparate data sources, and supporting innovation.Through practical case studies, you will learn how organizations across various industries have successfully implemented data APIs to manage complex data workflows, ensure compliance, and scale their operations. You'll also explore emerging trends in API management, including the rise of GraphQL, serverless computing, and edge computing, helping you stay ahead of the curve.Packed with actionable advice, real-world examples, and a focus on hands-on implementation, Data API Management is an indispensable resource for anyone looking to harness the full potential of their data through effective API management.Unlock the power of data integration and efficiency with this authoritative guide to Data API Management. Read more

R for Data Analysis: A Beginner's Guide to Statistical Modeling and VisualizationUnlock the power of R for data analysis with R for Data Analysis: A Beginner's Guide to Statistical Modeling and Visualization. This comprehensive guide is designed for beginners and aspiring data scientists who want to master data analysis with R, one of the most powerful and widely used programming languages for statistical computing and visualization.Whether you're a student, researcher, or professional, this book will equip you with essential skills to clean, manipulate, visualize, and model data effectively. With a hands-on approach, you’ll learn how to transform raw datasets into meaningful insights using R programming for data science.What You’ll Learn:✅ Set up your R environment with RStudio and essential packages like Tidyverse.✅ Master data manipulation with dplyr, tidyr, and data.table.✅ Visualize data beautifully using ggplot2, plotly, and advanced charting techniques.✅ Perform Exploratory Data Analysis (EDA) to uncover trends and patterns.✅ Build statistical models, including linear regression, hypothesis testing, and time series forecasting.✅ Work with large datasets efficiently and automate data workflows.✅ Integrate R with SQL for database-driven analysis.✅ Explore real-world case studies in finance, healthcare, marketing, and social media.Why Choose This Book?✔ Beginner-friendly with step-by-step tutorials.✔ Practical examples & hands-on exercises to reinforce learning.✔ Real-world applications for business and research.✔ Covers automation, web scraping, and advanced R techniques.By the end of this book, you’ll have the confidence to analyze complex datasets, create compelling visualizations, and apply statistical modeling techniques in real-world scenarios. Whether you're working on data analytics with R for business, research, or personal projects, this guide is your ultimate companion to R programming for data analysis.Start your journey today and master data analysis with R! 🚀 Read more

Are You Ready to Transform Your Career with Python and Stand Out in the Competitive Tech Industry?If you want to master Python from scratch, boost your programming skills, and secure high-paying job opportunities, “The Python Bible for Beginners” is the guide you've been waiting for.Tired of getting lost in complex tutorials without clear instructions? Frustrated by learning theoretical concepts that don't translate to solving real-world problems? Worried that the information you're getting isn't up-to-date in the fast-evolving tech world? You're not alone.This comprehensive book eliminates these hurdles by offering a clear, step-by-step approach, ensuring you won't just learn Python—you'll master it. Updated and revised as of June 2024, every tutorial is current, accurate, and ready to make you a sought-after programming professional.Friendly, Jargon-Free Language: Designed for complete beginners, with easy-to-understand explanations.Step-by-Step Guidance: Learn from the basics and progress to advanced topics with confidence.All-In-One Solution: Structured like a true university course, covering basics to advanced techniques, including powerful libraries such as TensorFlow, NumPy, Sklearn, Keras, Matplotlib, and more.Practical Learning: Code snippets, hands-on exercises, and real-world examples to solidify your understanding and skills.Updated Content (June 2024): Fully revised and updated to ensure accuracy and relevance in today's tech landscape.🎁 TODAY WITH POWERFUL EXTRA CONTENT INCLUDED:Exclusive access to the most complete online directory of Python exercises (with solutions).The Python Interview Handbook: The ultimate list of questions and answers to crack your next coding interview and secure a position in a high-paying tech company.A Comprehensive Python Cheat Sheet: The ultimate Python reference to lower the entry barrier for newcomers and help veterans refresh old tricks.In just 7 days, you’ll be able to effortlessly write Python code, solve complex problems, and impress top tech companies with your skills. Imagine the confidence you'll feel when you can tackle real-world computational challenges and stand out in the job market.Don’t wait any longer to achieve your programming dreams.Buy your copy today and start your journey towards becoming the expert that top companies vie to hire! Read more

Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology.You will:Explore machine learning, including distributed computing concepts and terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture Read more

In today’s data-driven world, the products we use every day are increasingly powered by data, not just traditional code and features. Data Product Thinking: Crafting Intelligent Products in a Data-Driven World offers a comprehensive guide to designing, building, and scaling products that leverage the power of data to create personalized, impactful user experiences.This book introduces the concept of Data Product Thinking—a methodology that blends product development with advanced data science techniques. It explores the key components necessary for building data products, from developing a strong data strategy to integrating machine learning and AI. Readers will learn how to build dynamic products that continuously learn and improve over time by embedding data-driven insights into every aspect of the product lifecycle.With practical frameworks, actionable strategies, and real-world case studies from industry leaders like Netflix, Spotify, and Google, this book provides insights on creating products that evolve with user behavior and feedback. It also covers the critical role of cross-functional collaboration, data governance, and ethical considerations in crafting data products that users trust and value.Whether you're a product manager, data scientist, designer, or engineer, Data Product Thinking will equip you with the tools and mindset to develop smarter, more adaptive products in a world where data is the key to innovation. Explore how you can harness the full potential of data to create products that are not just functional but intelligent, and that grow more valuable with each interaction. Read more

Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you.Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies.Build more trustworthy and reliable data pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityLearn how to set and maintain data SLAs, SLIs, and SLOsDevelop and lead data quality initiatives at your companyLearn how to treat data services and systems with the diligence of production softwareAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets Read more

Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world's population, the amount of data generated each day became huge, and continues to grow exponentially. It includes all our uploaded documents, video, and photos, all our social media traffic, our online shopping, even the GPS data from our cars.'Big Data' represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analysed, and exploited by a variety of bodies from big companies to organizations concerned with disease control. Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, data security, and domestic smart devices which can be hijacked by hackers. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable. Read more

Data Science in 30 Days: The Complete Beginner’s Guide is the ultimate hands-on roadmap for anyone looking to master data science for dummies and transition into the world of data-driven decision-making. Designed to take you from an absolute beginner to a confident data practitioner in just 30 days, this book provides a structured, easy-to-follow approach to learning data science for beginners efficiently.With step-by-step lessons, real-world examples, and hands-on exercises, this book is perfect for those looking for a Python data science book that balances theory with practical applications. Whether you are a student, professional, or hobbyist, you’ll find everything you need to get started with data science projects with Python and apply it in various domains such as business analytics, finance, artificial intelligence, and big data processing.What You’ll Learn in 30 Days:✔ The fundamentals of data science, including data collection, cleaning, and preprocessing✔ How to use Python, NumPy, Pandas, and Matplotlib for Python for data science tasks✔ Exploratory data analysis (EDA) techniques to uncover insights from data✔ Introduction to statistics, probability, and hypothesis testing for data science for business applications✔ Supervised and unsupervised machine learning models, including regression, classification, and clustering✔ Working with databases, SQL queries, and big data tools like Apache Spark✔ Advanced topics such as data science for finance, deep learning, NLP, time series analysis, and model deploymentWhy This Book Stands Out:✔ A structured day-by-day learning plan to keep you on track✔ Practical exercises and coding challenges to reinforce each concept✔ Covers both beginner and intermediate topics, making it ideal for continuous learning✔ A bonus section with cheat sheets, common errors, and interview questionsIf you’re looking for the best Python data science book that breaks down complex concepts into digestible lessons, this book is for you. Whether you’re interested in data science projects with Python, preparing for a data science bootcamp, or simply want to explore data science for business in an easy-to-understand manner, this book provides a complete, practical guide.By the end of 30 days, you’ll have the confidence and skills to analyze data, build machine learning models, and derive meaningful insights. If you're looking for the best data science for dummies guide to start your journey, this is it.Start your data science adventure today and become proficient in just 30 days!* 🚀* Read more

"Dive into the future of filmmaking with 'AI and ML Technological Solutions for the Film Industry.' This groundbreaking book explores the fusion of artificial intelligence (AI) and machine learning (ML) with the cinematic world, revealing a revolution that transcends traditional boundaries. From AI-optimized scriptwriting to facial recognition technology shaping actor expressions, the book unravels the transformative impact on storytelling, production, and audience engagement. Explore the realms of intelligent film preservation, joint AI-ML applications, linguistic AI processing, and machine learning in distribution, witnessing the seamless integration of creativity and technology. As the pages unfold, discover a cinematic landscape where algorithms enhance every aspect of the filmmaking process, ushering in a new era of immersive storytelling and unprecedented possibilities. 'AI and ML Technological Solutions for the Film Industry' is a captivating exploration for film enthusiasts, industry professionals, and tech aficionados alike." Read more

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates Read more