In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization. As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional—it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models. Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today. In this book, you will learn: The basics of foundational statistical modeling with Tableau How to prove your analysis is statistically significant How to calculate and interpret confidence intervals Best practices for incorporating statistics into data visualizations How to connect external analytics resources from Tableau using R and Python

Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable.

Data Science Fundamentals with R, Python, and Open Data Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projects Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers’ active learning. Each chapter presents one or more case studies. Written by a highly qualified academic, Data Science Fundamentals with R, Python, and Open Data discuss sample topics such as: Data organization and operations on data frames, covering reading CSV dataset and common errors, and slicing, creating, and deleting columns in R Logical conditions and row selection, covering selection of rows with logical condition and operations on dates, strings, and missing values Pivoting operations and wide form-long form transformations, indexing by groups with multiple variables, and indexing by group and aggregations Conditional statements and iterations, multicolumn functions and operations, data frame joins, and handling data in list/dictionary format Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to computer science, and medical fields using stochastic and quantitative models.

In "Extending Power BI with Python and R," you'll learn how to enhance your Power BI reports and analyses by leveraging the advanced analytical capabilities of Python and R. From working with large datasets to creating sophisticated visuals, this book provides practical instructions on powerful techniques that unlock new possibilities in Power BI. What this Book will help me do Configure and optimize Python and R integration in Power BI for enhanced performance. Implement advanced data transformation techniques to overcome Power BI limitations. Develop advanced visualizations using the Grammar of Graphics in Python and R. Analyze data leveraging powerful Python and R algorithms, including machine learning models. Secure your Power BI data with anonymization and pseudonymization techniques. Author(s) None Zavarella is a data analytics expert with years of practical experience in business intelligence and data analytics. With a passion for enhancing data tools with programming languages like Python and R, they bring practical knowledge and technical acumen to this comprehensive resource. They aim to make complex concepts approachable to their readers. Who is it for? This book is aimed at professionals such as business analysts, business intelligence specialists, and data scientists who leverage Power BI for their data solutions. Readers should have a working knowledge of Power BI basics and a desire to extend its capabilities. A familiarity with Python and R programming basics is also beneficial for following the advanced techniques presented.

"Cracking the Data Science Interview" is your ultimate resource for preparing for roles in the competitive field of data science. With this book, you'll explore essential topics such as Python, SQL, statistics, and machine learning, as well as learn practical skills for building portfolios and acing interviews. Follow its guidance and you'll be equipped to stand out in any data science interview. What this Book will help me do Confidently explain complex statistical and machine learning concepts. Develop models and deploy them while ensuring version control and efficiency. Learn and apply scripting skills in shell and Bash for productivity. Master Git workflows to handle collaborative coding in projects. Perfectly tailor portfolios and resumes to land data science opportunities. Author(s) Leondra R. Gonzalez, with years of data science and mentorship experience, co-authors this book with None Stubberfield, a seasoned expert in technology and machine learning. Together, they integrate their expertise to provide practical advice for navigating the data science job market. Who is it for? If you're preparing for data science interviews, this book is for you. It's ideal for candidates with a foundational knowledge of Python, SQL, and statistics looking to refine and expand their technical and professional skills. Professionals transitioning into data science will also find it invaluable for building confidence and succeeding in this rewarding field.

If programming is magic, then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. This thoroughly updated third edition not only introduces you to web scraping but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter. Parse complicated HTML pages Develop crawlers with the Scrapy framework Learn methods to store the data you scrape Read and extract data from documents Clean and normalize badly formatted data Read and write natural languages Crawl through forms and logins Scrape JavaScript and crawl through APIs Use and write image-to-text software Avoid scraping traps and bot blockers Use scrapers to test your website

You Will Learn Python! Zed Shaw has created the world's most reliable system for learning Python. Follow it and you will succeed--just like the millions of beginners Zed has taught to date! You bring the discipline, persistence, and attention; the author supplies the masterful knowledge you need to succeed. In Learn Python the Hard Way, Fifth Edition, you'll learn Python by working through 60 lovingly crafted exercises. Read them. Type in the code. Run it. Fix your mistakes. Repeat. As you do, you'll learn how a computer works, how to solve problems, and how to enjoy programming . . . even when it's driving you crazy. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Strings and text Interact with users Work with files Looping and logic Object-oriented programming Data structures using lists and dictionaries Modules, classes, and objects Python packaging Automated testing Basic SQL for Data Science Web scraping Fixing bad data (munging) The "Data" part of "Data Science" It'll be frustrating at first. But if you keep trying, you'll get it--and it'll feel amazing! This course will reward you for every minute you put into it. Soon, you'll know one of the world's most powerful, popular programming languages. You'll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven't written code in years Aspiring Data Scientists or academics who need to learn to code Seasoned professionals looking for a fast, simple crash course in Python for Data Science Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs. Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You'll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren't available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value. With entity resolution, you'll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of ML and AI. This book covers: Challenges in deduplicating and joining datasets Extracting, cleansing, and preparing datasets for matching Text matching algorithms to identify equivalent entities Techniques for deduplicating and joining datasets at scale Matching datasets containing persons and organizations Evaluating data matches Optimizing and tuning data matching algorithms Entity resolution using cloud APIs Matching using privacy-enhancing technologies

Principles of Data Science offers an end-to-end introduction to data science fundamentals, blending key mathematical concepts with practical programming. You'll learn how to clean and prepare data, construct predictive models, and leverage modern tools like pre-trained models for NLP and computer vision. By integrating theory and practice, this book sets the foundation for impactful data-driven decision-making. What this Book will help me do Develop a solid understanding of foundational statistics and machine learning. Learn how to clean, transform, and visualize data for impactful analysis. Explore transfer learning and pre-trained models for advanced AI tasks. Understand ethical implications, biases, and governance in AI and ML. Gain the knowledge to implement complete data pipelines effectively. Author(s) Sinan Ozdemir is an experienced data scientist, educator, and author with a deep passion for making complex topics accessible. With a background in computer science and applied statistics, Sinan has taught data science at leading institutions and authored multiple books on the topic. His practical approach to teaching combines real-world examples with insightful explanations, ensuring learners gain both competence and confidence. Who is it for? This book is ideal for beginners in data science who want to gain a comprehensive understanding of the field. If you have a background in programming or mathematics and are eager to combine these skills to analyze and extract insights from data, this book will guide you. Individuals working with machine learning or AI who need to solidify their foundational knowledge will find it invaluable. Some familiarity with Python is recommended to follow along seamlessly.

Discover how to navigate the world of Web3 data with 'Data Science for Web3,' an expertly crafted guide by Gabriela Castillo Areco. Through practical examples, industry insights, and real-world use cases, you will learn the skills needed to analyze blockchain data and extract actionable business insights. What this Book will help me do Understand blockchain transactions and data structures to build robust datasets. Leverage on-chain and off-chain data for valuable Web3 business insights. Create DeFi- and NFT-specific datasets for targeted analysis. Develop machine learning models tailored for blockchain use cases. Apply data science techniques to innovate in the Web3 ecosystem. Author(s) Gabriela Castillo Areco is a seasoned data scientist and an expert in blockchain analytics. With years of experience in the technology and finance sectors, Gabriela brings a practical perspective to understanding intricate data within the emerging Web3 paradigm. Her engaging approach makes technical concepts accessible and actionable. Who is it for? This book is ideal for data professionals such as analysts, scientists, or engineers, aiming to harness the potential of blockchain analytics. It's also suitable for business professionals exploring data-driven opportunities within Web3. Whether you're a beginner or an experienced learner with some Python background, this book will meet you at your level.

Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide. In Bayesian Optimization in Action you will learn how to: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian optimization to cost-constrained, multi-objective, and preference optimization Implement Bayesian optimization with PyTorch, GPyTorch, and BoTorch Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn’t have to be difficult! You’ll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting-edge Python libraries. The book’s easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. About the Technology In machine learning, optimization is about achieving the best predictions—shortest delivery routes, perfect price points, most accurate recommendations—in the fewest number of steps. Bayesian optimization uses the mathematics of probability to fine-tune ML functions, algorithms, and hyperparameters efficiently when traditional methods are too slow or expensive. About the Book Bayesian Optimization in Action teaches you how to create efficient machine learning processes using a Bayesian approach. In it, you’ll explore practical techniques for training large datasets, hyperparameter tuning, and navigating complex search spaces. This interesting book includes engaging illustrations and fun examples like perfecting coffee sweetness, predicting weather, and even debunking psychic claims. You’ll learn how to navigate multi-objective scenarios, account for decision costs, and tackle pairwise comparisons. What's Inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the Reader For machine learning practitioners who are confident in math and statistics. About the Author Quan Nguyen is a research assistant at Washington University in St. Louis. He writes for the Python Software Foundation and has authored several books on Python programming. Quotes Using a hands-on approach, clear diagrams, and real-world examples, Quan lifts the veil off the complexities of Bayesian optimization. - From the Foreword by Luis Serrano, Author of Grokking Machine Learning This book teaches Bayesian optimization, starting from its most basic components. You’ll find enough depth to make you comfortable with the tools and methods and enough code to do real work very quickly. - From the Foreword by David Sweet, Author of Experimentation for Engineers Combines modern computational frameworks with visualizations and infographics you won’t find anywhere else. It gives readers the confidence to apply Bayesian optimization to real world problems! - Ravin Kumar, Google

Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines. What You Will Learn Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems Understand the differences between PySpark, scikit-learn, and pandas Perform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySpark Distinguish between the pipelines of PySpark and scikit-learn Who This Book Is For Data scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.

Let Python do the heavy lifting for you as you analyze large datasets Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples. Get a firm background in the basics of Python coding for data analysis Learn about data science careers you can pursue with Python coding skills Integrate data analysis with multimedia and graphics Manage and organize data with cloud-based relational databases Python careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.

In "Hands-On Web Scraping with Python," you'll learn how to harness the power of Python libraries to extract, process, and analyze data from the web. This book provides a practical, step-by-step guide for beginners and data enthusiasts alike. What this Book will help me do Master the use of Python libraries like requests, lxml, Scrapy, and Beautiful Soup for web scraping. Develop advanced techniques for secure browsing and data extraction using APIs and Selenium. Understand the principles behind regex and PDF data parsing for comprehensive scraping. Analyze and visualize data using data science tools such as Pandas and Plotly. Build a portfolio of real-world scraping projects to demonstrate your capabilities. Author(s) Anish Chapagain, the author of "Hands-On Web Scraping with Python," is an experienced programmer and instructor who specializes in Python and data-related technologies. With his vast experience in teaching individuals from diverse backgrounds, Anish approaches complex concepts with clarity and a hands-on methodology. Who is it for? This book is perfect for aspiring data scientists, Python beginners, and anyone who wants to delve into web scraping. Readers should have a basic understanding of how websites work but no prior coding experience is required. If you aim to develop scraping skills and understand data analysis, this book is the ideal starting point.

Streamlit for Data Science is your complete guide to mastering the creation of powerful, interactive data-driven applications using Python and Streamlit. With this comprehensive resource, you'll learn everything from foundational Streamlit skills to advanced techniques like integrating machine learning models and deploying apps to cloud platforms, enabling you to significantly enhance your data science toolkit. What this Book will help me do Master building interactive applications using Streamlit, including techniques for user interfaces and integrations. Develop visually appealing and functional data visualizations using Python libraries in Streamlit. Learn to integrate Streamlit applications with machine learning frameworks and tools like Hugging Face and OpenAI. Understand and apply best practices to deploy Streamlit apps to cloud platforms such as Streamlit Community Cloud and Heroku. Improve practical Python skills through implementing end-to-end data applications and prototyping data workflows. Author(s) Tyler Richards, the author of Streamlit for Data Science, is a senior data scientist with in-depth practical experience in building data-driven applications. With a passion for Python and data visualization, Tyler leverages his knowledge to help data professionals craft effective and compelling tools. His teaching approach combines clarity, hands-on exercises, and practical relevance. Who is it for? This book is written for data scientists, engineers, and enthusiasts who use Python and want to create dynamic data-driven applications. With a focus on those who have some familiarity with Python and libraries like Pandas or NumPy, it assists readers in building on their knowledge by offering tailored guidance. Perfect for those looking to prototype data projects or enhance their programming toolkit.

As an aspiring data scientist, you appreciate why organizations rely on data for important decisions—whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas. Refine a question of interest to one that can be studied with data Pursue data collection that may involve text processing, web scraping, etc. Glean valuable insights about data through data cleaning, exploration, and visualization Learn how to use modeling to describe the data Generalize findings beyond the data

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You'll Learn Understand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis

Building Statistical Models in Python is your go-to guide for mastering statistical modeling techniques using Python. By reading this book, you will explore how to use Python libraries like stats models and others to tackle tasks such as regression, classification, and time series analysis. What this Book will help me do Develop a deep practical knowledge of statistical concepts and their implementation in Python. Create regression and classification models to solve real-world problems. Gain expertise analyzing time series data and generating valuable forecasts. Learn to perform hypothesis verification to interpret data correctly. Understand survival analysis and apply it in various industry scenarios. Author(s) Huy Hoang Nguyen, Paul N Adams, and Stuart J Miller bring their extensive expertise in data science and Python programming to the table. With years of professional experience in both industry and academia, they aim to make statistical modeling approachable and applicable. Combining technical depth with hands-on coding, their goal is to ensure readers not only understand the theory but also gain confidence in its application. Who is it for? This book is tailored for beginners and intermediate programmers seeking to learn statistical modeling without a prerequisite in mathematics. It's ideal for data analysts, data scientists, and Python enthusiasts who want to leverage statistical models to gain insights from data. With this book, you will journey from the basics to advanced applications, making it perfect for those who aim to master statistical analysis.

This comprehensive book on Tableau 2023 is your practical guide to mastering data visualization and business intelligence techniques. You will explore the latest features of Tableau, learn how to create insightful dashboards, and gain proficiency in integrating analytics and machine learning workflows. By the end, you'll have the skills to address a variety of analytics challenges using Tableau. What this Book will help me do Master the latest Tableau 2023 features and use cases to tackle analytics challenges. Develop and implement ETL workflows using Tableau Prep Builder for optimized data preparation. Integrate Tableau with programming languages such as Python and R to enhance analytics. Create engaging, visually impactful dashboards for effective data storytelling. Understand and apply data governance to ensure data quality and compliance. Author(s) Marleen Meier is an experienced data visualization expert and Tableau consultant with over a decade of experience helping organizations transform data into actionable insights. Her approach integrates her technical expertise and a keen eye for design to make analytics accessible rather than overwhelming. Her passion for teaching others to use visualization tools effectively shines through in her writing. Who is it for? This book is ideal for business analysts, BI professionals, or data analysts looking to enhance their Tableau expertise. It caters to both newcomers seeking to understand the foundations of Tableau and experienced users aiming to refine their skills in advanced analytics and data visualization. If your goal is to leverage Tableau as a strategic tool in your organization's BI projects, this book is for you.

Building Data Science Applications with FastAPI is your comprehensive guide to mastering the FastAPI framework to build efficient, reliable data science applications and APIs. You'll explore examples and projects that integrate machine learning models, manage databases, and leverage advanced FastAPI features like asynchronous I/O and WebSockets. What this Book will help me do Develop an understanding of the fundamentals and advanced features of the FastAPI framework, like dependency injection and type hinting. Learn how to integrate machine learning models into a FastAPI-based web backend effectively. Master concepts of authentication, database connections, and asynchronous programming in Python. Build and deploy two practical AI applications: a real-time object detection tool and a text-to-image generator. Acquire skills to monitor, log, and maintain software systems for optimal performance and reliability. Author(s) François Voron is an experienced Python developer and data scientist with extensive knowledge of western frameworks including FastAPI. With years of experience designing and deploying machine learning and data science applications, François focuses on empowering developers with practical techniques and real-world applications. His guidance helps readers tackle contemporary challenges in software development. Who is it for? This book is ideal for data scientists and software engineers looking to broaden their skillset by creating robust web APIs for data science applications. Readers are expected to have a working knowledge of Python and basic data science concepts, offering them a chance to expand into backend development. If you're keen to deploy machine learning models and integrate them seamlessly with web technologies, this book is for you. It provides both fundamental insights and advanced techniques to serve a broad range of learners.