Modern systems contain multi-core CPUs and GPUs that have the potential for parallel computing. But many scientific Python tools were not designed to leverage this parallelism. With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn. Authors Holden Karau and Mika Kimmins show you how to use Dask computations in local systems and then scale to the cloud for heavier workloads. This practical book explains why Dask is popular among industry experts and academics and is used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA. With this book, you'll learn: What Dask is, where you can use it, and how it compares with other tools How to use Dask for batch data parallel processing Key distributed system concepts for working with Dask Methods for using Dask with higher-level APIs and building blocks How to work with integrated libraries such as scikit-learn, pandas, and PyTorch How to use Dask with GPUs

All You Need to Know, and Nothing You Don't, to Solve Real Problems with Python Python is one of the most popular programming languages in the world, used for everything from shell scripts to web development to data science. As a result, Python is a great language to learn, but you don't need to learn "everything" to get started, just how to use it efficiently to solve real problems. In Learn Enough Python to Be Dangerous, renowned instructor Michael Hartl teaches the specific concepts, skills, and approaches you need to be professionally productive. Even if you've never programmed before, Hartl helps you quickly build technical sophistication and master the lore you need to succeed. Hartl introduces Python both as a general-purpose language and as a specialist tool for web development and data science, presenting focused examples and exercises that help you internalize what matters, without wasting time on details pros don't care about. Soon, it'll be like you were born knowing this stuff--and you'll be suddenly, seriously dangerous. Learn enough about . . . Applying core Python concepts with the interactive interpreter and command line Writing object-oriented code with Python's native objects Developing and publishing self-contained Python packages Using elegant, powerful functional programming techniques, including Python comprehensions Building new objects, and extending them via Test-Driven Development (TDD) Leveraging Python's exceptional shell scripting capabilities Creating and deploying a full web app, using routes, layouts, templates, and forms Getting started with data-science tools for numerical computations, data visualization, data analysis, and machine learning Mastering concrete and informal skills every developer needs Michael Hartl's Learn Enough Series includes books and video courses that focus on the most important parts of each subject, so you don't have to learn everything to get started--you just have to learn enough to be dangerous and solve technical problems yourself. Like this book? Don't miss Michael Hartl's companion video tutorial, Learn Enough Python to Be Dangerous LiveLessons. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Dive into the exciting world of data science with this practical introduction. Packed with essential skills and useful examples, Dive Into Data Science will show you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges. With only a basic understanding of Python and high school math, you’ll be able to effortlessly work through the book and start implementing data science in your day-to-day work. From improving a bike sharing company to extracting data from websites and creating recommendation systems, you’ll discover how to find and use data-driven solutions to make business decisions. Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using machine learning algorithms. You’ll also learn how to: •Forecast consumer demand •Optimize marketing campaigns •Reduce customer attrition •Predict website traffic •Build recommendation systems With this practical guide at your fingertips, harness the power of programming, mathematical theory, and good old common sense to find data-driven solutions that make a difference. Don’t wait; dive right in!

Time series data is at the heart of many applications, from finance and system monitoring to weather forecasting and medical data analysis. "Time Series Indexing" offers a hands-on guide to implementing and leveraging the iSAX indexing technique in Python to efficiently manage, search, and analyze time series data. What this Book will help me do Gain the know-how to implement algorithms like SAX and iSAX with illustrative Python examples. Learn to construct robust time series indexes tailored to real-world data sets. Understand the theoretical underpinnings of time series processing and indexing techniques. Explore and employ visualization techniques to interpret time series structures and insights. Gain the skills to adapt iSAX methodologies to other programming environments and practices. Author(s) Mihalis Tsoukalos is an accomplished developer and author specializing in Python programming and data processing techniques. With years of experience translating complex academic research into practical applications, Mihalis excels at bridging the gap between theory and practice. His writing approach ensures readers grasp both the foundational principles and the hands-on methods needed to succeed. Who is it for? This book best suits researchers, analysts, and developers who work with time series data and seek to elevate their proficiency in indexing and managing such data. It is perfect for professionals with a foundational knowledge of Python and programming concepts. This material also supports learners eager to derive actionable insights from theory-heavy academic research.

Microsoft Power BI Machine Learning and OpenAI offers a comprehensive exploration into advanced data analytics and artificial intelligence using Microsoft Power BI. Through hands-on, workshop-style examples, readers will discover the integration of machine learning models and OpenAI features to enhance business intelligence. This book provides practical examples, real-world scenarios, and step-by-step guidance. What this Book will help me do Learn to apply machine learning capabilities within Power BI to create predictive analytics Understand how to integrate OpenAI services to build enhanced analytics workflows Gain hands-on experience in using R and Python for advanced data visualization in Power BI Master the skills needed to build and deploy SaaS auto ML models within Power BI Leverage Power BI's AI visuals and features to elevate data storytelling Author(s) Greg Beaumont, an expert in data science and business intelligence, brings years of experience in Power BI and analytics to this book. With a focus on practical applications, Greg empowers readers to harness the power of AI and machine learning to elevate their data solutions. As a consultant and trainer, he shares his deep knowledge to help readers unlock the full potential of their tools. Who is it for? This book is ideal for data analysts, BI professionals, and data scientists who aim to integrate machine learning and OpenAI into their workflows. If you're familiar with Power BI's fundamentals and are eager to explore its advanced capabilities, this guide is tailored for you. Perfect for professionals looking to elevate their analytics to a new level, combining data science concepts with Power BI's features.

This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics. What You Will Learn Master the mathematical foundations required for business analytics Understand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given task Use R and Python to develop descriptive models, predictive models, and optimize models Interpret and recommend actions based on analytical model outcomes Who This Book Is For Software professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

"Applied Geospatial Data Science with Python" introduces readers to the power of integrating geospatial data into data science workflows. This book equips you with practical methods for processing, analyzing, and visualizing spatial data to solve real-world problems. Through hands-on examples and clear, actionable advice, you will master the art of spatial data analysis using Python. What this Book will help me do Learn to process, analyze, and visualize geospatial data using Python libraries. Develop a foundational understanding of GIS and geospatial data science principles. Gain skills in building geospatial AI and machine learning models for specific use cases. Apply geospatial data workflows to practical scenarios like optimization and clustering. Create a portfolio of geospatial data science projects relevant across different industries. Author(s) David S. Jordan is an experienced data scientist with years of expertise in GIS and geospatial analytics. With a passion for making complex topics accessible, David leverages his deep technical knowledge to provide practical, hands-on instruction. His approach emphasizes real-world applications and encourages learners to develop confidence as they work with geospatial data. Who is it for? This book is perfect for data scientists looking to integrate geospatial data analysis into their existing workflows, and GIS professionals seeking to expand into data science. If you already have a basic knowledge of Python for data analysis or data science and want to explore how to work effectively with geospatial data to drive impactful solutions, this is the book for you.

Optimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries. In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to: Design, run, and analyze an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You’ll start with a deep dive into methods like A/B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. Learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls. About the Technology Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world’s most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions. About the Book Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You’ll start by learning the limits of A/B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you’ll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results. What's Inside Design, run, and analyze an A/B test Break the “feedback loops” caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization About the Reader For ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy. About the Author David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science master's programs at Yeshiva University. Quotes Putting an ‘improved’ version of a system into production can be really risky. This book focuses you on what is important! - Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland A must-have for anyone setting up experiments, from A/B tests to contextual bandits and Bayesian optimization. - Maxim Volgin, KLM Shows a non-mathematical programmer exactly what they need to write powerful mathematically-based testing algorithms. - Patrick Goetz, The University of Texas at Austin Gives you the tools you need to get the most out of your experiments. - Marc-Anthony Taylor, Raiffeisen Bank International

With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. Features: Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics Uses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interface Includes companion files with the case study files from the book, solution spreadsheets, data sets, etc.

Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if youre new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. New features to the second edition include: Extended coverage of plotting and the seaborn data visualization library Expanded examples and resources Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries Online bonus material on geopandas, Dask, and creating interactive graphics with Altair Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine data sets and handle missing data Reshape, tidy, and clean data sets so theyre easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large data sets with groupby Leverage Pandas advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the best one Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning ...

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

Learn how to integrate data-driven methodologies and machine learning into your business decision-making processes with 'The Art of Data-Driven Business.' This comprehensive guide shows you how to apply Python-based machine learning techniques to real-world challenges, transforming your organization into an innovative and well-informed enterprise. What this Book will help me do Create professional-quality data visualizations using Python's seaborn library to derive business insights. Analyze customer behavior, including predicting churn, with machine learning techniques. Apply clustering algorithms to segment customers for targeted marketing campaigns. Utilize pandas effectively for pricing and sales analytics to optimize your pricing strategies. Forecast outcomes of promotional strategies to determine costs and benefits and maximize performance. Author(s) None Palacio is an experienced data scientist and educator who specializes in the application of machine learning to solve business problems. With extensive real-world industry experience, Palacio brings practical insights and methodologies to learners. Their teaching connects technical knowledge to actionable business strategies. Who is it for? This book is ideal for business professionals aiming to incorporate data science into their strategies and technical experts seeking to leverage machine learning for business scenarios. Beginners to Python can find foundational help, while data scientists will appreciate the focused practical applications. It's perfect for individuals seeking a strong data-driven perspective in marketing, sales, and customer management.

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators. In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You'll explore distributed processing (the pure Python implementation of serverless) and learn how to: Implement stateful applications with Ray actors Build workflow management in Ray Use Ray as a unified system for batch and stream processing Apply advanced data processing with Ray Build microservices with Ray Implement reliable Ray applications

A swift and practical introduction to building interactive data visualization apps in Python, known as dashboards. Youâ??ve seen dashboards before; think election result visualizations you can update in real time, or population maps you can filter by demographic. With the Python Dash library youâ??ll create analytic dashboards that present data in effective, usable, elegant ways in just a few lines of code. The book is fast-paced and caters to those entirely new to dashboards. It will talk you through the necessary software, then get straight into building the dashboards themselves. Youâ??ll learn the basic format of a Dash app by building a twitter analysis dashboard that maps the number of likes certain accounts gained over time. Youâ??ll build up skills through three more sophisticated projects. The first is a global analysis app that compares country data in three areas: the percentage of a population using the internet, percentage of parliament seats held by women, and CO2 emissions. Youâ??ll then build an investment portfolio dashboard, and an app that allows you to visualize and explore machine learning algorithms. In this book you will: â?¢Create and run your first Dash apps â?¢Use the pandas library to manipulate and analyze social media data â?¢Use Git to download and build on existing apps written by the pros â?¢Visualize machine learning models in your apps â?¢Create and manipulate statistical and scientific charts and maps using Plotly Dash combines several technologies to get you building dashboards quickly and efficiently. This book will do the same.

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis

This book provides all you need to find data from external sources and load and transform that data into Power BI where you can mine it for business insights and a competitive edge. This ranges from connecting to corporate databases such as Azure SQL and SQL Server to file-based data sources, and cloud- and web-based data sources. The book also explains the use of Direct Query and Live Connect to establish instant connections to databases and data warehouses and avoid loading data. The book provides detailed guidance on techniques for transforming inbound data into normalized data sets that are easy to query and analyze. This covers data cleansing, data modification, and standardization as well as merging source data into robust data structures that can feed into your data model. You will learn how to pivot and transpose data and extrapolate missing values as well as harness external programs such as R and Python into a Power Query data flow. You also will see how to handle errors in source data and extend basic data ingestion to create robust and parameterized data load and transformation processes. Everything in this book is aimed at helping you deliver compelling and interactive insight with remarkable ease using Power BI’s built-in data load and transformation tools. What You Will Learn Connect Power BI to a range of external data sources Prepare data from external sources for easy analysis in Power BI Cleanse data from duplicates, outliers, and other bad values Make live connections from which to refresh data quickly and easily Apply advanced techniques to interpolate missing data Who This Book Is For All Power BI users from beginners to super users. Any user of the world’s leading dashboarding toolcan leverage the techniques explained in this book to turbo-charge their data preparation skills and learn how a wide range of external data sources can be harnessed and loaded into Power BI to drive their analytics. No previous knowledge of working with data, databases, or external data sources is required—merely the need to find, transform, and load data into Power BI..

Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the Technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the Book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's Inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the Reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the Author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Quotes By reading and referring to this book, I’m confident you will learn how to make your machine learning operations much more efficient and productive. - From the Foreword by Travis Oliphant, Author of NumPy, Founder of Anaconda, PyData, and NumFOCUS Effective Data Science Infrastructure is a brilliant book. It’s a must-have for every data science team. - Ninoslav Cerkez, Logit More data science. Less headaches. - Dr. Abel Alejandro Coronado Iruegas, National Institute of Statistics and Geography of Mexico Indispensable. A copy should be on every data engineer’s bookshelf. - Matthew Copple, Grand River Analytics

Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. Youâ??ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Pythonâ??s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.

Discover how to leverage the full potential of Domo, a robust cloud-based business intelligence platform, in your organization. This comprehensive guide walks you through data integration, transformation, visualization, and governance techniques, enabling you to deliver impactful, data-driven results quickly and effectively. What this Book will help me do Understand and utilize Domo's cloud data architecture for comprehensive data analysis. Seamlessly acquire and manage data using Domo connectors and tools. Create and customize dashboards that communicate data insights effectively. Build and deploy Python applications and machine learning models on Domo. Securely govern your organization's data with robust Domo features. Author(s) The author, None Burtenshaw, is an expert in business intelligence and data platforms. With years of experience working with data integration tools, their writing combines technical thoroughness with practical insights. They aim to empower professionals with the skills to excel in data-driven decision making, reflecting their passion for making technology accessible and actionable. Who is it for? This book is ideal for business intelligence professionals, including developers and analysts, looking to elevate their understanding of Domo. It is suited for those with a fundamental knowledge of data platforms seeking advanced skills in data management and visualization. BI managers will gain insights into governance and security, while analysts will find inspiration for data storytelling. If you're aiming to master the possibilities of Domo, this book is for you.

The Pandas Workshop offers a detailed journey into the world of data analysis using Python and the pandas library. Throughout the book, you'll build skills in accessing, transforming, visualizing, and modeling data, all while focusing on real-world data science challenges. You will gain the knowledge and confidence needed to dissect and derive insights from complex datasets. What this Book will help me do Understand how to access and load data from various formats including databases and web-based sources. Manipulate and transform data for analysis using efficient pandas techniques. Create insightful visualizations using Matplotlib integrated with pandas for clearer data presentation. Build predictive and descriptive data models and glean data-driven insights. Handle and analyze time-series data to uncover trends and seasonal effects in data patterns. Author(s) Blaine Bateman, Saikat Basak, Thomas Joseph, and William So collectively bring diverse expertise in data analysis, programming, and teaching. Their goal is to make cutting-edge data science techniques accessible through clear explanations and practical exercises, helping learners from varied backgrounds master the pandas library. Who is it for? This book is best suited for novice to intermediate programmers and data enthusiasts who are already familiar with Python but are new to the pandas library. Ideal readers are those interested in honing their skills in data analysis and visualization, as well as leveraging data for informed decision-making. Whether you're an analyst, aspiring data scientist, or business professional seeking to strengthen your analytical toolkit, this book provides beneficial insights and techniques.