Into You For:Data PDF Download
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Author: Dawn Bradley
Publisher: Xlibris Corporation
ISBN: 1465337032
Category : Fiction
Languages : en
Pages : 53
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Book Description
Myra Rae Watts doesnt realize she has a stalker until a theft occurs. She has to find out who he is in order to reclaim her losses.
Author: Dawn Bradley
Publisher: Xlibris Corporation
ISBN: 1465337032
Category : Fiction
Languages : en
Pages : 53
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Book Description
Myra Rae Watts doesnt realize she has a stalker until a theft occurs. She has to find out who he is in order to reclaim her losses.
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
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Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Author: Hadley Wickham
Publisher: Springer Science & Business Media
ISBN: 0387981411
Category : Computers
Languages : en
Pages : 211
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Book Description
Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Category : Computers
Languages : en
Pages : 553
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Book Description
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
Author: Ken Steif
Publisher: CRC Press
ISBN: 1000401618
Category : Business & Economics
Languages : en
Pages : 254
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Book Description
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.
Author: John Paul Mueller
Publisher: John Wiley & Sons
ISBN: 1394213093
Category : Computers
Languages : en
Pages : 471
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Book Description
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.
Author: Monika Wahi
Publisher: Packt Publishing Ltd
ISBN: 1789531187
Category : Computers
Languages : en
Pages : 494
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Book Description
Build a strong foundation in SAS data warehousing by understanding data transformation code and policy, data stewardship and management, interconnectivity between SAS and other warehousing products, and print and web reporting Key FeaturesUnderstand how to use SAS macros for standardizing extract, transform, and load (ETL) protocolsDevelop and use data curation files for effective warehouse managementLearn how to develop and manage ETL, policies, and print and web reports that meet user needsBook Description SAS is used for various functions in the development and maintenance of data warehouses, thanks to its reputation of being able to handle ’big data’. This book will help you learn the pros and cons of storing data in SAS. As you progress, you’ll understand how to document and design extract-transform-load (ETL) protocols for SAS processes. Later, you’ll focus on how the use of SAS arrays and macros can help standardize ETL. The book will also help you examine approaches for serving up data using SAS and explore how connecting SAS to other systems can enhance the data warehouse user’s experience. By the end of this data management book, you will have a fundamental understanding of the roles SAS can play in a warehouse environment, and be able to choose wisely when designing your data warehousing processes involving SAS. What you will learnDevelop efficient ways to manage data input/output (I/O) in SASCreate and manage extract, transform, and load (ETL) code in SASStandardize ETL through macro variables, macros, and arraysIdentify data warehouse users and ensure their needs are metDesign crosswalk and other variables to serve analyst needsMaintain data curation files to improve communication and managementUse the output delivery system (ODS) for print and web reportingConnect other products to SAS to optimize storage and reportingWho this book is for This book is for data architects, managers leading data projects, and programmers or developers using SAS who want to effectively maintain a data lake, data mart, or data warehouse.
Author: Shekhar Khandelwal
Publisher: BPB Publications
ISBN: 9355515391
Category : Computers
Languages : en
Pages : 251
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Book Description
A hands-on guide to building and deploying deep learning models with Python KEY FEATURES ● Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning tasks. ● Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). ● Gain hands-on experience by working on practical projects and applying deep learning techniques to real-world problems. DESCRIPTION “Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and deep learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and deep learning. The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations. By the end of the book, you will be able to use deep learning to solve real-world problems. WHAT YOU WILL LEARN ● Develop a comprehensive understanding of neural networks' key concepts and principles. ● Gain proficiency in Python as you code and implement major deep-learning algorithms from scratch. ● Build and implement predictive models using various neural networks ● Learn how to use Transformers for complex NLP tasks ● Explore techniques to enhance the performance of your deep learning models. WHO THIS BOOK IS FOR This book is for anyone who is interested in a career in emerging technologies, such as artificial intelligence (AI), data analytics, machine learning, deep learning, and data science. It is a comprehensive guide that covers the fundamentals of these technologies, as well as the skills and knowledge that you need to succeed in this field. TABLE OF CONTENTS 1. Python for Data Science 2. Real-World Challenges for Data Professionals in Converting Data Into Insights 3. Build a Neural Network-Based Predictive Model 4. Convolutional Neural Networks 5. Optical Character Recognition 6. Object Detection 7. Image Segmentation 8. Recurrent Neural Networks 9. Generative Adversarial Networks 10. Transformers
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 149204735X
Category : Computers
Languages : en
Pages : 372
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Book Description
Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production
Author: Tahera Firdose
Publisher: Orange Education Pvt Ltd
ISBN: 8197256241
Category : Computers
Languages : en
Pages : 384
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Book Description
TAGLINE Unlock the power of Data Manipulation with Pandas. KEY FEATURES ● Master Pandas from basics to advanced and its data manipulation techniques. ● Visualize data effectively with Matplotlib and explore data efficiently. ● Learn through hands-on examples and practical real-world use cases. DESCRIPTION Unlock the power of Pandas, the essential Python library for data analysis and manipulation. This comprehensive guide takes you from the basics to advanced techniques, ensuring you master every aspect of pandas. You'll start with an introduction to pandas and data analysis, followed by in-depth explorations of pandas Series and DataFrame, the core data structures. Learn essential skills for data cleaning and filtering, and master grouping and aggregation techniques to summarize and analyze your data sets effectively. Discover how to reshape and pivot data, join and merge multiple datasets, and handle time series analysis. Enhance your data analysis with compelling visualizations using Matplotlib, and apply your knowledge in a real-world scenario by analyzing bank customer churn. Through hands-on examples and practical use cases, this book equips you with the tools to clean, filter, aggregate, reshape, merge, and visualize data effectively, transforming it into actionable insights. WHAT WILL YOU LEARN ● Wrangle data efficiently using Pandas' cleaning, filtering, and transformation techniques. ● Unlock hidden patterns with advanced grouping, joining, and merging operations. ● Master time series analysis with Pandas to extract valuable insights from your data. ● Apply Pandas to real-world scenarios like customer churn analysis and financial modeling. ● Unleash the power of data visualization with Matplotlib and craft compelling charts and graphs. ● Enhance your workflow with essential Pandas optimizations and performance tips. WHO IS THIS BOOK FOR? This book is ideal for aspiring data scientists, analysts, and Python enthusiasts looking to enhance their data manipulation skills using Pandas. Familiarity with Python programming basics and a basic understanding of data structures will greatly benefit readers as they delve into the concepts presented in this book. TABLE OF CONTENTS 1. Introduction to Pandas and Data Analysis 2. Pandas Series 3. Pandas DataFrame 4. Data Cleaning with Pandas 5. Data Filtering with Pandas 6. Grouping and Aggregating Data 7. Reshaping and Pivoting in Pandas 8. Joining and Merging Data in Pandas 9. Introduction to Time Series Analysis in Pandas 10. Visualization Using Matplotlib 11. Analyzing Bank Customer Churn Using Pandas Index
