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About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. Found inside – Page 1But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested ... This is where this book helps. The data science solutions book provides a repeatable, robust, and reliable framework to apply the right-fit workflows, strategies, tools, APIs, and domain for your data science projects. Found inside – Page 1This book is the "Hello, World" tutorial for building products, technologies, and teams in a startup environment. "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"-- This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. Found insideIn this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ... With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for . Python comes with many libraries and tools that help you work on geoprocessing tasks without investing in expensive tools. This book introduces you to new libraries that perform geospatial and statistical analysis and data management. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Found inside – Page 441Data Camp tutorial. https://www.datacamp.com/community/tutorials/stemminglemmatization-python. Johnson, Bruce, Mila Oiva, and Hannu Salmi. 2019. Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. 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With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features ... Pro Spring Batch includes examples of I/O options that are not mentioned in the official user’s guide, as well as performance tips on things like how to limit the impact of maintaining the state of your jobs. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive ... This book is a complete introduction to the power of R for marketing research practitioners. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of ... Found inside – Page iThis essential guide to doing social research in this fast-evolving digital age explains how the digital revolution is transforming the way social scientists observe behavior, ask questions, run experiments, and engage in mass ... Found inside – Page 1You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory ... 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Found insideDrawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to ... Found insideLinear algebra is a pillar of machine learning. Your one-stop guide to building an efficient data science pipeline using JupyterAbout This Book* Get the most out of your Jupyter notebook to complete the trickiest of tasks in Data Science* Learn all the tasks in the data science pipeline ... Found insideWhat you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build ... 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