Data Science Pour L Agriculture Et L Environnement M Thodes Et Applications Avec R Et Python
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Data Science Pour L Agriculture Et L Environnement M Thodes Et Applications Avec R Et Python
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Author : François Brun
language : fr
Publisher: Editions Ellipses
Release Date : 2021-03-16
Data Science Pour L Agriculture Et L Environnement M Thodes Et Applications Avec R Et Python written by François Brun and has been published by Editions Ellipses this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-16 with Science categories.
La data science a acquis une grande renommée dans de nombreux domaines mais son utilisation en agriculture et, plus largement dans les sciences environnementales, reste encore limitée. Ce manuel d’initiation vise à démocratiser l’usage de la data science pour des applications en lien avec l’agriculture et l’environnement. L’ouvrage couvre les principales méthodes couramment utilisées pour la prédiction, la classification et le partitionnement de données. Il comporte à la fois des explications détaillées du fonctionnement de chaque méthode, une description de codes R et Python permettant leur utilisation pratique, et des exemples d’applications concrètes en lien avec les sciences agricoles et environnementales.
Data Science Pour L Agriculture Et L Environnement
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Author : François Brun
language : fr
Publisher:
Release Date : 2021-03-16
Data Science Pour L Agriculture Et L Environnement written by François Brun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-16 with categories.
Data Science Using Python And R
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Author : Chantal D. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2019-04-09
Data Science Using Python And R written by Chantal D. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-09 with Computers categories.
Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.
Data Science
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Author : Eric Biernat
language : fr
Publisher: Editions Eyrolles
Release Date : 2015
Data Science written by Eric Biernat and has been published by Editions Eyrolles this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computers categories.
Nous vivons une époque très excitante, qui ramène l'analyse de données et les méthodes quantitatives au coeur de la société. L'aboutissement de nombreux projets de recherche, la puissance de calcul informatique disponible et des données à profusion permettent aujourd'hui d'incroyables réalisations, grâce au travail des data scientists.Un livre de référence pour les data scientistsLa data science est l'art de traduire des problèmes industriels, sociaux, scientifiques, ou de toute autre nature, en problèmes de modélisation quantitative, pouvant être résolus par des algorithmes de traitement de données. Cela passe par une réflexion structurée, devant faire en sorte que se rencontrent problèmes humains, outils techniques/informatiques et méthodes statistiques/algorithmiques. Chaque projet de data science est une petite aventure, qui nécessite de partir d'un problème opérationnel souvent flou, à une réponse formelle et précise, qui aura des conséquences réelles sur le quotidien d'un nombre plus ou moins important de personnes.Éric Biernat et Michel Lutz proposent de vous guider dans cette aventure. Ils vous feront visiter les vastes espaces de la data science moderne, de plus en plus présente dans notre société et qui fait tant parler d'elle, parfois par l'intermédiaire d'un sujet qui lui est corollaire, les big data.Des études de cas pour devenir kaggle masterLoin des grands discours abstraits, les auteurs vous feront découvrir, claviers à la main, les pratiques de leur métier de data scientist chez OCTO Technology, l'un des leaders français du domaine. Et vous mettrez également la main à la pâte: avec juste ce qu'il faut de théorie pour comprendre ce qu'impliquent les méthodes mathématiques utilisées, mais surtout avec votre ordinateur personnel, quelques logiciels gratuits et puissants, ainsi qu'un peu de réflexion, vous allez participer activement à cette passionnante exploration !À qui s'adresse cet ouvrage ?Aux développeurs, statisticiens, étudiants et chefs de pr
Applied Data Science With Python And Jupyter
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Author : Alex Galea
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31
Applied Data Science With Python And Jupyter written by Alex Galea and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.
Practical Data Science With R And Python
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Author : THOMPSON. CARTER
language : en
Publisher: Independently Published
Release Date : 2025-01-19
Practical Data Science With R And Python written by THOMPSON. CARTER and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-19 with Computers categories.
Practical Data Science with R and Python: A Hands-On Approach to Solving Data Problems with R and Python Master the art of data science with Practical Data Science with R and Python, a comprehensive guide for aspiring data scientists, analysts, and professionals eager to leverage the power of R and Python to solve real-world data challenges. This hands-on book combines the strengths of these two leading data science tools to provide practical solutions to common data problems, helping you make data-driven decisions with confidence. Whether you're new to data science or looking to expand your skillset, this book equips you with the tools, techniques, and workflows necessary to process, analyze, and visualize data effectively. What You'll Learn: Getting Started with R and Python: Set up your environment and learn the basics of both R and Python for data science. Data Wrangling and Cleaning: Handle messy datasets with Pandas in Python and dplyr in R to prepare data for analysis. Exploratory Data Analysis (EDA): Uncover patterns, trends, and insights in data using visualization libraries like ggplot2 (R) and Matplotlib (Python). Statistical Analysis: Perform descriptive and inferential statistics to test hypotheses and summarize data. Machine Learning Basics: Build and evaluate predictive models using scikit-learn (Python) and caret (R). Data Visualization: Create compelling and interactive visualizations with R's Shiny and Python's Plotly libraries. Big Data Processing: Learn to work with large datasets using tools like SparkR, Dask, and PySpark. Time-Series Analysis: Analyze and forecast time-series data with ARIMA and Prophet in both R and Python. Text Mining and Natural Language Processing: Extract insights from unstructured text data using libraries like tidytext (R) and NLTK (Python). Handling Geospatial Data: Visualize and analyze geospatial data with GeoPandas (Python) and sf (R). Integration and Automation: Combine R and Python workflows to streamline repetitive tasks and maximize efficiency. Model Deployment: Learn to deploy your data science models into production environments with Flask (Python) and RStudio Connect. Real-World Applications: Work through projects including fraud detection, customer segmentation, and recommendation systems. Collaborating and Version Control: Utilize Git and GitHub to manage and share your projects effectively. Who Is This Book For? This book is ideal for data scientists, analysts, and professionals who want to master both R and Python to tackle diverse data challenges and improve their workflows. Why Choose This Book? With its hands-on approach and focus on practical applications, Practical Data Science with R and Python bridges the gap between theory and practice, helping you confidently solve data problems and deliver actionable insights. Start solving your data challenges today with Practical Data Science with R and Python: A Hands-On Approach to Solving Data Problems with R and Python-your essential guide to becoming a versatile data scientist.
Python Pour Le Data Scientist
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Author : Emmanuel Jakobowicz
language : fr
Publisher:
Release Date : 2021-03-03
Python Pour Le Data Scientist written by Emmanuel Jakobowicz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-03 with categories.
Python And R For The Modern Data Scientist
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Author : Rick J. Scavetta
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-22
Python And R For The Modern Data Scientist written by Rick J. Scavetta and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-22 with Computers categories.
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together
Python Pour Le Data Scientist 3e D
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Author : Emmanuel Jakobowicz
language : fr
Publisher: Dunod
Release Date : 2024-03-13
Python Pour Le Data Scientist 3e D written by Emmanuel Jakobowicz and has been published by Dunod this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-13 with Computers categories.
Si vous vous intéressez au traitement des données avec le langage Python, cet ouvrage s’adresse à vous. Que vous soyez débutant en Python ou que vous ayez une expérience significative, il vous apportera les clés pour utiliser ce langage en data science. Ce livre répond à de nombreuses questions sur Python : Comment utiliser Python en data science ? Comment coder en Python ? Comment préparer des données avec Python ? Comment créer des visualisations attractives avec Python ? Comment appliquer des modèles de machine learning et de deep learning avec Python ? Comment passer aux environnements big data ? Pour mettre en place vos traitements, vous apprendrez à tirer parti des multiples outils Python tels que Anaconda, Jupyter, NumPy, Pandas, Matplotlib, Seaborn, Bokeh, Scikit-Learn, TensorFlow, PySpark... Cette troisième édition, entièrement refondue et augmentée, est enrichie des mises à jour de code liées aux évolutions de Python et des packages de data science. L’ensemble du code compris dans cet ouvrage est disponible sous la forme de notebooks Jupyter dans le repository public de l’auteur : https://github.com/emjako/pythondatascientist
From Experimental Network To Meta Analysis
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Author : David Makowski
language : en
Publisher: Springer
Release Date : 2019-05-07
From Experimental Network To Meta Analysis written by David Makowski and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-07 with Technology & Engineering categories.
This book has been designed as a methodological guide and shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science.