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SQL for Data Scientists
  • Language: en
  • Pages: 400

SQL for Data Scientists

Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgroun...

Hands-On Data Science with R
  • Language: en
  • Pages: 414

Hands-On Data Science with R

A hands-on guide for professionals to perform various data science tasks in R Key FeaturesExplore the popular R packages for data scienceUse R for efficient data mining, text analytics and feature engineeringBecome a thorough data science professional with the help of hands-on examples and use-cases in RBook Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world da...

R for Data Science
  • Language: en
  • Pages: 579

R for Data Science

Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto

SQL for Data Scientists: A Beginner′s Guide for Building Datasets for Analysis
  • Language: es

SQL for Data Scientists: A Beginner′s Guide for Building Datasets for Analysis

  • Type: Book
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  • Published: 2021
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  • Publisher: Unknown

None

R für Data Science
  • Language: de
  • Pages: 722

R für Data Science

  • Type: Book
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  • Published: 2024-03-26
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  • Publisher: O'Reilly

Mit R Daten analysieren - die anschauliche und verständliche Einführung 2. Auflage des US-Bestellers, jetzt vollständig aktualisiert und erweitert Hadley Wickham ist eine Legende auf dem Gebiet der Data Science: Er hat eine vollkommen neue, bahnbrechende Methode der Datenanalyse mit R entwickelt Wickhams innovative Herangehensweise wird in diesem Buch beschrieben, es ist ein Standardwerk für Datenanalysten Erfahren Sie, wie Sie mit R aus Ihren Daten Erkenntnisse und Einsichten gewinnen. Dieses Buch führt Sie in R und RStudio ein sowie in Tidyverse, eine Sammlung von R-Paketen, mit denen Data-Science-Aufgaben effektiv und zeitsparend erledigt werden können. Auch wenn Sie keine Programmi...

Data Conscience
  • Language: en
  • Pages: 305

Data Conscience

DATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge ...

Dissertation Abstracts International
  • Language: en
  • Pages: 604

Dissertation Abstracts International

  • Type: Book
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  • Published: 2007
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  • Publisher: Unknown

None

True Latter-Day Saints' Herald
  • Language: en
  • Pages: 1514

True Latter-Day Saints' Herald

  • Type: Book
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  • Published: 1928
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  • Publisher: Unknown

The family magazine of the Reorganized Church of Jesus Christ of Latter-Day Saints.

Analysis and Design of Algorithms
  • Language: en
  • Pages: 282

Analysis and Design of Algorithms

A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computerKey features This book is especially designed for beginners and explains all aspects of algorithm and its analysis in a simple and systematic manner. Algorithms and their working are explained in detail with the help of several illustrative examples. Important features like greedy algorithm, dynamic algorithm, string matching algorithm, branch and bound algorithm, NP hard and NP complete problems are suitably highlighted. Solved and frequently asked questions in the various competitive examinations, sample papers of the past examinations are provided which will serve as a use...

Graphical Models, Exponential Families, and Variational Inference
  • Language: en
  • Pages: 324

Graphical Models, Exponential Families, and Variational Inference

The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.