business data science

【中古】ビッグデ-タを活かすデ-タサイエンス クロス集計から機械学習までのビジネス活用事例 /東京図書/酒巻隆治(単行本)Entertainment Science Data Analytics and Practical Theory for Movies, Games, Books, and Music【電子書籍】 Thorsten Hennig-ThurauData Science from Scratch First Principles with Python【電子書籍】 Joel Grus【中古】 データサイエンス超入門 ビジネスで役立つ「統計学」の本当の活かし方 / 工藤卓哉, 保科学世 / 日経BP 単行本 【メール便送料無料】【あす楽対応】Data Science for Business With R【電子書籍】 Jeffrey S. SaltzData Science and Machine Learning Mathematical and Statistical Methods【電子書籍】 Dirk P. Kroeseビジネス データサイエンス入門 データ分析業務の自動化とデータサイエンティストのリスキリング 喜田 昌樹An Introduction to Data Science【電子書籍】 Jeffrey S. Saltz【中古】デ-タサイエンス超入門 ビジネスで役立つ「統計学」の本当の活かし方 /日経BP/工藤卓哉(単行本)Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions【電子書籍】 Matt TaddyQuantitative Economics with R A Data Science Approach【電子書籍】 Vikram DayalDriving Digital Transformation through Data and AI A Practical Guide to Delivering Data Science and Machine Learning Products【電子書籍】 Alexander BorekBasketball Data Science With Applications in R【電子書籍】 Paola Zuccolotto戦略的データサイエンス入門 ビジネスに活かすコンセプトとテクニック / 原タイトル:Data Science for Business 本/雑誌 / FosterProvost/著 TomFawcett/著 竹田正和/監訳 古畠敦/訳 瀬戸山雅人/訳 大木嘉人/訳 藤野賢祐/訳 宗定洋平/訳 西谷雅史/訳 砂子一徳/訳 市川正和/Becoming a Data Head How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning【電子書籍】 Alex J. Gutman【中古】 データサイエンス超入門 ビジネスで役立つ「統計学」の本当の活かし方 / 工藤卓哉, 保科学世 / 日経BP 単行本 【宅配便出荷】Statistics with Julia Fundamentals for Data Science, Machine Learning and Artificial Intelligence【電子書籍】 Yoni NazarathyStorytelling with Data A Data Visualization Guide for Business Professionals【電子書籍】 Cole Nussbaumer KnaflicData Science and Productivity Analytics【電子書籍】Data Analytics for Businesses 2019: Master Data Science with Optimised Marketing Strategies using Data Mining Algorithms (Artificial Intelligence, Machine Learning, Predictive Modelling and more)【電子書籍】 Riley Adams
 

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  • ◆◆◆非常にきれいな状態です。中古商品のため使用感等ある場合がございますが、品質には十分注意して発送いたします。 【毎日発送】 商品状態 著者名 酒巻隆治、里洋平 出版社名 東京図書 発売日 2014年11月 ISBN 9784489022012
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  • <p>The entertainment industry has long been dominated by legendary screenwriter William Goldman’s “Nobody-Knows-Anything” mantra, which argues that success is the result of managerial intuition and instinct. This book builds the case that combining such intuition with data analytics and rigorous scholarly knowledge provides a source of sustainable competitive advantage ? the same recipe for success that is behind the rise of firms such as Netflix and Spotify, but has also fueled Disney’s recent success. Unlocking a large repertoire of scientific studies by business scholars and entertainment economists, the authors identify essential factors, mechanisms, and methods that help a new entertainment product succeed. The book thus offers a timely alternative to “Nobody-Knows” decision-making in the digital era: while coupling a good idea with smart data analytics and entertainment theory cannot guarantee a hit, it systematically and substantially increases the probability of success in...
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  • <p>Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.</p> <p>If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.</p>画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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  • 著者:工藤卓哉, 保科学世出版社:日経BPサイズ:単行本ISBN-10:4822249786ISBN-13:9784822249786■こちらの商品もオススメです ● 伝える力 「話す」「書く」「聞く」能力が仕事を変える! / 池上 彰 / PHP研究所 [新書] ● 火花 / 又吉 直樹 / 文藝春秋 [単行本] ● 人生がときめく片づけの魔法 / 近藤麻理恵 / サンマーク出版 [単行本(ソフトカバー)] ● 白洲次郎の日本国憲法 / 鶴見 紘 / 光文社 [文庫] ● コトラーを読む / 酒井 光雄 / 日経BPマーケティング(日本経済新聞出版 [新書] ● 小説会計監査 / 細野 康弘 / 東洋経済新報社 [単行本] ● 人口減が地方を強くする / 藤波 匠 / 日経BPマーケティング(日本経済新聞出版 [単行本] ● 統計学が最強の学問である データ社会を生き抜くための武器と教養 / 西内 啓 / ダイヤモンド社 [単行本(ソフトカバー)] ● シンプル&ナチュラルな部屋づくり 理想のインテリアが実現できる実例集 / 主婦の友社 / 主婦の友社 [ムック] ● 文系でもわかるビジネス統計入門 / 内田 学, 兼子 良久, 斉藤 嘉一 / 東洋経済新報社 [単行本] ● プロジェクトマネジメントリテラシ / アイテック情報技術教育研究部 / アイテック [単行本(ソフト...
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  • <p><em>Data Science for Business with R,</em> written by Jeffrey S. Saltz and Jeffrey M. Stanton,focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available.</p> <p>Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar struct...
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  • <p>"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!" <strong>-Nicholas Hoell, University of Toronto</strong></p> <p><em>"This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche.</em> ...
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  • データ分析業務の自動化とデータサイエンティストのリスキリング 喜田 昌樹 白桃書房ビジネスデータサイエンスニュウモン キダ マサキ 発行年月:2023年12月28日 予約締切日:2023年11月21日 ページ数:320p サイズ:単行本 ISBN:9784561247883 喜田昌樹(キダマサキ) 1989年同志社大学経済学部卒業。2021年滋賀大学経済学部教授(現在)。研究領域:認知的組織論、ナレッジマネジメント、テキストマイニング(本データはこの書籍が刊行された当時に掲載されていたものです) 第1章 ビジネス・データサイエンスとは/第2章 ビジネスの理解(経営課題の発見)に必要な経営学の知識/第3章 データサイエンスの前提条件1:基幹系システムとデータマネジメント/第4章 データサイエンスの前提条件2:データの理解と準備/第5章 モデリングと対応する経営課題/第6章 リスクマネジメントに使う/第7章 顧客行動を理解し、予測する:需要予測とチャーン・マネジメント/第8章 より効率的な顧客ターゲッティングを行いたい:自己組織化マップによる合成変数の構築/第9章 売上が伸びる仕組みを構築したい:店舗設計とレコメンドシステム/第10章 非構造化データを扱...
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  • <p><strong>An Introduction to Data Science</strong> is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language and RStudio? from the ground up. Short chapters allow instructors to group concepts together for a semester course and provide students with manageable amounts of information for each concept. By taking students systematically through the R programming environment, the book takes the fear out of data science and familiarizes students with the environment so they can be successful when performing advanced functions.</p> <p>The authors cover statistics from a conceptual standpoint, focusing on how to use and interpret statistics, rather than the math behind the statistics. This text then demonstr...
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    商品の説明

  • ◆◆◆非常にきれいな状態です。中古商品のため使用感等ある場合がございますが、品質には十分注意して発送いたします。 【毎日発送】 商品状態 著者名 工藤卓哉、保科学世 出版社名 日経BP 発売日 2013年11月 ISBN 9784822249786
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  • <p><strong>Use machine learning to understand your customers, frame decisions, and drive value</strong></p> <p>The business analytics world has changed, and Data Scientists are taking over. <em>Business Data Science</em> takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to:</p> <ul> <li>Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling</li> <li>Understand how use ML tools in real world business problems, where causation matters more that correlation</li> <li>Solve data science programs by scripting in the R programming language</li> </ul> <p>Toda...
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  • <p>This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises.</p> <p>At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inferenceis illuminated using simulation, data graphs, and R code for applications with real economic examples,...
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  • <p><strong>Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business.</strong></p> <p>It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working.</p> <p>Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, ma...
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  • <p>Using data from one season of NBA games, <strong>Basketball Data Science: With Applications in R</strong> is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, <strong>Basketball Data Science with R</strong> is suitable for students, technicians, coaches, data analysts and applied researchers.</p> <p><strong>Features</strong>:</p> <ul> <li>One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball</li> <li>Presents tools for modelling graphs and figures to visualize...
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  • ご注文前に必ずご確認ください<商品説明>ビッグデータ時代とも言われる昨今においては、データ収集を行ってビジネスの全体像を把握し、適切なデータ分析を行って正確な予測をした上でビジネス戦略を決めることが求められています。本書は、データをビジネスに活かすために身に付けておくべき基本的な考え方と、データマイニングやモデリングの根底に存在するコンセプトについて、体系的に解説しています。データサイエンスの重要性とその威力を学べる一冊です。<収録内容>はじめに:データ分析思考ビジネス問題とデータサイエンスが提供するソリューション予測モデリング:相関から教師ありセグメンテーションへモデルをデータにフィットさせるオーバーフィッティングとその回避方法類似度、近傍、クラスタ意思決定のための分析思考1:良いモデルとは何かモデル性能の可視化エビデンスと確率テキスト表現とテキストマインニグ意思決定のための分析思考2:分析思考から分析工学へその他のデータサイエンスの問題と技法データサイエンスとビジネス戦略おわりに提案レビューのガイドその他の提案例用語辞書<商品詳細>商品番号:NEOBK-1691502メディア:本/雑誌重量:340g発...
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  • <p><strong>"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful."</strong><br /> Thomas H. Davenport, Research Fellow, Author of <em>Competing on Analytics</em>, <em>Big Data @ Work</em>, and <em>The AI Advantage</em></p> <p><strong>You've heard the hype around dataーnow get the facts.</strong></p> <p>In <em>Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning</em>, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.</p> <p>You'll learn how to:</p> <ul> <li>Think statistically and understand the role variation plays in your life and decision making</li> <li>Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace</li> <li>Understand...
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    商品の説明

  • 著者:工藤卓哉, 保科学世出版社:日経BPサイズ:単行本ISBN-10:4822249786ISBN-13:9784822249786■こちらの商品もオススメです ● 伝える力 「話す」「書く」「聞く」能力が仕事を変える! / 池上 彰 / PHP研究所 [新書] ● 火花 / 又吉 直樹 / 文藝春秋 [単行本] ● 人生がときめく片づけの魔法 / 近藤麻理恵 / サンマーク出版 [単行本(ソフトカバー)] ● 白洲次郎の日本国憲法 / 鶴見 紘 / 光文社 [文庫] ● コトラーを読む / 酒井 光雄 / 日経BPマーケティング(日本経済新聞出版 [新書] ● 小説会計監査 / 細野 康弘 / 東洋経済新報社 [単行本] ● 人口減が地方を強くする / 藤波 匠 / 日経BPマーケティング(日本経済新聞出版 [単行本] ● 統計学が最強の学問である データ社会を生き抜くための武器と教養 / 西内 啓 / ダイヤモンド社 [単行本(ソフトカバー)] ● シンプル&ナチュラルな部屋づくり 理想のインテリアが実現できる実例集 / 主婦の友社 / 主婦の友社 [ムック] ● 文系でもわかるビジネス統計入門 / 内田 学, 兼子 良久, 斉藤 嘉一 / 東洋経済新報社 [単行本] ● プロジェクトマネジメントリテラシ / アイテック情報技術教育研究部 / アイテック [単行本(ソフト...
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  • <p>This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics.</p> <p>The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than...
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  • <p><strong>Don't simply show your dataーtell a story with it!</strong></p> <p><em>Storytelling with Data</em> teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examplesーready for immediate application to your next graph or presentation.</p> <p>Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:</p> <ul> <li>Understand the importance of context and audience</li> <li>Determine the appropriate type of graph for your ...
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  • <p>This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.</p> <p>Examples of data science techniques include linear and logistic regressions, decision trees, Na?ve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without ...
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  • <p><strong>Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximise YOUR business.</strong></p> <p>Yes, you have customers that love your product. However, you're having trouble finding new customers and captivating their attention. You realized your also losing customers, and you have no clue what you can do to prevent this from happening. How do I stand out in a crowd of businesses? How do I target my perfect client and make them choose ME? If this sounds like you, Data Analytics for Businesses if the guide you need.</p> <p>This book will walk you through the fundamental principles of data science and how to apply the "data analytic mindset" when approaching your business. You will learn how to extract valuable insights from data sources you ALREADY HAVE, and mak...
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