Predictive Analytics and Data Mining: Concepts and Practice (預測分析與資料探勘：概念與實務)

Vijay Kotu, Bala Deshpande

Predictive Analytics and Data Mining: Concepts and Practice

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商品描述

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.

Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data.

You’ll be able to:

  1. Gain the necessary knowledge of different data science techniques to extract value from data.

  2. Master the concepts and inner workings of 30 commonly used powerful data science algorithms.

  3. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform

Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

  • Contains fully updated content on data science, including tactics on how to mine business data for information
  • Presents simple explanations for over twenty powerful data science techniques
  • Enables the practical use of data science algorithms without the need for programming
  • Demonstrates processes with practical use cases
  • Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language
  • Describes the commonly used setup options for the open source tool RapidMiner

商品描述(中文翻譯)

這本書以易於理解的概念框架介紹資料科學的基礎知識，並且立即實踐使用RapidMiner平台。無論您是對資料科學全新還是已經進行了十個項目，本書都將向您展示如何分析數據，發現隱藏的模式和關係，以幫助重要的決策和預測。

資料科學已成為任何收集、存儲和處理數據的組織中從數據中提取價值的重要工具。本書適合商業用戶、數據分析師、業務分析師、工程師和分析專業人士，以及任何與數據工作的人。

您將能夠：
1. 獲得不同資料科學技術的必要知識，以從數據中提取價值。
2. 掌握30種常用強大的資料科學算法的概念和內部運作。
3. 使用RapidMiner進行逐步的資料科學流程實踐，RapidMiner是一個基於圖形用戶界面的開源資料科學平台。

涵蓋的資料科學技術包括：探索性數據分析、可視化、決策樹、規則歸納、k最近鄰算法、朴素貝葉斯分類器、人工神經網絡、深度學習、支持向量機、集成模型、隨機森林、回歸、推薦引擎、關聯分析、K-Means和基於密度的聚類、自組織映射、文本挖掘、時間序列預測、異常檢測、特徵選擇等等...

本書內容包括：
- 完全更新的資料科學內容，包括如何從商業數據中挖掘信息的策略。
- 對二十多種強大資料科學技術的簡單解釋。
- 可以實際應用資料科學算法，無需編程。
- 通過實際案例演示流程。
- 介紹每個算法或技術，並用通俗的語言解釋資料科學算法的運作方式。
- 描述開源工具RapidMiner的常用設置選項。

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