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Data science handbook : a practical approach / Kolla Bhanu Prakash
(Next-generation computing and communication engineering)

データ種別	電子ブック
出版者	(Hoboken, NJ : Wiley-Scrivener)
出版年	2022
大きさ	1 online resource : illustrations (chiefly color)
著者標目	*Prakash, Kolla Bhanu author

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0005256	Wiley Online Library: Complete oBooks				9781119858003

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一般注記	Includes bibliographical references Description based on online resource; title from digital title page (viewed on October 19, 2022) Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgment -- Preface -- 1 Data Munging Basics -- 1 Introduction -- 1.1 Filtering and Selecting Data -- 1.2 Treating Missing Values -- 1.3 Removing Duplicatesduplicates -- 1.4 Concatenating and Transforming Data -- 1.5 Grouping and Data Aggregation -- References -- 2 Data Visualization -- 2.1 Creating Standard Plots (Line, Bar, Pie) -- 2.2 Defining Elements of a Plot -- 2.3 Plot Formatting Segment 3 Plot formatting -- 2.4 Creating Labels and Annotations -- 2.5 Creating Visualizations from Time Series Data -- 2.6 Constructing Histograms, Box Plots, and Scatter Plots -- References -- 3 Basic Math and Statistics -- 3.1 Linear Algebra -- 3.2 Calculus -- 3.2.1 Differential Calculus -- 3.2.2 Integral Calculus -- Statistics for Data Science -- 3.3 Inferential Statistics -- 3.3.1 Central Limit Theorem -- 3.3.2 Hypothesis Testing -- 3.3.3 ANOVA -- 3.3.4 Qualitative Data Analysis -- 3.4 Using NumPy to Perform Arithmetic Operations on Data -- 3.5 Generating Summary Statistics Using Pandas and Scipy -- 3.6 Summarizing Categorical Data Using Pandas -- 3.7 Starting with Parametric Methods in Pandas and Scipy -- 3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy -- 3.9 Transforming Dataset Distributions -- References -- 4 Introduction to Machine Learning -- 4.1 Introduction to Machine Learning -- 4.2 Types of Machine Learning Algorithms -- 4.3 Explanatory Factor Analysis -- 4.4 Principal Component Analysis (PCA) -- References -- 5 Outlier Analysis -- 5.1 Extreme Value Analysis Using Univariate Methods -- 5.2 Multivariate Analysis for Outlier Detection -- 5.3 DBSCan Clustering to Identify Outliers -- References -- 6 Cluster Analysis -- 6.1 K-Means Algorithm -- 6.2 Hierarchial Methods -- 6.3 Instance-Based Learning w/k-Nearest Neighbor This desk reference handbook gives a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains John Wiley and Sons Wiley Online Library: Complete oBooks HTTP:URL=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119858010
件　名	LCSH:Big data LCSH:Data mining LCSH:Quantitative research LCSH:Information visualization CSHF:Donn�ees volumineuses CSHF:Exploration de donn�ees (Informatique) CSHF:Recherche quantitative CSHF:Visualisation de l'information FREE:Big data FREE:Data mining FREE:Information visualization FREE:Quantitative research
分　類	LCC:QA76.9.B45 DC23:005.7
書誌ID	EB00004545
ISBN	9781119858003