TY  - BOOK
AU  - Prakash,Kolla Bhanu
TI  - Data science handbook: a practical approach 
T2  - Next-generation computing and communication engineering
SN  - 9781119858003
AV  - QA76.9.B45 P73 2022
U1  - 005.7 23
PY  - 2022///
CY  - Hoboken, NJ
PB  - Wiley-Scrivener
KW  - Big data
KW  - Data mining
KW  - Quantitative research
KW  - Information visualization
KW  - Donn�ees volumineuses
KW  - Exploration de donn�ees (Informatique)
KW  - Recherche quantitative
KW  - Visualisation de l'information
KW  - fast
N1  - Includes bibliographical references; 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
N2  - 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
UR  - https://onlinelibrary.wiley.com/doi/book/10.1002/9781119858010
ER  -