Main Title |
Data science handbook : a practical approach / |
Author |
Prakash, Kolla Bhanu,
|
Publisher |
Wiley-Scrivener, |
Year Published |
2022 |
OCLC Number |
1376445959 |
ISBN |
9781119857334; 1119857333 |
Subjects |
Big data ;
Data mining ;
Quantitative research ;
Information visualization
|
Holdings |
Library |
Call Number |
Additional Info |
Location |
Last Modified |
Checkout Status |
EKBM |
QA76.9.B45P73 2022 |
|
Research Triangle Park Library/RTP, NC |
04/26/2023 |
STATUS |
|
Collation |
xiii, 453 pages : illustrations ; 24 cm |
Notes |
Includes bibliographical references. Print version record |
Contents Notes |
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. |
Place Published |
Hoboken, NJ |
PUB Date Free Form |
©2022 |
Series Title Untraced |
Next-generation computing and communication engineering |
BIB Level |
m |
Medium |
unmediated |
Content |
text |
Carrier |
volume |
Cataloging Source |
RDA |
OCLC Time Stamp |
20230419213018 |
Language |
eng |
Origin |
OCLC |
Type |
CAT |
OCLC Rec Leader |
03405nam 2200445Ii 45010 |