Data science handbook : a practical approach / / Kolla Bhanu Prakash

• Autore: Prakash Kolla Bhanu
• Pubblicazione: Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2022]; ©2022
• Descrizione fisica: 1 online resource (472 pages)
• Disciplina: 005.7
• Soggetto topico: Big data; Data mining; Information visualization
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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. ; References -- 7 Network Analysis with NetworkX -- 7.1 Working with Graph Objects -- 7.2 Simulating a Social Network (ie -- Directed Network Analysis) -- 7.3 Analyzing a Social Network -- References -- 8 Basic Algorithmic Learning -- 8.1 Linear Regression -- 8.2 Logistic Regression -- 8.3 Naive Bayes Classifiers -- References -- 9 Web-Based Data Visualizations with Plotly -- 9.1 Collaborative Analytics -- 9.2 Basic Charts -- 9.3 Statistical Charts -- 9.4 Plotly Maps -- References -- 10 Web Scraping with Beautiful Soup -- 10.1 The BeautifulSoup Object -- 10.2 Exploring NavigableString Objects -- 10.3 Data Parsing -- 10.4 Web Scraping -- 10.5 Ensemble Models with Random Forests -- References -- 11 Covid19 Detection and Prediction -- Bibliography -- 12 Leaf Disease Detection -- Bibliography -- 13 Brain Tumor Detection with Data Science -- Bibliography -- 14 Color Detection with Python -- Bibliography -- 15 Detecting Parkinson's Disease -- Bibliography -- 16 Sentiment Analysis -- Bibliography -- 17 Road Lane Line Detection -- Bibliography -- 18 Fake News Detection -- Bibliography -- 19 Speech Emotion Recognition -- Bibliography -- 20 Gender and Age Detection with Data Science -- Bibliography -- 21 Diabetic Retinopathy -- Bibliography -- 22 Driver Drowsiness Detection in Python -- Bibliography -- 23 Chatbot Using Python -- Bibliography -- 24 Handwritten Digit Recognition Project -- Bibliography -- 25 Image Caption Generator Project in Python -- Bibliography -- 26 Credit Card Fraud Detection Project -- Bibliography -- 27 Movie Recommendation System -- Bibliography -- 28 Customer Segmentation -- Bibliography -- 29 Breast Cancer Classification -- Bibliography -- 30 Traffic Signs Recognition -- Bibliography -- EULA.
• Titolo autorizzato: Data science handbook
• ISBN: 1-119-85801-1; 1-119-85800-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910677188103321
• Lo trovi qui: Univ. Federico II