Data Science Handbook
- Length: 480 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-09-27
- ISBN-10: 1119857333
- ISBN-13: 9781119857334
- Sales Rank: #0 (See Top 100 Books)
The book starts with introductory concepts in data science like data munging, data preparation, transforming data. Chapter 2 discusses data visualization, drawing various plots and histograms. Chapter 3 covers mathematics and statistics for data science. Chapter 4 mainly focuses on machine learning algorithms in data science. Chapter 5 comprises outlier analysis and DBSCAN algorithm. Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter 8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers web-based data visualizations with Plotly. Chapter 10 discusses web scraping. The remainder of the book discusses various projects in data science.
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
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.