{"product_id":"data-science-handbook-a-practical-approach-hardback-9781119857334","title":"Data Science Handbook; A Practical Approach (Hardback) 9781119857334","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Science Handbook\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Practical Approach\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eKolla Bhanu Prakash (Edited by), KB Prakash (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119857334, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 22 November 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e22.9 x 15.2 x 2.9 cm, 0.948 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eDATA SCIENCE HANDBOOK\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eThis 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. \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding.\u003c\/p\u003e \u003cp\u003eThe book starts with introductory concepts in data science like data munging, data preparation, and 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 of 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.\u003c\/p\u003e \u003cp\u003eThe book concludes with a section discussing 19 projects on various subjects in data science.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAudience \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe handbook will be used by graduate students up to research scholars in computer science and electrical engineering as well as industry professionals in a range of industries such as healthcare.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAcknowledgment xi\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Munging Basics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1 Filtering and Selecting Data 6\u003c\/p\u003e \u003cp\u003e1.2 Treating Missing Values 11\u003c\/p\u003e \u003cp\u003e1.3 Removing Duplicates 14\u003c\/p\u003e \u003cp\u003e1.4 Concatenating and Transforming Data 16\u003c\/p\u003e \u003cp\u003e1.5 Grouping and Data Aggregation 20\u003c\/p\u003e \u003cp\u003eReferences 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Data Visualization 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Creating Standard Plots (Line, Bar, Pie) 26\u003c\/p\u003e \u003cp\u003e2.2 Defining Elements of a Plot 30\u003c\/p\u003e \u003cp\u003e2.3 Plot Formatting 33\u003c\/p\u003e \u003cp\u003e2.4 Creating Labels and Annotations 38\u003c\/p\u003e \u003cp\u003e2.5 Creating Visualizations from Time Series Data 42\u003c\/p\u003e \u003cp\u003e2.6 Constructing Histograms, Box Plots, and Scatter Plots 44\u003c\/p\u003e \u003cp\u003eReferences 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Basic Math and Statistics 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Linear Algebra 57\u003c\/p\u003e \u003cp\u003e3.2 Calculus 58\u003c\/p\u003e \u003cp\u003e3.2.1 Differential Calculus 58\u003c\/p\u003e \u003cp\u003e3.2.2 Integral Calculus 58\u003c\/p\u003e \u003cp\u003e3.3 Inferential Statistics 60\u003c\/p\u003e \u003cp\u003e3.3.1 Central Limit Theorem 60\u003c\/p\u003e \u003cp\u003e3.3.2 Hypothesis Testing 60\u003c\/p\u003e \u003cp\u003e3.3.3 ANOVA 60\u003c\/p\u003e \u003cp\u003e3.3.4 Qualitative Data Analysis 60\u003c\/p\u003e \u003cp\u003e3.4 Using NumPy to Perform Arithmetic Operations on Data 61\u003c\/p\u003e \u003cp\u003e3.5 Generating Summary Statistics Using Pandas and Scipy 64\u003c\/p\u003e \u003cp\u003e3.6 Summarizing Categorical Data Using Pandas 68\u003c\/p\u003e \u003cp\u003e3.7 Starting with Parametric Methods in Pandas and Scipy 84\u003c\/p\u003e \u003cp\u003e3.8 Delving Into Non-Parametric Methods Using Pandas and Scipy 87\u003c\/p\u003e \u003cp\u003e3.9 Transforming Dataset Distributions 91\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Introduction to Machine Learning 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction to Machine Learning 97\u003c\/p\u003e \u003cp\u003e4.2 Types of Machine Learning Algorithms 101\u003c\/p\u003e \u003cp\u003e4.3 Explanatory Factor Analysis 114\u003c\/p\u003e \u003cp\u003e4.4 Principal Component Analysis (PCA) 115\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Outlier Analysis 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Extreme Value Analysis Using Univariate Methods 123\u003c\/p\u003e \u003cp\u003e5.2 Multivariate Analysis for Outlier Detection 125\u003c\/p\u003e \u003cp\u003e5.3 DBSCan Clustering to Identify Outliers 127\u003c\/p\u003e \u003cp\u003eReferences 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Cluster Analysis 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 K-Means Algorithm 135\u003c\/p\u003e \u003cp\u003e6.2 Hierarchial Methods 141\u003c\/p\u003e \u003cp\u003e6.3 Instance-Based Learning w\/ k-Nearest Neighbor 149\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Network Analysis with NetworkX 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Working with Graph Objects 159\u003c\/p\u003e \u003cp\u003e7.2 Simulating a Social Network (ie; Directed Network Analysis) 163\u003c\/p\u003e \u003cp\u003e7.3 Analyzing a Social Network 169\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Basic Algorithmic Learning 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Linear Regression 173\u003c\/p\u003e \u003cp\u003e8.2 Logistic Regression 183\u003c\/p\u003e \u003cp\u003e8.3 Naive Bayes Classifiers 189\u003c\/p\u003e \u003cp\u003eReferences 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Web-Based Data Visualizations with Plotly 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Collaborative Aanalytics 197\u003c\/p\u003e \u003cp\u003e9.2 Basic Charts 208\u003c\/p\u003e \u003cp\u003e9.3 Statistical Charts 212\u003c\/p\u003e \u003cp\u003e9.4 Plotly Maps 216\u003c\/p\u003e \u003cp\u003eReferences 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Web Scraping with Beautiful Soup 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 The BeautifulSoup Object 224\u003c\/p\u003e \u003cp\u003e10.2 Exploring NavigableString Objects 228\u003c\/p\u003e \u003cp\u003e10.3 Data Parsing 230\u003c\/p\u003e \u003cp\u003e10.4 Web Scraping 233\u003c\/p\u003e \u003cp\u003e10.5 Ensemble Models with Random Forests 235\u003c\/p\u003e \u003cp\u003eReferences 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003eData Science Projects 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Covid19 Detection and Prediction 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Leaf Disease Detection 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Brain Tumor Detection with Data Science 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Color Detection with Python 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Detecting Parkinson’s Disease 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Sentiment Analysis 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Road Lane Line Detection 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 315\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Fake News Detection 317\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Speech Emotion Recognition 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Gender and Age Detection with Data Science 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 339\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Diabetic Retinopathy 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 350\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Driver Drowsiness Detection in Python 351\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 356\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Chatbot Using Python 357\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Handwritten Digit Recognition Project 365\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 368\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Image Caption Generator Project in Python 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 379\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Credit Card Fraud Detection Project 381\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Movie Recommendation System 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 411\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Customer Segmentation 413\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 431\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Breast Cancer Classification 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 443\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Traffic Signs Recognition 445\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBibliography 453\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430980284696,"sku":"9781119857334","price":106.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119857334.jpg?v=1784767534","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-science-handbook-a-practical-approach-hardback-9781119857334","provider":"Freshly Printed Books","version":"1.0","type":"link"}