{"product_id":"data-science-essentials-for-dummies-paperback-softback-9781394297009","title":"Data Science Essentials For Dummies (Paperback \/ softback) 9781394297009","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Science Essentials For Dummies\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eLillian Pierson (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394297009, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 19 December 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e192 pages\u003cbr\u003e21.3 x 14 x 1.3 cm, 0.181 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\u003eFeel confident navigating the fundamentals of data science\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eData Science Essentials For Dummies\u003c\/i\u003e is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast. \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eStrengthen your understanding of data science basics\u003c\/li\u003e \u003cli\u003eReview what you've already learned or pick up key skills\u003c\/li\u003e \u003cli\u003eEffectively work with data and provide accessible materials to others\u003c\/li\u003e \u003cli\u003eJog your memory on the essentials as you work and get clear answers to your questions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, \u003ci\u003eData Science Essentials For Dummies\u003c\/i\u003e is a reliable reference that's great to keep on hand as an everyday desk reference.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eAbout This Book 2\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 3\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Wrapping Your Head Around Data Science 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSeeing Who Can Make Use of Data Science 6\u003c\/p\u003e \u003cp\u003eInspecting the Pieces of the Data Science Puzzle 8\u003c\/p\u003e \u003cp\u003eCollecting, querying, and consuming data 9\u003c\/p\u003e \u003cp\u003eApplying mathematical modeling to data science tasks 11\u003c\/p\u003e \u003cp\u003eDeriving insights from statistical methods 11\u003c\/p\u003e \u003cp\u003eCoding, coding, coding — it’s just part of the game 12\u003c\/p\u003e \u003cp\u003eApplying data science to a subject area 12\u003c\/p\u003e \u003cp\u003eCommunicating data insights 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Tapping into Critical Aspects of Data Engineering 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining the Three Vs 15\u003c\/p\u003e \u003cp\u003eGrappling with data volume 16\u003c\/p\u003e \u003cp\u003eHandling data velocity 16\u003c\/p\u003e \u003cp\u003eDealing with data variety 17\u003c\/p\u003e \u003cp\u003eIdentifying Important Data Sources 18\u003c\/p\u003e \u003cp\u003eGrasping the Differences among Data Approaches 18\u003c\/p\u003e \u003cp\u003eDefining data science 19\u003c\/p\u003e \u003cp\u003eDefining machine learning engineering 20\u003c\/p\u003e \u003cp\u003eDefining data engineering 20\u003c\/p\u003e \u003cp\u003eComparing machine learning engineers, data scientists, and data engineers 21\u003c\/p\u003e \u003cp\u003eStoring and Processing Data for Data Science 22\u003c\/p\u003e \u003cp\u003eStoring data and doing data science directly in the cloud 22\u003c\/p\u003e \u003cp\u003eProcessing data in real-time 27\u003c\/p\u003e \u003cp\u003eRecognizing the Impact of Generative AI 27\u003c\/p\u003e \u003cp\u003eThe reshaping of data engineering 28\u003c\/p\u003e \u003cp\u003eTools and frameworks for supporting AI workloads 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Using a Machine to Learn from Data 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Machine Learning and Its Processes 29\u003c\/p\u003e \u003cp\u003eWalking through the steps of the machine learning process 30\u003c\/p\u003e \u003cp\u003eBecoming familiar with machine learning terms 30\u003c\/p\u003e \u003cp\u003eConsidering Learning Styles 31\u003c\/p\u003e \u003cp\u003eLearning with supervised algorithms 31\u003c\/p\u003e \u003cp\u003eLearning with unsupervised algorithms 32\u003c\/p\u003e \u003cp\u003eLearning with reinforcement 32\u003c\/p\u003e \u003cp\u003eSeeing What You Can Do 32\u003c\/p\u003e \u003cp\u003eSelecting algorithms based on function 33\u003c\/p\u003e \u003cp\u003eGenerating real-time analytics with Spark 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Math, Probability, and Statistical Modeling 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring Probability and Inferential Statistics 40\u003c\/p\u003e \u003cp\u003eProbability distributions 42\u003c\/p\u003e \u003cp\u003eConditional probability with Naïve Bayes 44\u003c\/p\u003e \u003cp\u003eQuantifying Correlation 45\u003c\/p\u003e \u003cp\u003eCalculating correlation with Pearson’s r 45\u003c\/p\u003e \u003cp\u003eRanking variable pairs using Spearman’s rank correlation 47\u003c\/p\u003e \u003cp\u003eReducing Data Dimensionality with Linear Algebra 48\u003c\/p\u003e \u003cp\u003eDecomposing data to reduce dimensionality 48\u003c\/p\u003e \u003cp\u003eReducing dimensionality with factor analysis 52\u003c\/p\u003e \u003cp\u003eDecreasing dimensionality and removing outliers with PCA 53\u003c\/p\u003e \u003cp\u003eModeling Decisions with Multiple Criteria Decision-Making 54\u003c\/p\u003e \u003cp\u003eTurning to traditional MCDM 55\u003c\/p\u003e \u003cp\u003eFocusing on fuzzy MCDM 57\u003c\/p\u003e \u003cp\u003eIntroducing Regression Methods 57\u003c\/p\u003e \u003cp\u003eLinear regression 57\u003c\/p\u003e \u003cp\u003eLogistic regression 59\u003c\/p\u003e \u003cp\u003eOrdinary least squares regression methods 60\u003c\/p\u003e \u003cp\u003eDetecting Outliers 60\u003c\/p\u003e \u003cp\u003eAnalyzing extreme values 60\u003c\/p\u003e \u003cp\u003eDetecting outliers with univariate analysis 61\u003c\/p\u003e \u003cp\u003eDetecting outliers with multivariate analysis 62\u003c\/p\u003e \u003cp\u003eIntroducing Time Series Analysis 64\u003c\/p\u003e \u003cp\u003eIdentifying patterns in time series 64\u003c\/p\u003e \u003cp\u003eModeling univariate time series data 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Grouping Your Way into Accurate Predictions 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStarting with Clustering Basics 68\u003c\/p\u003e \u003cp\u003eGetting to know clustering algorithms 69\u003c\/p\u003e \u003cp\u003eExamining clustering similarity metrics 71\u003c\/p\u003e \u003cp\u003eIdentifying Clusters in Your Data 72\u003c\/p\u003e \u003cp\u003eClustering with the k-means algorithm 72\u003c\/p\u003e \u003cp\u003eEstimating clusters with kernel density estimation 74\u003c\/p\u003e \u003cp\u003eClustering with hierarchical algorithms 75\u003c\/p\u003e \u003cp\u003eDabbling in the DBScan neighborhood 77\u003c\/p\u003e \u003cp\u003eCategorizing Data with Decision Tree and Random Forest Algorithms 79\u003c\/p\u003e \u003cp\u003eDrawing a Line between Clustering and Classification 80\u003c\/p\u003e \u003cp\u003eIntroducing instance-based learning classifiers 81\u003c\/p\u003e \u003cp\u003eGetting to know classification algorithms 81\u003c\/p\u003e \u003cp\u003eMaking Sense of Data with Nearest Neighbor Analysis 84\u003c\/p\u003e \u003cp\u003eClassifying Data with Average Nearest Neighbor Algorithms 86\u003c\/p\u003e \u003cp\u003eClassifying with K-Nearest Neighbor Algorithms 89\u003c\/p\u003e \u003cp\u003eUnderstanding how the k-nearest neighbor algorithm works 90\u003c\/p\u003e \u003cp\u003eKnowing when to use the k-nearest neighbor algorithm 91\u003c\/p\u003e \u003cp\u003eExploring common applications of k-nearest neighbor algorithms 92\u003c\/p\u003e \u003cp\u003eSolving Real-World Problems with Nearest Neighbor Algorithms 92\u003c\/p\u003e \u003cp\u003eSeeing k-nearest neighbor algorithms in action 92\u003c\/p\u003e \u003cp\u003eSeeing average nearest neighbor algorithms in action 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Coding Up Data Insights and Decision Engines 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSeeing Where Python Fits into Your Data Science Strategy 95\u003c\/p\u003e \u003cp\u003eUsing Python for Data Science 96\u003c\/p\u003e \u003cp\u003eSorting out the various Python data types 98\u003c\/p\u003e \u003cp\u003ePutting loops to good use in Python 101\u003c\/p\u003e \u003cp\u003eHaving fun with functions 103\u003c\/p\u003e \u003cp\u003eKeeping cool with classes 104\u003c\/p\u003e \u003cp\u003eChecking out some useful Python libraries 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Generating Insights with Software Applications 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChoosing the Best Tools for Your Data Science Strategy 116\u003c\/p\u003e \u003cp\u003eGetting a Handle on SQL and Relational Databases 118\u003c\/p\u003e \u003cp\u003eInvesting Some Effort into Database Design 123\u003c\/p\u003e \u003cp\u003eDefining data types 123\u003c\/p\u003e \u003cp\u003eDesigning constraints properly 124\u003c\/p\u003e \u003cp\u003eNormalizing your database 124\u003c\/p\u003e \u003cp\u003eNarrowing the Focus with SQL Functions 127\u003c\/p\u003e \u003cp\u003eMaking Life Easier with Excel 131\u003c\/p\u003e \u003cp\u003eUsing Excel to quickly get to know your data 132\u003c\/p\u003e \u003cp\u003eReformatting and summarizing with PivotTables 137\u003c\/p\u003e \u003cp\u003eAutomating Excel tasks with macros 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Telling Powerful Stories with Data 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Visualizations: The Big Three 144\u003c\/p\u003e \u003cp\u003eData storytelling for decision-makers 145\u003c\/p\u003e \u003cp\u003eData showcasing for analysts 145\u003c\/p\u003e \u003cp\u003eDesigning data art for activists 146\u003c\/p\u003e \u003cp\u003eDesigning to Meet the Needs of Your Target Audience 146\u003c\/p\u003e \u003cp\u003eStep 1: Brainstorm (All about Eve) 147\u003c\/p\u003e \u003cp\u003eStep 2: Define the purpose 148\u003c\/p\u003e \u003cp\u003eStep 3: Choose the most functional visualization type for your purpose 149\u003c\/p\u003e \u003cp\u003ePicking the Most Appropriate Design Style 150\u003c\/p\u003e \u003cp\u003eInducing a calculating, exacting response 150\u003c\/p\u003e \u003cp\u003eEliciting a strong emotional response 151\u003c\/p\u003e \u003cp\u003eSelecting the Appropriate Data Graphic Type 152\u003c\/p\u003e \u003cp\u003eStandard chart graphics 154\u003c\/p\u003e \u003cp\u003eComparative graphics 157\u003c\/p\u003e \u003cp\u003eStatistical plots 161\u003c\/p\u003e \u003cp\u003eTopology structures 162\u003c\/p\u003e \u003cp\u003eSpatial plots and maps 164\u003c\/p\u003e \u003cp\u003eTesting Data Graphics 167\u003c\/p\u003e \u003cp\u003eAdding Context 168\u003c\/p\u003e \u003cp\u003eCreating context with data 169\u003c\/p\u003e \u003cp\u003eCreating context with annotations 169\u003c\/p\u003e \u003cp\u003eCreating context with graphical elements 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Ten Free or Low-Cost Data Science Libraries and Platforms 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eScraping the Web with Beautiful Soup 171\u003c\/p\u003e \u003cp\u003eWrangling Data with pandas 172\u003c\/p\u003e \u003cp\u003eVisualizing Data with Looker Studio 172\u003c\/p\u003e \u003cp\u003eMachine Learning with scikit-learn 172\u003c\/p\u003e \u003cp\u003eCreating Interactive Dashboards with Streamlit 173\u003c\/p\u003e \u003cp\u003eDoing Geospatial Data Visualization with Kepler.gl 173\u003c\/p\u003e \u003cp\u003eMaking Charts with Tableau Public 173\u003c\/p\u003e \u003cp\u003eDoing Web-Based Data Visualization with RAWGraphs 174\u003c\/p\u003e \u003cp\u003eMaking Cool Infographics with Infogram 174\u003c\/p\u003e \u003cp\u003eMaking Cool Infographics with Canva 174\u003c\/p\u003e \u003cp\u003eIndex 175\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Business applications [\u003ca title=\"See our other books on Business applications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Business%20applications%20%5BUF%5D%22\"\u003eUF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"For Dummies","offers":[{"title":"Brand 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