{"product_id":"applied-math-with-python-solve-real-world-problems-with-python-based-solutions-paperback-softback-9781394370757","title":"Applied Math with Python; Solve Real-World Problems with Python-Based Solutions (Paperback \/ softback) 9781394370757","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eApplied Math with Python\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eSolve Real-World Problems with Python-Based Solutions\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eBlake Rayfield (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394370757, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 1 June 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e288 pages\u003cbr\u003e22.9 x 18.3 x 1.5 cm, 0.59 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\u003eA step-by-step guide for using Python to transform abstract mathematical concepts into effective, on-the-ground scripts that solve real-world business problems\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eApplied Math with Python: Solve Real-World Problems with Python-Based Solutions\u003c\/i\u003e is a detailed, step-by-step guide for business professionals, analysts, and data scientists interested in using Python to perform crucial organizational tasks: optimizing inefficient supply chains, calculating probabilities, forecasting financial performance, mining customer data for new insights, and more. \u003c\/p\u003e\n\u003cp\u003eAuthor, researcher, and Assistant Professor of Finance at the University of North Florida, Blake Rayfield uses practical examples and hands-on exercises to explain how to combine concepts from optimization, probability, statistics, and other branches of mathematics with the Python language to solve difficult, common business problems. You’ll discover how marketing managers can use Python to create useful customer segments, how to model revenue growth, and how to allocate limited resources in a product launch or expansion. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eInside the book:\u003c\/b\u003e \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eModular, plug-and-play strategies for solving hard problems in Python in situations where a spreadsheet is inadequate\u003c\/li\u003e \u003cli\u003eInstructions for building effective, scalable Python scripts incorporating many of the most powerful Python libraries, including pandas, NumPy, matplotlib, seaborn, scikit-learn, and Plotly\u003c\/li\u003e \u003cli\u003eStart-to-finish coverage for business professionals – from building a Python scripting environment on your local computer or in a cloud environment to designing, writing, testing, and running a functional script\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for entrepreneurs, analysts, managers, and professionals working in AI, data science, and finance, \u003ci\u003eApplied Math with Python\u003c\/i\u003e is an expert guide for transforming abstract mathematical concepts into useful, repeatable, scalable solutions you can put to work immediately in your team and in your organization.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Getting Started\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Introduction to Python for Business Applications 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Python for Business 3\u003c\/p\u003e \u003cp\u003eWhy Python, Not a Spreadsheet? 4\u003c\/p\u003e \u003cp\u003eSetting Up Your Tools 5\u003c\/p\u003e \u003cp\u003eInstall Python with the Anaconda Distribution (Running Python on Your Machine) 5\u003c\/p\u003e \u003cp\u003eLaunch Jupyter Notebook 6\u003c\/p\u003e \u003cp\u003eCloud-friendly Alternatives 6\u003c\/p\u003e \u003cp\u003eThe Python Ecosystem 7\u003c\/p\u003e \u003cp\u003eWhat Is a (Jupyter) Notebook? 8\u003c\/p\u003e \u003cp\u003eInstalling Libraries Locally or in a Notebook 8\u003c\/p\u003e \u003cp\u003eWriting Your First Python Script 9\u003c\/p\u003e \u003cp\u003eSummary 10\u003c\/p\u003e \u003cp\u003eContinue Your Learning 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Basic Mathematical Operations in Python 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNumbers, Variables, and Functions: The Foundations of Business Logic 11\u003c\/p\u003e \u003cp\u003eUnderstanding Variables 12\u003c\/p\u003e \u003cp\u003eArithmetic in Python 13\u003c\/p\u003e \u003cp\u003eWorking with the math Module 14\u003c\/p\u003e \u003cp\u003eData Types in Python 14\u003c\/p\u003e \u003cp\u003eCore Data Types 14\u003c\/p\u003e \u003cp\u003eWhy Data Types Matter 16\u003c\/p\u003e \u003cp\u003eConverting Between Types 17\u003c\/p\u003e \u003cp\u003eBusiness Data Structures: Arrays and Matrices 18\u003c\/p\u003e \u003cp\u003eOne-dimensional Arrays 18\u003c\/p\u003e \u003cp\u003eMatrices: Two-dimensional Arrays 19\u003c\/p\u003e \u003cp\u003eData Manipulation Basics with Pandas 23\u003c\/p\u003e \u003cp\u003eConstructing a DataFrame 23\u003c\/p\u003e \u003cp\u003eFirst Looks: head(), info(), describe() 24\u003c\/p\u003e \u003cp\u003eWorking with Columns and Rows 24\u003c\/p\u003e \u003cp\u003eFiltering with Booleans 25\u003c\/p\u003e \u003cp\u003eCreating New Columns 25\u003c\/p\u003e \u003cp\u003eGrouping and Aggregation 26\u003c\/p\u003e \u003cp\u003eJoins and Merges 27\u003c\/p\u003e \u003cp\u003eReshaping: Pivot, Melt, Stack 28\u003c\/p\u003e \u003cp\u003eSummary 28\u003c\/p\u003e \u003cp\u003eContinue Your Learning 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Visualization for Business Decision-making 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Landscape of Visualization Tools in Python 29\u003c\/p\u003e \u003cp\u003eVisualization Applications: Dashboarding Frameworks 30\u003c\/p\u003e \u003cp\u003eChoosing the Right Visualization Tool for Your Work 31\u003c\/p\u003e \u003cp\u003eGraphing Basics with Matplotlib 32\u003c\/p\u003e \u003cp\u003eUnderstanding the Structure of a Plot 32\u003c\/p\u003e \u003cp\u003eCreating and Working with Plots 33\u003c\/p\u003e \u003cp\u003eCustomizing Visualizations to Enhance Understanding 35\u003c\/p\u003e \u003cp\u003ePlotting Options 36\u003c\/p\u003e \u003cp\u003eCreating Effective Visuals to Communicate Business Data 37\u003c\/p\u003e \u003cp\u003eTime-series Data and Line Charts 38\u003c\/p\u003e \u003cp\u003eCross-sectional Data and Bar or Pie Charts 38\u003c\/p\u003e \u003cp\u003eRelational Data and Scatterplots 39\u003c\/p\u003e \u003cp\u003eOther Charts You Can Create 41\u003c\/p\u003e \u003cp\u003eVisualizing Trends and Patterns for Business Insights 42\u003c\/p\u003e \u003cp\u003eHighlighting Seasonality and Long-term Growth 42\u003c\/p\u003e \u003cp\u003eComparing Categories and Segments 44\u003c\/p\u003e \u003cp\u003eVisualizing Cumulative Effects 46\u003c\/p\u003e \u003cp\u003eSmoothing Trends with Rolling Averages 47\u003c\/p\u003e \u003cp\u003eLine Charts with Confidence Intervals Using Seaborn 49\u003c\/p\u003e \u003cp\u003eAnalyzing Relationships and Distributions with jointplot 52\u003c\/p\u003e \u003cp\u003eSummary 55\u003c\/p\u003e \u003cp\u003eContinue Your Learning 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Applying the Math\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Linear Algebra for Business and Finance 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWorking with Vectors and Matrices 59\u003c\/p\u003e \u003cp\u003eUnderstanding Vectors 60\u003c\/p\u003e \u003cp\u003eUnderstanding Matrix 61\u003c\/p\u003e \u003cp\u003eOperations with Vectors and Matrices 62\u003c\/p\u003e \u003cp\u003eScalar Multiplication 63\u003c\/p\u003e \u003cp\u003eThe Dot Product 63\u003c\/p\u003e \u003cp\u003eNorms (Vector Lengths) 64\u003c\/p\u003e \u003cp\u003eCombining Matrices 64\u003c\/p\u003e \u003cp\u003eSlicing Matrices 65\u003c\/p\u003e \u003cp\u003eMatrix Multiplication 66\u003c\/p\u003e \u003cp\u003eTranspose 67\u003c\/p\u003e \u003cp\u003eCreating and Manipulating Vectors (and Matrices) with NumPy 67\u003c\/p\u003e \u003cp\u003eStep 1: Compute Asset Returns from Prices 69\u003c\/p\u003e \u003cp\u003eStep 2: Portfolio with Constant Weights 70\u003c\/p\u003e \u003cp\u003eStep 3: Portfolio with Time-varying Weights 72\u003c\/p\u003e \u003cp\u003eComparing Strategies (Same Math, Different Inputs) 75\u003c\/p\u003e \u003cp\u003eEigenvalues and Eigenvectors: Business Applications 76\u003c\/p\u003e \u003cp\u003eWhat Eigenvalues and Eigenvectors Represent 76\u003c\/p\u003e \u003cp\u003eWhy Eigenvalues Matter for Long-term Stability 77\u003c\/p\u003e \u003cp\u003eSummary 80\u003c\/p\u003e \u003cp\u003eContinue Your Learning 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Calculus for Business Problem Solving 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNumerical Differentiation and Integration in Business Analytics 84\u003c\/p\u003e \u003cp\u003eThe Derivative: Finding the Rate of Change 84\u003c\/p\u003e \u003cp\u003eThe Second Derivative: Pinpointing the Point of Diminishing Returns 86\u003c\/p\u003e \u003cp\u003eThe Integral: Accumulating the Totals 87\u003c\/p\u003e \u003cp\u003eThe Calculus Ecosystem in Python 90\u003c\/p\u003e \u003cp\u003eNumerical Calculus with NumPy 90\u003c\/p\u003e \u003cp\u003eSymbolic Calculus with SymPy 91\u003c\/p\u003e \u003cp\u003eAdvanced Numerical Methods with SciPy 92\u003c\/p\u003e \u003cp\u003eChoosing the Right Tool 93\u003c\/p\u003e \u003cp\u003eSolving Business Growth and Pricing Models with Differential Equations 93\u003c\/p\u003e \u003cp\u003eSensitivity Analysis with Partial Derivatives 96\u003c\/p\u003e \u003cp\u003eCase Study: Revenue, Cost, and Profit Analysis 98\u003c\/p\u003e \u003cp\u003eStep 1: Understanding Marginal Cost (the Derivative of Cost) 99\u003c\/p\u003e \u003cp\u003eStep 2: Understanding Marginal Revenue (the Derivative of Revenue) 100\u003c\/p\u003e \u003cp\u003eStep 3: Finding the Sweet Spot with Marginal Profit 102\u003c\/p\u003e \u003cp\u003eSummary 104\u003c\/p\u003e \u003cp\u003eContinue Your Learning 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Optimization Techniques for Business Strategy 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Python Optimization Ecosystem 108\u003c\/p\u003e \u003cp\u003eA Framework for Solving Most Optimization Problems 109\u003c\/p\u003e \u003cp\u003eThe Four-step Formulation Process 109\u003c\/p\u003e \u003cp\u003eUnderstanding the Local vs. Global Optima Issue 110\u003c\/p\u003e \u003cp\u003eApplying the Framework: Profit Maximization 110\u003c\/p\u003e \u003cp\u003eLinear Programming 112\u003c\/p\u003e \u003cp\u003eConstrained Optimization 116\u003c\/p\u003e \u003cp\u003eThe Geometry of Optimization 116\u003c\/p\u003e \u003cp\u003eVisualizing the Difference Between Constrained and Unconstrained Optimization 119\u003c\/p\u003e \u003cp\u003eReal-world Applications 122\u003c\/p\u003e \u003cp\u003ePortfolio Allocation 122\u003c\/p\u003e \u003cp\u003eSupply Chain and Operations 128\u003c\/p\u003e \u003cp\u003eInteger Programming for Workforce Scheduling 131\u003c\/p\u003e \u003cp\u003eSummary 134\u003c\/p\u003e \u003cp\u003eContinue Your Learning 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Probability and Statistics for Business Analytics 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Python Statistics Ecosystems 137\u003c\/p\u003e \u003cp\u003eUnderstanding Random Variables and Distributions in Business Contexts 138\u003c\/p\u003e \u003cp\u003eDiscrete vs. Continuous Distributions 139\u003c\/p\u003e \u003cp\u003eThe Most Common Business Distributions 140\u003c\/p\u003e \u003cp\u003eHypothesis Testing 144\u003c\/p\u003e \u003cp\u003eTest Statistics 145\u003c\/p\u003e \u003cp\u003eThe p-value 146\u003c\/p\u003e \u003cp\u003eThe A\/B Test 147\u003c\/p\u003e \u003cp\u003eConfidence Intervals: The Other Side of the Coin 148\u003c\/p\u003e \u003cp\u003eLinear Regression 149\u003c\/p\u003e \u003cp\u003eAnalyzing Marketing Effectiveness 151\u003c\/p\u003e \u003cp\u003eExplaining Financial Risk Factors 153\u003c\/p\u003e \u003cp\u003eOther Considerations 155\u003c\/p\u003e \u003cp\u003eLogistic Regression 156\u003c\/p\u003e \u003cp\u003ePredicting Customer Churn 156\u003c\/p\u003e \u003cp\u003eForecasting 161\u003c\/p\u003e \u003cp\u003eSummary 164\u003c\/p\u003e \u003cp\u003eContinue Your Learning 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Applied Business Problems with Math and Python 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuilding a Dynamic Loan Amortization Engine 168\u003c\/p\u003e \u003cp\u003eBuilding a Simple Recommender System 171\u003c\/p\u003e \u003cp\u003eMaximizing Yield with Constrained Optimization 173\u003c\/p\u003e \u003cp\u003eQuality Control with Hypothesis Testing 177\u003c\/p\u003e \u003cp\u003ePredicting Employee Attrition with Logistic Regression 179\u003c\/p\u003e \u003cp\u003eSummary 185\u003c\/p\u003e \u003cp\u003eContinue Your Learning 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Visualizing the Numbers\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Illustrating Time-series and Linear Data 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Your Data Structure 189\u003c\/p\u003e \u003cp\u003eCross-sectional Data 190\u003c\/p\u003e \u003cp\u003eTime-series Data 192\u003c\/p\u003e \u003cp\u003ePanel Data 193\u003c\/p\u003e \u003cp\u003eVisualizing Change Over Time (Time-series) 194\u003c\/p\u003e \u003cp\u003eTime-series Diagnostics 195\u003c\/p\u003e \u003cp\u003eSeasonality and Autocorrelation 201\u003c\/p\u003e \u003cp\u003ePanel Data 206\u003c\/p\u003e \u003cp\u003eSummary 208\u003c\/p\u003e \u003cp\u003eContinue Your Learning 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: Illustrating Cross-sectional Data 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Categories 211\u003c\/p\u003e \u003cp\u003eThe Pie Chart 211\u003c\/p\u003e \u003cp\u003eDonut Charts 213\u003c\/p\u003e \u003cp\u003eStacked Bar Charts 216\u003c\/p\u003e \u003cp\u003eCorrelations and Distributions 217\u003c\/p\u003e \u003cp\u003eBar Charts 218\u003c\/p\u003e \u003cp\u003eBoxplots 220\u003c\/p\u003e \u003cp\u003eCorrelations in the Cross Section 222\u003c\/p\u003e \u003cp\u003eScatterplots 222\u003c\/p\u003e \u003cp\u003eCorrelation Heatmaps 225\u003c\/p\u003e \u003cp\u003eThe Pair Plot 227\u003c\/p\u003e \u003cp\u003eSummary 229\u003c\/p\u003e \u003cp\u003eContinue Your Learning 230\u003c\/p\u003e \u003cp\u003eEssential Cross-sectional Functions 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Illustrating Alternative Data Types 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTextual Analysis 233\u003c\/p\u003e \u003cp\u003eThe Word Cloud 234\u003c\/p\u003e \u003cp\u003eN-grams 236\u003c\/p\u003e \u003cp\u003eVisualizing Customer Sentiment 239\u003c\/p\u003e \u003cp\u003eGeospatial Data 242\u003c\/p\u003e \u003cp\u003eThe Choropleth Map 243\u003c\/p\u003e \u003cp\u003eThe Marker Map 244\u003c\/p\u003e \u003cp\u003eThe Heatmap 246\u003c\/p\u003e \u003cp\u003eVisualizing Networks 248\u003c\/p\u003e \u003cp\u003eVisualizing Structure 249\u003c\/p\u003e \u003cp\u003eWeighted Graphs 252\u003c\/p\u003e \u003cp\u003eSummary 254\u003c\/p\u003e \u003cp\u003eContinue Your Learning 254\u003c\/p\u003e \u003cp\u003eIndex 257\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52458343694616,"sku":"9781394370757","price":24.28,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394370757.jpg?v=1785371273","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/applied-math-with-python-solve-real-world-problems-with-python-based-solutions-paperback-softback-9781394370757","provider":"Freshly Printed Books","version":"1.0","type":"link"}