Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with…mehr
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora
After previous careers in physics and softwaredevelopment, Philipp K. Janert currentlyprovides consulting services for data analysis,algorithm development, and mathematical modeling.He has worked for small start-ups and in largecorporate environments, both in the U.S. andoverseas. He prefers simple solutions that workto complicated ones that don't, and thinks thatpurpose is more important than process. Philippis the author of "Gnuplot in Action - UnderstandingData with Graphs" (Manning Publications), and haswritten for the O'Reilly Network, IBM developerWorks,and IEEE Software. He is named inventor on a handfulof patents, and is an occasional contributor to CPAN.He holds a Ph.D. in theoretical physics from theUniversity of Washington. Visit his company websiteat www.principal-value.com.
Inhaltsangabe
Preface Before We Begin Conventions Used in This Book Using Code Examples Safari® Books Online How to Contact Us Acknowledgments Chapter 1: Introduction 1.1 Data Analysis 1.2 What's in This Book 1.3 What's with the Workshops? 1.4 What's with the Math? 1.5 What You'll Need 1.6 What's Missing Graphics: Looking at Data Chapter 2: A Single Variable: Shape and Distribution 2.1 Dot and Jitter Plots 2.2 Histograms and Kernel Density Estimates 2.3 The Cumulative Distribution Function 2.4 Rank-Order Plots and Lift Charts 2.5 Only When Appropriate: Summary Statistics and Box Plots 2.6 Workshop: NumPy 2.7 Further Reading Chapter 3: Two Variables: Establishing Relationships 3.1 Scatter Plots 3.2 Conquering Noise: Smoothing 3.3 Logarithmic Plots 3.4 Banking 3.5 Linear Regression and All That 3.6 Showing What's Important 3.7 Graphical Analysis and Presentation Graphics 3.8 Workshop: matplotlib 3.9 Further Reading Chapter 4: Time As a Variable: Time-Series Analysis 4.1 Examples 4.2 The Task 4.3 Smoothing 4.4 Don't Overlook the Obvious! 4.5 The Correlation Function 4.6 Optional: Filters and Convolutions 4.7 Workshop: scipy.signal 4.8 Further Reading Chapter 5: More Than Two Variables: Graphical Multivariate Analysis 5.1 False-Color Plots 5.2 A Lot at a Glance: Multiplots 5.3 Composition Problems 5.4 Novel Plot Types 5.5 Interactive Explorations 5.6 Workshop: Tools for Multivariate Graphics 5.7 Further Reading Chapter 6: Intermezzo: A Data Analysis Session 6.1 A Data Analysis Session 6.2 Workshop: gnuplot 6.3 Further Reading Analytics: Modeling Data Chapter 7: Guesstimation and the Back of the Envelope 7.1 Principles of Guesstimation 7.2 How Good Are Those Numbers? 7.3 Optional: A Closer Look at Perturbation Theory and Error Propagation 7.4 Workshop: The Gnu Scientific Library (GSL) 7.5 Further Reading Chapter 8: Models from Scaling Arguments 8.1 Models 8.2 Arguments from Scale 8.3 Mean-Field Approximations 8.4 Common Time-Evolution Scenarios 8.5 Case Study: How Many Servers Are Best? 8.6 Why Modeling? 8.7 Workshop: Sage 8.8 Further Reading Chapter 9: Arguments from Probability Models 9.1 The Binomial Distribution and Bernoulli Trials 9.2 The Gaussian Distribution and the Central Limit Theorem 9.3 Power-Law Distributions and Non-Normal Statistics 9.4 Other Distributions 9.5 Optional: Case Study-Unique Visitors over Time 9.6 Workshop: Power-Law Distributions 9.7 Further Reading Chapter 10: What You Really Need to Know About Classical Statistics 10.1 Genesis 10.2 Statistics Defined 10.3 Statistics Explained 10.4 Controlled Experiments Versus Observational Studies 10.5 Optional: Bayesian Statistics-The Other Point of View 10.6 Workshop: R 10.7 Further Reading Chapter 11: Intermezzo: Mythbusting-Bigfoot, Least Squares, and All That 11.1 How to Average Averages 11.2 The Standard Deviation 11.3 Least Squares 11.4 Further Reading Computation: Mining Data Chapter 12: Simulations 12.1 A Warm-Up Question 12.2 Monte Carlo Simulations 12.3 Resampling Methods 12.4 Workshop: Discrete Event Simulations with SimPy 12.5 Further Reading Chapter 13: Finding Clusters 13.1 What Constitutes a Cluster? 13.2 Distance and Similarity Measures 13.3 Clustering Methods 13.4 Pre- and Postprocessing 13.5 Other Thoughts 13.6 A Special Case: Market Basket Analysis 13.7 A Word of Warning 13.8 Workshop: Pycluster and the C Clustering Library 13.9 Further Reading Chapter 14: Seeing the Forest for the Trees: Finding Important Attributes 14.1 Principal Component Analysis 14.2 Visual Techniques 14.3 Kohonen Maps 14.4 Workshop: PCA with R 14.5 Further Reading Chapter 15: Intermezzo: When More Is Different 15.1 A Horror Story 15.2 Some Suggestions 15.3 What About Map/Reduce? 15.4 Workshop: Generating Permutations 15.5 Further Reading Applications: Using Data Chapter 16: Reporting, Business Intelligence, and Dashboards 16.1 Business Intelligence 16.2 Corporate Metrics and Dashboards 16.3 Data Quality Issues 16.4 Workshop: Berkeley DB and SQLite 16.5 Further Reading Chapter 17: Financial Calculations and Modeling 17.1 The Time Value of Money 17.2 Uncertainty in Planning and Opportunity Costs 17.3 Cost Concepts and Depreciation 17.4 Should You Care? 17.5 Is This All That Matters? 17.6 Workshop: The Newsvendor Problem 17.7 Further Reading Chapter 18: Predictive Analytics 18.1 Topics in Predictive Analytics 18.2 Some Classification Terminology 18.3 Algorithms for Classification 18.4 The Process 18.5 The Secret Sauce 18.6 The Nature of Statistical Learning 18.7 Workshop: Two Do-It-Yourself Classifiers 18.8 Further Reading Chapter 19: Epilogue: Facts Are Not Reality Programming Environments for Scientific Computation and Data Analysis Software Tools A Catalog of Scientific Software Writing Your Own Further Reading Results from Calculus Common Functions Calculus Useful Tricks Notation and Basic Math Where to Go from Here Further Reading Working with Data Sources for Data Cleaning and Conditioning Sampling Data File Formats The Care and Feeding of Your Data Zoo Skills Terminology Further Reading About the Author Colophon
Preface Before We Begin Conventions Used in This Book Using Code Examples Safari® Books Online How to Contact Us Acknowledgments Chapter 1: Introduction 1.1 Data Analysis 1.2 What's in This Book 1.3 What's with the Workshops? 1.4 What's with the Math? 1.5 What You'll Need 1.6 What's Missing Graphics: Looking at Data Chapter 2: A Single Variable: Shape and Distribution 2.1 Dot and Jitter Plots 2.2 Histograms and Kernel Density Estimates 2.3 The Cumulative Distribution Function 2.4 Rank-Order Plots and Lift Charts 2.5 Only When Appropriate: Summary Statistics and Box Plots 2.6 Workshop: NumPy 2.7 Further Reading Chapter 3: Two Variables: Establishing Relationships 3.1 Scatter Plots 3.2 Conquering Noise: Smoothing 3.3 Logarithmic Plots 3.4 Banking 3.5 Linear Regression and All That 3.6 Showing What's Important 3.7 Graphical Analysis and Presentation Graphics 3.8 Workshop: matplotlib 3.9 Further Reading Chapter 4: Time As a Variable: Time-Series Analysis 4.1 Examples 4.2 The Task 4.3 Smoothing 4.4 Don't Overlook the Obvious! 4.5 The Correlation Function 4.6 Optional: Filters and Convolutions 4.7 Workshop: scipy.signal 4.8 Further Reading Chapter 5: More Than Two Variables: Graphical Multivariate Analysis 5.1 False-Color Plots 5.2 A Lot at a Glance: Multiplots 5.3 Composition Problems 5.4 Novel Plot Types 5.5 Interactive Explorations 5.6 Workshop: Tools for Multivariate Graphics 5.7 Further Reading Chapter 6: Intermezzo: A Data Analysis Session 6.1 A Data Analysis Session 6.2 Workshop: gnuplot 6.3 Further Reading Analytics: Modeling Data Chapter 7: Guesstimation and the Back of the Envelope 7.1 Principles of Guesstimation 7.2 How Good Are Those Numbers? 7.3 Optional: A Closer Look at Perturbation Theory and Error Propagation 7.4 Workshop: The Gnu Scientific Library (GSL) 7.5 Further Reading Chapter 8: Models from Scaling Arguments 8.1 Models 8.2 Arguments from Scale 8.3 Mean-Field Approximations 8.4 Common Time-Evolution Scenarios 8.5 Case Study: How Many Servers Are Best? 8.6 Why Modeling? 8.7 Workshop: Sage 8.8 Further Reading Chapter 9: Arguments from Probability Models 9.1 The Binomial Distribution and Bernoulli Trials 9.2 The Gaussian Distribution and the Central Limit Theorem 9.3 Power-Law Distributions and Non-Normal Statistics 9.4 Other Distributions 9.5 Optional: Case Study-Unique Visitors over Time 9.6 Workshop: Power-Law Distributions 9.7 Further Reading Chapter 10: What You Really Need to Know About Classical Statistics 10.1 Genesis 10.2 Statistics Defined 10.3 Statistics Explained 10.4 Controlled Experiments Versus Observational Studies 10.5 Optional: Bayesian Statistics-The Other Point of View 10.6 Workshop: R 10.7 Further Reading Chapter 11: Intermezzo: Mythbusting-Bigfoot, Least Squares, and All That 11.1 How to Average Averages 11.2 The Standard Deviation 11.3 Least Squares 11.4 Further Reading Computation: Mining Data Chapter 12: Simulations 12.1 A Warm-Up Question 12.2 Monte Carlo Simulations 12.3 Resampling Methods 12.4 Workshop: Discrete Event Simulations with SimPy 12.5 Further Reading Chapter 13: Finding Clusters 13.1 What Constitutes a Cluster? 13.2 Distance and Similarity Measures 13.3 Clustering Methods 13.4 Pre- and Postprocessing 13.5 Other Thoughts 13.6 A Special Case: Market Basket Analysis 13.7 A Word of Warning 13.8 Workshop: Pycluster and the C Clustering Library 13.9 Further Reading Chapter 14: Seeing the Forest for the Trees: Finding Important Attributes 14.1 Principal Component Analysis 14.2 Visual Techniques 14.3 Kohonen Maps 14.4 Workshop: PCA with R 14.5 Further Reading Chapter 15: Intermezzo: When More Is Different 15.1 A Horror Story 15.2 Some Suggestions 15.3 What About Map/Reduce? 15.4 Workshop: Generating Permutations 15.5 Further Reading Applications: Using Data Chapter 16: Reporting, Business Intelligence, and Dashboards 16.1 Business Intelligence 16.2 Corporate Metrics and Dashboards 16.3 Data Quality Issues 16.4 Workshop: Berkeley DB and SQLite 16.5 Further Reading Chapter 17: Financial Calculations and Modeling 17.1 The Time Value of Money 17.2 Uncertainty in Planning and Opportunity Costs 17.3 Cost Concepts and Depreciation 17.4 Should You Care? 17.5 Is This All That Matters? 17.6 Workshop: The Newsvendor Problem 17.7 Further Reading Chapter 18: Predictive Analytics 18.1 Topics in Predictive Analytics 18.2 Some Classification Terminology 18.3 Algorithms for Classification 18.4 The Process 18.5 The Secret Sauce 18.6 The Nature of Statistical Learning 18.7 Workshop: Two Do-It-Yourself Classifiers 18.8 Further Reading Chapter 19: Epilogue: Facts Are Not Reality Programming Environments for Scientific Computation and Data Analysis Software Tools A Catalog of Scientific Software Writing Your Own Further Reading Results from Calculus Common Functions Calculus Useful Tricks Notation and Basic Math Where to Go from Here Further Reading Working with Data Sources for Data Cleaning and Conditioning Sampling Data File Formats The Care and Feeding of Your Data Zoo Skills Terminology Further Reading About the Author Colophon
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