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Produktbild: Data Analysis with Open Source Tools

Data Analysis with Open Source Tools A hands-on guide for programmers and data scientists

33,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.12.2010

Verlag

O'Reilly Media

Seitenzahl

536

Maße (L/B/H)

23,3/18/3 cm

Gewicht

868 g

Sprache

Englisch

ISBN

978-0-596-80235-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.12.2010

Verlag

O'Reilly Media

Seitenzahl

536

Maße (L/B/H)

23,3/18/3 cm

Gewicht

868 g

Sprache

Englisch

ISBN

978-0-596-80235-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Data Analysis with Open Source Tools

  • ; 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;