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{ "item_title" : "Introduction to Python in Earth Science Data Analysis", "item_author" : [" Maurizio Petrelli "], "item_description" : "Part I Python for Geologists, a kick-off; 1. Setting Up Your Python Environment, Easily; 2. Python Essentials for a Geologist; 3. Start Solving Geological Problems Using Python; Part II Describing Geological Data; 4. Graphical Visualization of a Geological Dataset; 5. Descriptive Statistics; Part III Integrals and Differential Equations in Geology; 6. Numerical Integration; 7. Ordinary Differential Equations (ODE); 8. Partial Differential Equations (PDE); Part IV Probability Density Functions and Error Analysis; 9. Probability Density Functions and their Use in Geology; 10. Error Analysis; Part V Robust Statistics and Machine Learning; 11. Introduction to Robust Statistics; 12. Machine Learning;", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/03/078/057/3030780570_b.jpg", "price_data" : { "retail_price" : "64.99", "online_price" : "64.99", "our_price" : "64.99", "club_price" : "64.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Introduction to Python in Earth Science Data Analysis|Maurizio Petrelli

Introduction to Python in Earth Science Data Analysis : From Descriptive Statistics to Machine Learning

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Overview

Part I Python for Geologists, a kick-off; 1. Setting Up Your Python Environment, Easily; 2. Python Essentials for a Geologist; 3. Start Solving Geological Problems Using Python; Part II Describing Geological Data; 4. Graphical Visualization of a Geological Dataset; 5. Descriptive Statistics; Part III Integrals and Differential Equations in Geology; 6. Numerical Integration; 7. Ordinary Differential Equations (ODE); 8. Partial Differential Equations (PDE); Part IV Probability Density Functions and Error Analysis; 9. Probability Density Functions and their Use in Geology; 10. Error Analysis; Part V Robust Statistics and Machine Learning; 11. Introduction to Robust Statistics; 12. Machine Learning;

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Details

  • ISBN-13: 9783030780579
  • ISBN-10: 3030780570
  • Publisher: Springer
  • Publish Date: September 2022
  • Dimensions: 9.21 x 6.14 x 0.52 inches
  • Shipping Weight: 0.78 pounds
  • Page Count: 229

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