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Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others]
| Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2021 |
| Descrizione fisica | xxxvi, 405 pages |
| Soggetto topico |
earth sciences
climatology data science remote sensing machine learning Algorithms - Study and teaching |
| ISBN |
1-119-64616-2
1-119-64618-9 1-119-64615-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555005503321 |
| Hoboken, New Jersey : , : Wiley, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others]
| Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences / / edited by Gustau Camps-Valls [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2021 |
| Descrizione fisica | xxxvi, 405 pages |
| Disciplina | 550.71 |
| Soggetto topico |
earth sciences
climatology data science remote sensing machine learning Algorithms - Study and teaching |
| ISBN |
1-119-64616-2
1-119-64618-9 1-119-64615-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910677724503321 |
| Hoboken, New Jersey : , : Wiley, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Process mining : data science in action / / by Wil M. P. van der Aalst
| Process mining : data science in action / / by Wil M. P. van der Aalst |
| Autore | van der Aalst Wil M. P |
| Edizione | [2nd edition.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 477 pages |
| Disciplina | 004 |
| Soggetto topico |
Application software
Information storage and retrieval Information technology Business—Data processing Software engineering Computer logic Information Systems Applications (incl. Internet) Information Storage and Retrieval IT in Business Software Engineering Logics and Meanings of Programs Computer Appl. in Administrative Data Processing Fouille de données Mémorisation des données Analyse des données Traitement des données open data data science text and data mining |
| ISBN |
9783662498507
3-662-49851-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Preliminaries -- From Event Logs to Process Models -- Beyond Process Discovery -- Putting Process Mining to Work -- Reflection -- Epilogue. |
| Record Nr. | UNINA-9910254981903321 |
van der Aalst Wil M. P
|
||
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
SQL for data scientists : a beginner's guide for building datasets for analysis / / Renee M. Teate
| SQL for data scientists : a beginner's guide for building datasets for analysis / / Renee M. Teate |
| Autore | Teate Renee M. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
| Descrizione fisica | 1 online resource (291 pages) |
| Disciplina | 005.756 |
| Soggetto topico |
SQL (Computer program language)
software data science information analysis text and data mining programming language |
| ISBN |
1-119-66939-1
1-119-66938-3 1-119-66937-5 9781119669364 9781119669371 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Title Page -- Copyright Page -- About the Author -- About the Technical Editor -- Acknowledgments -- Contents at a Glance -- Contents -- Introduction -- Who I Am and Why I'm Writing About This Topic -- Who This Book Is For -- Why You Should Learn SQL if You Want to Be a Data Scientist -- What I Hope You Gain from This Book -- Conventions -- Reader Support for This Book -- Companion Download Files -- How to Contact the Publisher -- How to Contact the Author -- Chapter 1 Data Sources -- Data Sources -- Tools for Connecting to Data Sources and Editing SQL -- Relational Databases -- Dimensional Data Warehouses -- Asking Questions About the Data Source -- Introduction to the Farmer's Market Database -- A Note on Machine Learning Dataset Terminology -- Exercises -- Chapter 2 The SELECT Statement -- The SELECT Statement -- The Fundamental Syntax Structure of a SELECT Query -- Selecting Columns and Limiting the Number of Rows Returned -- The ORDER BY Clause: Sorting Results -- Introduction to Simple Inline Calculations -- More Inline Calculation Examples: Rounding -- More Inline Calculation Examples: Concatenating Strings -- Evaluating Query Output -- SELECT Statement Summary -- Exercises Using the Included Database -- Chapter 3 The WHERE Clause -- The WHERE Clause -- Filtering SELECT Statement Results -- Filtering on Multiple Conditions -- Multi-Column Conditional Filtering -- More Ways to Filter -- BETWEEN -- IN -- LIKE -- IS NULL -- A Warning About Null Comparisons -- Filtering Using Subqueries -- Exercises Using the Included Database -- Chapter 4 CASE Statements -- CASE Statement Syntax -- Creating Binary Flags Using CASE -- Grouping or Binning Continuous Values Using CASE -- Categorical Encoding Using CASE -- CASE Statement Summary -- Exercises Using the Included Database -- Chapter 5 SQL JOINs -- Database Relationships and SQL JOINs -- A Common Pitfall when Filtering Joined Data -- JOINs with More than Two Tables -- Exercises Using the Included Database -- Chapter 6 Aggregating Results for Analysis -- GROUP BY Syntax -- Displaying Group Summaries -- Performing Calculations Inside Aggregate Functions -- MIN and MAX -- COUNT and COUNT DISTINCT -- Average -- Filtering with HAVING -- CASE Statements Inside Aggregate Functions -- Exercises Using the Included Database -- Chapter 7 Window Functions and Subqueries -- ROW NUMBER -- RANK and DENSE RANK -- NTILE -- Aggregate Window Functions -- LAG and LEAD -- Exercises Using the Included Database -- Chapter 8 Date and Time Functions -- Setting datetime Field Values -- EXTRACT and DATE_PART -- DATE_ADD and DATE_SUB -- DATEDIFF -- TIMESTAMPDIFF -- Date Functions in Aggregate Summaries and Window Functions -- Exercises -- Chapter 9 Exploratory Data Analysis with SQL -- Demonstrating Exploratory Data Analysis with SQL -- Exploring the Products Table -- Exploring Possible Column Values -- Exploring Changes Over Time -- Exploring Multiple Tables Simultaneously -- Exploring Inventory vs. Sales -- Exercises -- Chapter 10 Building SQL Datasets for Analytical Reporting -- Thinking Through Analytical Dataset Requirements -- Using Custom Analytical Datasets in SQL: CTEs and Views -- Taking SQL Reporting Further -- Exercises -- Chapter 11 More Advanced Query Structures -- UNIONs -- Self-Join to Determine To-Date Maximum -- Counting New vs. Returning Customers by Week -- Summary -- Exercises -- Chapter 12 Creating Machine Learning Datasets Using SQL -- Datasets for Time Series Models -- Datasets for Binary Classification -- Creating the Dataset -- Expanding the Feature Set -- Feature Engineering -- Taking Things to the Next Level -- Exercises -- Chapter 13 Analytical Dataset Development Examples -- What Factors Correlate with Fresh Produce Sales? -- How Do Sales Vary by Customer Zip Code, Market Distance, and Demographic Data? -- How Does Product Price Distribution Affect Market Sales? -- Chapter 14 Storing and Modifying Data -- Storing SQL Datasets as Tables and Views -- Adding a Timestamp Column -- Inserting Rows and Updating Values in Database Tables -- Using SQL Inside Scripts -- In Closing -- Exercises -- Appendix Answers to Exercises -- Chapter 1: Data Sources -- Answers -- Chapter 2: The SELECT Statement -- Answers -- Chapter 3: The WHERE Clause -- Answers -- Chapter 4: CASE Statements -- Answers -- Chapter 5: SQL JOINs -- Answers -- Chapter 6: Aggregating Results for Analysis -- Answers -- Chapter 7: Window Functions and Subqueries -- Answers -- Chapter 8: Date and Time Functions -- Answers -- Chapter 9: Exploratory Data Analysis with SQL -- Answers -- Chapter 10: Building SQL Datasets for Analytical Reporting -- Answers -- Chapter 11: More Advanced Query Structures -- Answers -- Chapter 12: Creating Machine Learning Datasets Using SQL -- Answers -- Chapter 14: Storing and Modifying Data |
| Record Nr. | UNINA-9910555033103321 |
|
Teate Renee M.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
SQL for data scientists : a beginner's guide for building datasets for analysis / / Renee M. Teate
| SQL for data scientists : a beginner's guide for building datasets for analysis / / Renee M. Teate |
| Autore | Teate Renee M. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
| Descrizione fisica | 1 online resource (291 pages) |
| Disciplina | 005.756 |
| Soggetto topico |
SQL (Computer program language)
software data science information analysis text and data mining programming language |
| ISBN |
1-119-66939-1
1-119-66938-3 1-119-66937-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Title Page -- Copyright Page -- About the Author -- About the Technical Editor -- Acknowledgments -- Contents at a Glance -- Contents -- Introduction -- Who I Am and Why I'm Writing About This Topic -- Who This Book Is For -- Why You Should Learn SQL if You Want to Be a Data Scientist -- What I Hope You Gain from This Book -- Conventions -- Reader Support for This Book -- Companion Download Files -- How to Contact the Publisher -- How to Contact the Author -- Chapter 1 Data Sources -- Data Sources -- Tools for Connecting to Data Sources and Editing SQL -- Relational Databases -- Dimensional Data Warehouses -- Asking Questions About the Data Source -- Introduction to the Farmer's Market Database -- A Note on Machine Learning Dataset Terminology -- Exercises -- Chapter 2 The SELECT Statement -- The SELECT Statement -- The Fundamental Syntax Structure of a SELECT Query -- Selecting Columns and Limiting the Number of Rows Returned -- The ORDER BY Clause: Sorting Results -- Introduction to Simple Inline Calculations -- More Inline Calculation Examples: Rounding -- More Inline Calculation Examples: Concatenating Strings -- Evaluating Query Output -- SELECT Statement Summary -- Exercises Using the Included Database -- Chapter 3 The WHERE Clause -- The WHERE Clause -- Filtering SELECT Statement Results -- Filtering on Multiple Conditions -- Multi-Column Conditional Filtering -- More Ways to Filter -- BETWEEN -- IN -- LIKE -- IS NULL -- A Warning About Null Comparisons -- Filtering Using Subqueries -- Exercises Using the Included Database -- Chapter 4 CASE Statements -- CASE Statement Syntax -- Creating Binary Flags Using CASE -- Grouping or Binning Continuous Values Using CASE -- Categorical Encoding Using CASE -- CASE Statement Summary -- Exercises Using the Included Database -- Chapter 5 SQL JOINs -- Database Relationships and SQL JOINs -- A Common Pitfall when Filtering Joined Data -- JOINs with More than Two Tables -- Exercises Using the Included Database -- Chapter 6 Aggregating Results for Analysis -- GROUP BY Syntax -- Displaying Group Summaries -- Performing Calculations Inside Aggregate Functions -- MIN and MAX -- COUNT and COUNT DISTINCT -- Average -- Filtering with HAVING -- CASE Statements Inside Aggregate Functions -- Exercises Using the Included Database -- Chapter 7 Window Functions and Subqueries -- ROW NUMBER -- RANK and DENSE RANK -- NTILE -- Aggregate Window Functions -- LAG and LEAD -- Exercises Using the Included Database -- Chapter 8 Date and Time Functions -- Setting datetime Field Values -- EXTRACT and DATE_PART -- DATE_ADD and DATE_SUB -- DATEDIFF -- TIMESTAMPDIFF -- Date Functions in Aggregate Summaries and Window Functions -- Exercises -- Chapter 9 Exploratory Data Analysis with SQL -- Demonstrating Exploratory Data Analysis with SQL -- Exploring the Products Table -- Exploring Possible Column Values -- Exploring Changes Over Time -- Exploring Multiple Tables Simultaneously -- Exploring Inventory vs. Sales -- Exercises -- Chapter 10 Building SQL Datasets for Analytical Reporting -- Thinking Through Analytical Dataset Requirements -- Using Custom Analytical Datasets in SQL: CTEs and Views -- Taking SQL Reporting Further -- Exercises -- Chapter 11 More Advanced Query Structures -- UNIONs -- Self-Join to Determine To-Date Maximum -- Counting New vs. Returning Customers by Week -- Summary -- Exercises -- Chapter 12 Creating Machine Learning Datasets Using SQL -- Datasets for Time Series Models -- Datasets for Binary Classification -- Creating the Dataset -- Expanding the Feature Set -- Feature Engineering -- Taking Things to the Next Level -- Exercises -- Chapter 13 Analytical Dataset Development Examples -- What Factors Correlate with Fresh Produce Sales? -- How Do Sales Vary by Customer Zip Code, Market Distance, and Demographic Data? -- How Does Product Price Distribution Affect Market Sales? -- Chapter 14 Storing and Modifying Data -- Storing SQL Datasets as Tables and Views -- Adding a Timestamp Column -- Inserting Rows and Updating Values in Database Tables -- Using SQL Inside Scripts -- In Closing -- Exercises -- Appendix Answers to Exercises -- Chapter 1: Data Sources -- Answers -- Chapter 2: The SELECT Statement -- Answers -- Chapter 3: The WHERE Clause -- Answers -- Chapter 4: CASE Statements -- Answers -- Chapter 5: SQL JOINs -- Answers -- Chapter 6: Aggregating Results for Analysis -- Answers -- Chapter 7: Window Functions and Subqueries -- Answers -- Chapter 8: Date and Time Functions -- Answers -- Chapter 9: Exploratory Data Analysis with SQL -- Answers -- Chapter 10: Building SQL Datasets for Analytical Reporting -- Answers -- Chapter 11: More Advanced Query Structures -- Answers -- Chapter 12: Creating Machine Learning Datasets Using SQL -- Answers -- Chapter 14: Storing and Modifying Data |
| Record Nr. | UNINA-9910676542403321 |
|
Teate Renee M.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
