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Big data analytics with R : utilize R to uncover hidden patterns in your big data / / Simon Walkowiak
| Big data analytics with R : utilize R to uncover hidden patterns in your big data / / Simon Walkowiak |
| Autore | Walkowiak Simon |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (498 pages) : illustrations |
| Collana | Community experience distilled |
| Soggetto topico |
R (Computer program language)
Data mining Information visualization text and data mining programming language big data cloud computing data processing software |
| ISBN | 1-78646-372-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910798524603321 |
Walkowiak Simon
|
||
| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data analytics with R : utilize R to uncover hidden patterns in your big data / / Simon Walkowiak
| Big data analytics with R : utilize R to uncover hidden patterns in your big data / / Simon Walkowiak |
| Autore | Walkowiak Simon |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (498 pages) : illustrations |
| Collana | Community experience distilled |
| Soggetto topico |
R (Computer program language)
Data mining Information visualization text and data mining programming language big data cloud computing data processing software |
| ISBN | 1-78646-372-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910811945103321 |
|
Walkowiak Simon
|
||
| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The Informatics of Time and Events : Inaugural lecture delivered on Thursday 28 March 2013 / / Gérard Berry
| The Informatics of Time and Events : Inaugural lecture delivered on Thursday 28 March 2013 / / Gérard Berry |
| Autore | Berry Gérard |
| Pubbl/distr/stampa | Paris, : Collège de France, 2016 |
| Altri autori (Persone) |
BerryGérard
HarocheSerge |
| Soggetto topico |
Multidisciplinary
informatique programmation embedded systems computer science programming language computer programming synchronization time |
| Soggetto non controllato |
computer science
computer programming embedded systems synchronization programming language time |
| ISBN | 2-7226-0429-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910149220103321 |
|
Berry Gérard
|
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| Paris, : Collège de France, 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 | ||
| ||
