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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
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| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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-9910811945103321 |
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Walkowiak Simon
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| Birmingham : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data-driven decisions : a practical toolkit for librarians and information professionals / / Amy Stubbing [[electronic resource]]
| Data-driven decisions : a practical toolkit for librarians and information professionals / / Amy Stubbing [[electronic resource]] |
| Autore | Stubbing Amy |
| Pubbl/distr/stampa | London : , : Facet, , 2022 |
| Descrizione fisica | 1 online resource (xvi, 180 pages) : digital, PDF file(s) |
| Disciplina | 025.1 |
| Soggetto topico |
Library administration - Decision making
Information resources management - Decision making Quantitative research Data mining library text and data mining decision-making data processing digital technology |
| ISBN |
1-78330-522-3
1-78330-480-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Praise for Data-Driven Decisions -- Title Page -- Copyright -- Dedication -- Contents -- Figures and Tables -- Contributors -- Acknowledgements -- Part 1: Background -- 1 Introduction -- About the author -- The wider context -- The what, the why and the where of the data-driven decision process -- The toolkit? -- Going further -- What will you get from this book? -- 2 Using the Toolkit -- Getting started -- Book layout -- The model -- A circular approach -- Part 2: The Toolkit -- 3 Step 1: Identify -- Introduction -- Data needs -- Data queries -- Data sources -- Time to practise -- Summary -- 4 Step 2: Collect -- Introduction -- Choosing your data -- Data collection methods -- Summary -- 5 Step 3: Map -- Introduction -- What is mapping? -- Making data comparable (normalising) -- Visualisation -- Creating a map of your data -- Summary -- 6 Step 4: Analyse -- Introduction -- What is analysis? -- Understand context -- Conclusions -- Summary -- 7 Step 5: Act -- Introduction -- What is the action step? -- Sharing data -- Planning actions -- Summary -- 8 Step 6: Review -- Introduction -- Why do we review? -- What do we review? -- How to review and questions to explore -- Make the changes -- What next? -- Part 3: Going Further -- 9 Moving from a Transactional to a Transformational Service Using Data -- Introduction -- Why lead with data? -- Transactional vs transformational work -- Data-led culture -- Data with compassion -- Case study -- 10 Collection Mapping for Collection Management -- Introduction -- Understanding your collection as a concept -- Collection mapping -- Conclusion -- 11 User Experience and Qualitative Data -- Introduction -- What is UX? -- Undertaking UX research in a library -- The UX techniques -- Ethics of research -- Recruiting participants -- Analysis -- Now write it up! -- What next? -- Words of caution.
This is the beginning -- 12 Alternative Data Sources: Using Digital and Social Media to Inform Management Decisions in Your Library -- Introduction -- Libraries and social media -- Social media terminology and background -- What sort of data are we talking about? -- Data from social media marketing activity -- User engagement data and dialogue (outcomes measurement and evaluation) -- Service improvements and customer services -- Altmetrics -- Web-based analytics -- Summary -- 13 Starting from Scratch: Building a Data Culture at the University of Westminster -- Background -- Overnight opening case study -- Lessons learned and reflection -- 14 Back to the Drawing Board: How Data Visualisation Techniques Informed Service Delivery during the COVID-19 Pandemic -- Setting the scene -- Background -- Piktochart -- Power BI -- Case study: the pandemic -- Final thoughts -- Appendix -- Bibliography -- Index. |
| Record Nr. | UNINA-9910795883603321 |
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Stubbing Amy
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| London : , : Facet, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Navigating copyright for libraries : purpose and scope / / edited by Jessica Coates, Victoria Owen and Susan Reilly
| Navigating copyright for libraries : purpose and scope / / edited by Jessica Coates, Victoria Owen and Susan Reilly |
| Autore | Coates Jessica |
| Pubbl/distr/stampa | Berlin/Boston, : De Gruyter, 2022 |
| Descrizione fisica | 1 online resource (vii, 547 pages) |
| Altri autori (Persone) |
OwenVictoria (Copyright and information policy expert)
ReillySusan |
| Collana | IFLA Publications |
| Soggetto topico |
library
copyright digital archiving information user information science text and data mining |
| Soggetto non controllato |
Library law
copyright law |
| ISBN | 3-11-073200-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910588781103321 |
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Coates Jessica
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| Berlin/Boston, : De Gruyter, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Navigating copyright for libraries : purpose and scope / / edited by Jessica Coates, Victoria Owen and Susan Reilly
| Navigating copyright for libraries : purpose and scope / / edited by Jessica Coates, Victoria Owen and Susan Reilly |
| Autore | Coates Jessica |
| Pubbl/distr/stampa | Berlin/Boston, : De Gruyter, 2022 |
| Descrizione fisica | 1 online resource (vii, 547 pages) |
| Altri autori (Persone) |
OwenVictoria (Copyright and information policy expert)
ReillySusan |
| Collana | IFLA Publications |
| Soggetto topico |
library
copyright digital archiving information user information science text and data mining |
| Soggetto non controllato |
Library law
copyright law |
| ISBN | 3-11-073200-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996487162903316 |
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Coates Jessica
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| Berlin/Boston, : De Gruyter, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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 |
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van der Aalst Wil M. P
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Teate Renee M.
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||
| 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.
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||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Soggetto topico
-
text and data mining
(8)
- programming language (4)
- software (4)
- Data mining (3)
- data processing (3)