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Becoming a data head : how to think, speak, and understand data science, statistics, and machine learning / / Alex J. Gutman, Jordan Goldmeier
| Becoming a data head : how to think, speak, and understand data science, statistics, and machine learning / / Alex J. Gutman, Jordan Goldmeier |
| Autore | Gutman Alex J |
| Edizione | [1] |
| Pubbl/distr/stampa | Wiley, 2021 |
| Descrizione fisica | 1 online resource |
| Disciplina | 006.312 |
| Soggetto topico |
Computer science
Statistics -- Data processing Machine learning |
| ISBN |
1-5231-4321-5
1-119-74176-9 1-119-74171-8 9781119741763 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | About the Technical Editors Introduction Part I Thinking Like a Data Head Part II Speaking Like a Data Head Part III Understanding the Data Scientist's Toolbox Part IV Ensuring Success EULA |
| Record Nr. | UNINA-9911007043203321 |
| Gutman Alex J | ||
| Wiley, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis ... [et al.]
| Big data analytics for large-scale multimedia search / / edited by Stefanos Vrochidis ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2019 |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 005.7 |
| Collana | THEi Wiley ebooks. |
| Soggetto topico |
Multimedia data mining
Big data |
| ISBN |
1-119-37699-8
1-119-37698-X 9781119376989 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Introduction -- List of Contributors -- About the Companion Website -- Part I Feature Extraction from Big Multimedia Data -- Chapter 1 Representation Learning on Large and Small Data -- 1.1 Introduction -- 1.2 Representative Deep CNNs -- 1.2.1 AlexNet -- 1.2.1.1 ReLU Nonlinearity -- 1.2.1.2 Data Augmentation -- 1.2.1.3 Dropout -- 1.2.2 Network in Network -- 1.2.2.1 MLP Convolutional Layer -- 1.2.2.2 Global Average Pooling -- 1.2.3 VGG -- 1.2.3.1 Very Small Convolutional Filters -- 1.2.3.2 Multi‐scale Training -- 1.2.4 GoogLeNet -- 1.2.4.1 Inception Modules -- 1.2.4.2 Dimension Reduction -- 1.2.5 ResNet -- 1.2.5.1 Residual Learning -- 1.2.5.2 Identity Mapping by Shortcuts -- 1.2.6 Observations and Remarks -- 1.3 Transfer Representation Learning -- 1.3.1 Method Specifications -- 1.3.2 Experimental Results and Discussion -- 1.3.2.1 Results of Transfer Representation Learning for OM -- 1.3.2.2 Results of Transfer Representation Learning for Melanoma -- 1.3.2.3 Qualitative Evaluation: Visualization -- 1.3.3 Observations and Remarks -- 1.4 Conclusions -- References -- Chapter 2 Concept‐Based and Event‐Based Video Search in Large Video Collections -- 2.1 Introduction -- 2.2 Video preprocessing and Machine Learning Essentials -- 2.2.1 Video Representation -- 2.2.2 Dimensionality Reduction -- 2.3 Methodology for Concept Detection and Concept‐Based Video Search -- 2.3.1 Related Work -- 2.3.2 Cascades for Combining Different Video Representations -- 2.3.2.1 Problem Definition and Search Space -- 2.3.2.2 Problem Solution -- 2.3.3 Multi‐Task Learning for Concept Detection and Concept‐Based Video Search -- 2.3.4 Exploiting Label Relations -- 2.3.5 Experimental Study -- 2.3.5.1 Dataset and Experimental Setup -- 2.3.5.2 Experimental Results -- 2.3.5.3 Computational Complexity.
2.4 Methods for Event Detection and Event‐Based Video Search -- 2.4.1 Related Work -- 2.4.2 Learning from Positive Examples -- 2.4.3 Learning Solely from Textual Descriptors: Zero‐Example Learning -- 2.4.4 Experimental Study -- 2.4.4.1 Dataset and Experimental Setup -- 2.4.4.2 Experimental Results: Learning from Positive Examples -- 2.4.4.3 Experimental Results: Zero‐Example Learning -- 2.5 Conclusions -- 2.6 Acknowledgments -- References -- Chapter 3 Big Data Multimedia Mining: Feature Extraction Facing Volume, Velocity, and Variety -- 3.1 Introduction -- 3.2 Scalability through Parallelization -- 3.2.1 Process Parallelization -- 3.2.2 Data Parallelization -- 3.3 Scalability through Feature Engineering -- 3.3.1 Feature Reduction through Spatial Transformations -- 3.3.2 Laplacian Matrix Representation -- 3.3.3 Parallel latent Dirichlet allocation and bag of words -- 3.4 Deep Learning‐Based Feature Learning -- 3.4.1 Adaptability that Conquers both Volume and Velocity -- 3.4.2 Convolutional Neural Networks -- 3.4.3 Recurrent Neural Networks -- 3.4.4 Modular Approach to Scalability -- 3.5 Benchmark Studies -- 3.5.1 Dataset -- 3.5.2 Spectrogram Creation -- 3.5.3 CNN‐Based Feature Extraction -- 3.5.4 Structure of the CNNs -- 3.5.5 Process Parallelization -- 3.5.6 Results -- 3.6 Closing Remarks -- 3.7 Acknowledgements -- References -- Part II Learning Algorithms for Large-Scale Multimedia -- Chapter 4 Large‐Scale Video Understanding with Limited Training Labels -- 4.1 Introduction -- 4.2 Video Retrieval with Hashing -- 4.2.1 Overview -- 4.2.2 Unsupervised Multiple Feature Hashing -- 4.2.2.1 Framework -- 4.2.2.2 The Objective Function of MFH -- 4.2.2.3 Solution of MFH -- 4.2.2.3.1 Complexity Analysis -- 4.2.3 Submodular Video Hashing -- 4.2.3.1 Framework -- 4.2.3.2 Video Pooling -- 4.2.3.3 Submodular Video Hashing -- 4.2.4 Experiments. 4.2.4.1 Experiment Settings -- 4.2.4.1.1 Video Datasets -- 4.2.4.1.2 Visual Features -- 4.2.4.1.3 Algorithms for Comparison -- 4.2.4.2 Results -- 4.2.4.2.1 CC_WEB_VIDEO -- 4.2.4.2.2 Combined Dataset -- 4.2.4.3 Evaluation of SVH -- 4.2.4.3.1 Results -- 4.3 Graph‐Based Model for Video Understanding -- 4.3.1 Overview -- 4.3.2 Optimized Graph Learning for Video Annotation -- 4.3.2.1 Framework -- 4.3.2.2 OGL -- 4.3.2.2.1 Terms and Notations -- 4.3.2.2.2 Optimal Graph-Based SSL -- 4.3.2.2.3 Iterative Optimization -- 4.3.3 Context Association Model for Action Recognition -- 4.3.3.1 Context Memory -- 4.3.4 Graph‐based Event Video Summarization -- 4.3.4.1 Framework -- 4.3.4.2 Temporal Alignment -- 4.3.5 TGIF: A New Dataset and Benchmark on Animated GIF Description -- 4.3.5.1 Data Collection -- 4.3.5.2 Data Annotation -- 4.3.6 Experiments -- 4.3.6.1 Experimental Settings -- 4.3.6.2 Results -- 4.4 Conclusions and Future Work -- References -- Chapter 5 Multimodal Fusion of Big Multimedia Data -- 5.1 Multimodal Fusion in Multimedia Retrieval -- 5.1.1 Unsupervised Fusion in Multimedia Retrieval -- 5.1.1.1 Linear and Non‐linear Similarity Fusion -- 5.1.1.2 Cross‐modal Fusion of Similarities -- 5.1.1.3 Random Walks and Graph‐based Fusion -- 5.1.1.4 A Unifying Graph‐based Model -- 5.1.2 Partial Least Squares Regression -- 5.1.3 Experimental Comparison -- 5.1.3.1 Dataset Description -- 5.1.3.2 Settings -- 5.1.3.3 Results -- 5.1.4 Late Fusion of Multiple Multimedia Rankings -- 5.1.4.1 Score Fusion -- 5.1.4.2 Rank Fusion -- 5.1.4.2.1 Borda Count Fusion -- 5.1.4.2.2 Reciprocal Rank Fusion -- 5.1.4.2.3 Condorcet Fusion -- 5.2 Multimodal Fusion in Multimedia Classification -- 5.2.1 Related Literature -- 5.2.2 Problem Formulation -- 5.2.3 Probabilistic Fusion in Active Learning -- 5.2.3.1 If P(S& -- equals -- 0|V,T)≠0: -- 5.2.3.2 If P(S& -- equals -- 0|V,T)&. equals -- 0: -- 5.2.3.3 Incorporating Informativeness in the Selection (P(S|V)) -- 5.2.3.4 Measuring Oracle's Confidence (P(S|T)) -- 5.2.3.5 Re‐training -- 5.2.4 Experimental Comparison -- 5.2.4.1 Datasets -- 5.2.4.2 Settings -- 5.2.4.3 Results -- 5.2.4.3.1 Expanding with Positive, Negative or Both -- 5.2.4.3.2 Comparing with Sample Selection Approaches -- 5.2.4.3.3 Comparing with Fusion Approaches -- 5.2.4.3.4 Parameter Sensitivity Investigation -- 5.2.4.3.5 Comparing with Existing Methods -- 5.3 Conclusions -- References -- Chapter 6 Large‐Scale Social Multimedia Analysis -- 6.1 Social Multimedia in Social Media Streams -- 6.1.1 Social Multimedia -- 6.1.2 Social Multimedia Streams -- 6.1.3 Analysis of the Twitter Firehose -- 6.1.3.1 Dataset: Overview -- 6.1.3.2 Linked Resource Analysis -- 6.1.3.3 Image Content Analysis -- 6.1.3.4 Geographic Analysis -- 6.1.3.5 Textual Analysis -- 6.2 Large‐Scale Analysis of Social Multimedia -- 6.2.1 Large‐Scale Processing of Social Multimedia Analysis -- 6.2.1.1 Batch‐Processing Frameworks -- 6.2.1.2 Stream‐Processing Frameworks -- 6.2.1.3 Distributed Processing Frameworks -- 6.2.2 Analysis of Social Multimedia -- 6.2.2.1 Analysis of Visual Content -- 6.2.2.2 Analysis of Textual Content -- 6.2.2.3 Analysis of Geographical Content -- 6.2.2.4 Analysis of User Content -- 6.3 Large‐Scale Multimedia Opinion Mining System -- 6.3.1 System Overview -- 6.3.2 Implementation Details -- 6.3.2.1 Social Media Data Crawler -- 6.3.2.2 Social Multimedia Analysis -- 6.3.2.3 Analysis of Visual Content -- 6.3.3 Evaluations: Analysis of Visual Content -- 6.3.3.1 Filtering of Synthetic Images -- 6.3.3.2 Near‐Duplicate Detection -- 6.4 Conclusion -- References -- Chapter 7 Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger -- 7.1 Introduction -- 7.1.1 The Dark Side of Big Multimedia Data. 7.1.2 Defining Multimedia Privacy -- 7.2 Protecting User Privacy -- 7.2.1 What to Protect -- 7.2.2 How to Protect -- 7.2.3 Threat Models -- 7.3 Multimedia Privacy -- 7.3.1 Privacy and Multimedia Big Data -- 7.3.2 Privacy Threats of Multimedia Data -- 7.3.2.1 Audio Data -- 7.3.2.2 Visual Data -- 7.3.2.3 Multimodal Threats -- 7.4 Privacy‐Related Multimedia Analysis Research -- 7.4.1 Multimedia Analysis Filters -- 7.4.2 Multimedia Content Masking -- 7.5 The Larger Research Picture -- 7.5.1 Multimedia Security and Trust -- 7.5.2 Data Privacy -- 7.6 Outlook on Multimedia Privacy Challenges -- 7.6.1 Research Challenges -- 7.6.1.1 Multimedia Analysis -- 7.6.1.2 Data -- 7.6.1.3 Users -- 7.6.2 Research Reorientation -- 7.6.2.1 Professional Paranoia -- 7.6.2.2 Privacy as a Priority -- 7.6.2.3 Privacy in Parallel -- References -- Part III Scalability in Multimedia Access -- Chapter 8 Data Storage and Management for Big Multimedia -- 8.1 Introduction -- 8.1.1 Multimedia Applications and Scale -- 8.1.2 Big Data Management -- 8.1.3 System Architecture Outline -- 8.1.4 Metadata Storage Architecture -- 8.1.4.1 Lambda Architecture -- 8.1.4.2 Storage Layer -- 8.1.4.3 Processing Layer -- 8.1.4.4 Serving Layer -- 8.1.4.5 Dynamic Data -- 8.1.5 Summary and Chapter Outline -- 8.2 Media Storage -- 8.2.1 Storage Hierarchy -- 8.2.1.1 Secondary Storage -- 8.2.1.2 The Five‐Minute Rule -- 8.2.1.3 Emerging Trends for Local Storage -- 8.2.2 Distributed Storage -- 8.2.2.1 Distributed Hash Tables -- 8.2.2.2 The CAP Theorem and the PACELC Formulation -- 8.2.2.3 The Hadoop Distributed File System -- 8.2.2.4 Ceph -- 8.2.3 Discussion -- 8.3 Processing Media -- 8.3.1 Metadata Extraction -- 8.3.2 Batch Processing -- 8.3.2.1 Map‐Reduce and Hadoop -- 8.3.2.2 Spark -- 8.3.2.3 Comparison -- 8.3.3 Stream Processing -- 8.4 Multimedia Delivery -- 8.4.1 Distributed In‐Memory Buffering. 8.4.1.1 Memcached and Redis. |
| Altri titoli varianti | Big data analytics for large scale multimedia search |
| Record Nr. | UNINA-9910824409003321 |
| Hoboken, N.J., : Wiley, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The big R-book : from data science to learning machines and big data / / Philippe J.S. De Brouwer
| The big R-book : from data science to learning machines and big data / / Philippe J.S. De Brouwer |
| Autore | De Brouwer Philippe J. S |
| Edizione | [1] |
| Pubbl/distr/stampa | Wiley, 2020 |
| Descrizione fisica | 1 online resource (928 pages) |
| Disciplina | 519.502855133 |
| Soggetto topico |
R (Computer program language)
Big data Machine learning |
| ISBN |
1-119-63277-3
1-119-63276-5 1-119-63275-7 9781119632764 |
| Classificazione |
007.6
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | About the Companion Site PART I: Introduction PART II: Starting with R and Elements of Statistics PART III: Data Import PART IV: Data Wrangling PART V: Modelling PART VI: Introduction to Companies PART VII: Reporting PART VIII: Bigger and Faster R PART IX: Appendices Nomenclature End User License Agreement |
| Altri titoli varianti | The big R book : from data science to learning machines and big data |
| Record Nr. | UNINA-9910877041703321 |
De Brouwer Philippe J. S
|
||
| Wiley, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data science in theory and practice : techniques for big data analytics and complex data sets / / Maria C. Mariani, Osei Kofi Tweneboah, Maria Pia Beccar-Varela
| Data science in theory and practice : techniques for big data analytics and complex data sets / / Maria C. Mariani, Osei Kofi Tweneboah, Maria Pia Beccar-Varela |
| Autore | Mariani Maria Cristina |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Wiley, 2021 |
| Descrizione fisica | 1 online resource (403 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Data mining |
| ISBN |
1-119-67473-5
1-119-67475-1 1-119-67470-0 9781119674689 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 Background of Data Science Chapter 2 Matrix Algebra and Random Vectors Chapter 3 Multivariate Analysis Chapter 4 Time Series Forecasting Chapter 5 Introduction to R Chapter 6 Introduction to Python Chapter 7 Algorithms Chapter 8 Data Preprocessing and Data Validations Chapter 9 Data Visualizations Chapter 10 Binomial and Trinomial Trees Chapter 11 Principal Component Analysis Chapter 12 Discriminant and Cluster Analysis Chapter 13 Multidimensional Scaling Chapter 14 Classification and Tree‐Based Methods Chapter 15 Association Rules Chapter 16 Support Vector Machines Chapter 17 Neural Networks Chapter 18 Fourier Analysis Chapter 19 Wavelets Analysis Chapter 20 Stochastic Analysis Chapter 21 Fractal Analysis - Lévy, Hurst, DFA, DEA Chapter 22 Stochastic Differential Equations Chapter 23 Ethics: With Great Power Comes Great Responsibility EULA |
| Record Nr. | UNINA-9911019497703321 |
|
Mariani Maria Cristina
|
||
| Wiley, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Decision intelligence for dummies / / by Pam Baker
| Decision intelligence for dummies / / by Pam Baker |
| Autore | Baker Pam |
| Edizione | [1] |
| Pubbl/distr/stampa | Wiley, 2021 |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 006.3 |
| Collana | --For dummies |
| Soggetto topico |
Big data
Decision making Artificial intelligence Management information systems |
| ISBN |
1-119-82486-9
1-119-82485-0 9781119824855 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Conventions Used in This Book -- Foolish Assumptions -- What You Don't Have to Read -- How This Book Is Organized -- Part 1: Getting Started with Decision Intelligence -- Part 2: Reaching the Best Possible Decision -- Part 3: Establishing Reality Checks -- Part 4: Proposing a New Directive -- Part 5: The Part of Tens -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Decision Intelligence -- Chapter 1 Short Takes on Decision Intelligence -- The Tale of Two Decision Trails -- Pointing out the way -- Making a decision -- Deputizing AI as Your Faithful Sidekick -- Seeing How Decision Intelligence Looks on Paper -- Tracking the Inverted V -- Estimating How Much Decision Intelligence Will Cost You -- Chapter 2 Mining Data versus Minding the Answer -- Knowledge Is Power - Data Is Just Information -- Experiencing the epiphany -- Embracing the new, not-so-new idea -- Avoiding thought boxes and data query borders -- Reinventing Actionable Outcomes -- Living with the fact that we have answers and still don't know what to do -- Going where humans fear to tread on data -- Ushering in The Great Revival: Institutional knowledge and human expertise -- Chapter 3 Cryptic Patterns and Wild Guesses -- Machines Make Human Mistakes, Too -- Seeing the Trouble Math Makes -- The limits of math-only approaches -- The right math for the wrong question -- Why data scientists and statisticians often make bad question-makers -- Identifying Patterns and Missing the Big Picture -- All the helicopters are broken -- MIA: Chunks of crucial but hard-to-get real-world data -- Evaluating man-versus-machine in decision-making -- Chapter 4 The Inverted V Approach -- Putting Data First Is the Wrong Move -- What's a decision, anyway?.
Any road will take you there -- The great rethink when it comes to making decisions at scale -- Applying the Upside-Down V: The Path to the Output and Back Again -- Evaluating Your Inverted V Revelations -- Having Your Inverted V Lightbulb Moment -- Recognizing Why Things Go Wrong -- Aiming for too broad an outcome -- Mimicking data outcomes -- Failing to consider other decision sciences -- Mistaking gut instincts for decision science -- Failing to change the culture -- Part 2 Reaching the Best Possible Decision -- Chapter 5 Shaping a Decision into a Query -- Defining Smart versus Intelligent -- Discovering That Business Intelligence Is Not Decision Intelligence -- Discovering the Value of Context and Nuance -- Defining the Action You Seek -- Setting Up the Decision -- Chapter 6 Mapping a Path Forward -- Putting Data Last -- Recognizing when you can (and should) skip the data entirely -- Leaning on CRISP-DM -- Using the result you seek to identify the data you need -- Digital decisioning and decision intelligence -- Don't store all your data - know when to throw it out -- Adding More Humans to the Equation -- The shift in thinking at the business line level -- How decision intelligence puts executives and ordinary humans back in charge -- Limiting Actions to What Your Company Will Actually Do -- Looking at budgets versus the company will -- Setting company culture against company resources -- Using long-term decisioning to craft short-term returns -- Chapter 7 Your DI Toolbox -- Decision Intelligence Is a Rethink, Not a Data Science Redo -- Taking Stock of What You Already Have -- The tool overview -- Working with BI apps -- Accessing cloud tools -- Taking inventory and finding the gaps -- Adding Other Tools to the Mix -- Decision modeling software -- Business rule management systems -- Machine learning and model stores -- Data platforms. Data visualization tools -- Option round-up -- Taking a Look at What Your Computing Stack Should Look Like Now -- Part 3 Establishing Reality Checks -- Chapter 8 Taking a Bow: Goodbye, Data Scientists - Hello, Data Strategists -- Making Changes in Organizational Roles -- Leveraging your current data scientist roles -- Realigning your existing data teams -- Looking at Emerging DI Jobs -- Hiring data strategists versus hiring decision strategists -- Onboarding mechanics and pot washers -- The Chief Data Officer's Fate -- Freeing Executives to Lead Again -- Chapter 9 Trusting AI and Tackling Scary Things -- Discovering the Truth about AI -- Thinking in AI -- Thinking in human -- Letting go of your ego -- Seeing Whether You Can Trust AI -- Finding out why AI is hard to test and harder to understand -- Hearing AI's confession -- Two AIs Walk into a Bar . . . -- Doing the right math but asking the wrong question -- Dealing with conflicting outputs -- Battling AIs -- Chapter 10 Meddling Data and Mindful Humans -- Engaging with Decision Theory -- Working with your gut instincts -- Looking at the role of the social sciences -- Examining the role of the managerial sciences -- The Role of Data Science in Decision Intelligence -- Fitting data science to decision intelligence -- Reimagining the rules -- Expanding the notion of a data source -- Where There's a Will, There's a Way -- Chapter 11 Decisions at Scale -- Plugging and Unplugging AI into Automation -- Dealing with Model Drifts and Bad Calls -- Reining in AutoML -- Seeing the Value of ModelOps -- Bracing for Impact -- Decide and dedicate -- Make decisions with a specific impact in mind -- Chapter 12 Metrics and Measures -- Living with Uncertainty -- Making the Decision -- Seeing How Much a Decision Is Worth -- Matching the Metrics to the Measure -- Leaning into KPIs -- Tapping into change data. Testing AI -- Deciding When to Weigh the Decision and When to Weigh the Impact -- Part 4 Proposing a New Directive -- Chapter 13 The Role of DI in the Idea Economy -- Turning Decisions into Ideas -- Repeating previous successes -- Predicting new successes -- Weighing the value of repeating successes versus creating new successes -- Leveraging AI to find more idea patterns -- Disruption Is the Point -- Creative problem-solving is the new competitive edge -- Bending the company culture -- Competing in the Moment -- Changing Winds and Changing Business Models -- Counting Wins in Terms of Impacts -- Chapter 14 Seeing How Decision Intelligence Changes Industries and Markets -- Facing the What-If Challenge -- What-if analysis in scenarios in Excel -- What-if analysis using a Data Tables feature -- What-if analysis using a Goal Seek feature -- Learning Lessons from the Pandemic -- Refusing to make decisions in a vacuum -- Living with toilet paper shortages and supply chain woes -- Revamping businesses overnight -- Seeing how decisions impact more than the Land of Now -- Rebuilding at the Speed of Disruption -- Redefining Industries -- Chapter 15 Trickle-Down and Streaming-Up Decisioning -- Understanding the Who, What, Where, and Why of Decision-Making -- Trickling Down Your Upstream Decisions -- Looking at Streaming Decision-Making Models -- Making Downstream Decisions -- Thinking in Systems -- Taking Advantage of Systems Tools -- Conforming and Creating at the Same Time -- Directing Your Business Impacts to a Common Goal -- Dealing with Decision Singularities -- Revisiting the Inverted V -- Chapter 16 Career Makers and Deal-Breakers -- Taking the Machine's Advice -- Adding Your Own Take -- Mastering your decision intelligence superpowers -- Ensuring that you have great data sidekicks -- The New Influencers: Decision Masters. Preventing Wrong Influences from Affecting Decisions -- Bad influences in AI and analytics -- The blame game -- Ugly politics and happy influencers -- Risk Factors in Decision Intelligence -- DI and Hyperautomation -- Part 5 The Part of Tens -- Chapter 17 Ten Steps to Setting Up a Smart Decision -- Check Your Data Source -- Track Your Data Lineage -- Know Your Tools -- Use Automated Visualizations -- Impact = Decision -- Do Reality Checks -- Limit Your Assumptions -- Think Like a Science Teacher -- Solve for Missing Data -- Partial versus incomplete data -- Clues and missing answers -- Take Two Perspectives and Call Me in the Morning -- Chapter 18 Bias In, Bias Out (and Other Pitfalls) -- A Pitfalls Overview -- Relying on Racist Algorithms -- Following a Flawed Model for Repeat Offenders -- Using A Sexist Hiring Algorithm -- Redlining Loans -- Leaning on Irrelevant Information -- Falling Victim to Framing Foibles -- Being Overconfident -- Lulled by Percentages -- Dismissing with Prejudice -- Index -- EULA. |
| Record Nr. | UNINA-9911152482903321 |
|
Baker Pam
|
||
| Wiley, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Decision intelligence for dummies / / by Pam Baker
| Decision intelligence for dummies / / by Pam Baker |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2022 |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 006.3 |
| Collana | --For dummies |
| Soggetto topico |
Big data
Decision making Artificial intelligence Management information systems |
| ISBN |
1-119-82486-9
1-119-82485-0 9781119824855 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Conventions Used in This Book -- Foolish Assumptions -- What You Don't Have to Read -- How This Book Is Organized -- Part 1: Getting Started with Decision Intelligence -- Part 2: Reaching the Best Possible Decision -- Part 3: Establishing Reality Checks -- Part 4: Proposing a New Directive -- Part 5: The Part of Tens -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Decision Intelligence -- Chapter 1 Short Takes on Decision Intelligence -- The Tale of Two Decision Trails -- Pointing out the way -- Making a decision -- Deputizing AI as Your Faithful Sidekick -- Seeing How Decision Intelligence Looks on Paper -- Tracking the Inverted V -- Estimating How Much Decision Intelligence Will Cost You -- Chapter 2 Mining Data versus Minding the Answer -- Knowledge Is Power - Data Is Just Information -- Experiencing the epiphany -- Embracing the new, not-so-new idea -- Avoiding thought boxes and data query borders -- Reinventing Actionable Outcomes -- Living with the fact that we have answers and still don't know what to do -- Going where humans fear to tread on data -- Ushering in The Great Revival: Institutional knowledge and human expertise -- Chapter 3 Cryptic Patterns and Wild Guesses -- Machines Make Human Mistakes, Too -- Seeing the Trouble Math Makes -- The limits of math-only approaches -- The right math for the wrong question -- Why data scientists and statisticians often make bad question-makers -- Identifying Patterns and Missing the Big Picture -- All the helicopters are broken -- MIA: Chunks of crucial but hard-to-get real-world data -- Evaluating man-versus-machine in decision-making -- Chapter 4 The Inverted V Approach -- Putting Data First Is the Wrong Move -- What's a decision, anyway?.
Any road will take you there -- The great rethink when it comes to making decisions at scale -- Applying the Upside-Down V: The Path to the Output and Back Again -- Evaluating Your Inverted V Revelations -- Having Your Inverted V Lightbulb Moment -- Recognizing Why Things Go Wrong -- Aiming for too broad an outcome -- Mimicking data outcomes -- Failing to consider other decision sciences -- Mistaking gut instincts for decision science -- Failing to change the culture -- Part 2 Reaching the Best Possible Decision -- Chapter 5 Shaping a Decision into a Query -- Defining Smart versus Intelligent -- Discovering That Business Intelligence Is Not Decision Intelligence -- Discovering the Value of Context and Nuance -- Defining the Action You Seek -- Setting Up the Decision -- Chapter 6 Mapping a Path Forward -- Putting Data Last -- Recognizing when you can (and should) skip the data entirely -- Leaning on CRISP-DM -- Using the result you seek to identify the data you need -- Digital decisioning and decision intelligence -- Don't store all your data - know when to throw it out -- Adding More Humans to the Equation -- The shift in thinking at the business line level -- How decision intelligence puts executives and ordinary humans back in charge -- Limiting Actions to What Your Company Will Actually Do -- Looking at budgets versus the company will -- Setting company culture against company resources -- Using long-term decisioning to craft short-term returns -- Chapter 7 Your DI Toolbox -- Decision Intelligence Is a Rethink, Not a Data Science Redo -- Taking Stock of What You Already Have -- The tool overview -- Working with BI apps -- Accessing cloud tools -- Taking inventory and finding the gaps -- Adding Other Tools to the Mix -- Decision modeling software -- Business rule management systems -- Machine learning and model stores -- Data platforms. Data visualization tools -- Option round-up -- Taking a Look at What Your Computing Stack Should Look Like Now -- Part 3 Establishing Reality Checks -- Chapter 8 Taking a Bow: Goodbye, Data Scientists - Hello, Data Strategists -- Making Changes in Organizational Roles -- Leveraging your current data scientist roles -- Realigning your existing data teams -- Looking at Emerging DI Jobs -- Hiring data strategists versus hiring decision strategists -- Onboarding mechanics and pot washers -- The Chief Data Officer's Fate -- Freeing Executives to Lead Again -- Chapter 9 Trusting AI and Tackling Scary Things -- Discovering the Truth about AI -- Thinking in AI -- Thinking in human -- Letting go of your ego -- Seeing Whether You Can Trust AI -- Finding out why AI is hard to test and harder to understand -- Hearing AI's confession -- Two AIs Walk into a Bar . . . -- Doing the right math but asking the wrong question -- Dealing with conflicting outputs -- Battling AIs -- Chapter 10 Meddling Data and Mindful Humans -- Engaging with Decision Theory -- Working with your gut instincts -- Looking at the role of the social sciences -- Examining the role of the managerial sciences -- The Role of Data Science in Decision Intelligence -- Fitting data science to decision intelligence -- Reimagining the rules -- Expanding the notion of a data source -- Where There's a Will, There's a Way -- Chapter 11 Decisions at Scale -- Plugging and Unplugging AI into Automation -- Dealing with Model Drifts and Bad Calls -- Reining in AutoML -- Seeing the Value of ModelOps -- Bracing for Impact -- Decide and dedicate -- Make decisions with a specific impact in mind -- Chapter 12 Metrics and Measures -- Living with Uncertainty -- Making the Decision -- Seeing How Much a Decision Is Worth -- Matching the Metrics to the Measure -- Leaning into KPIs -- Tapping into change data. Testing AI -- Deciding When to Weigh the Decision and When to Weigh the Impact -- Part 4 Proposing a New Directive -- Chapter 13 The Role of DI in the Idea Economy -- Turning Decisions into Ideas -- Repeating previous successes -- Predicting new successes -- Weighing the value of repeating successes versus creating new successes -- Leveraging AI to find more idea patterns -- Disruption Is the Point -- Creative problem-solving is the new competitive edge -- Bending the company culture -- Competing in the Moment -- Changing Winds and Changing Business Models -- Counting Wins in Terms of Impacts -- Chapter 14 Seeing How Decision Intelligence Changes Industries and Markets -- Facing the What-If Challenge -- What-if analysis in scenarios in Excel -- What-if analysis using a Data Tables feature -- What-if analysis using a Goal Seek feature -- Learning Lessons from the Pandemic -- Refusing to make decisions in a vacuum -- Living with toilet paper shortages and supply chain woes -- Revamping businesses overnight -- Seeing how decisions impact more than the Land of Now -- Rebuilding at the Speed of Disruption -- Redefining Industries -- Chapter 15 Trickle-Down and Streaming-Up Decisioning -- Understanding the Who, What, Where, and Why of Decision-Making -- Trickling Down Your Upstream Decisions -- Looking at Streaming Decision-Making Models -- Making Downstream Decisions -- Thinking in Systems -- Taking Advantage of Systems Tools -- Conforming and Creating at the Same Time -- Directing Your Business Impacts to a Common Goal -- Dealing with Decision Singularities -- Revisiting the Inverted V -- Chapter 16 Career Makers and Deal-Breakers -- Taking the Machine's Advice -- Adding Your Own Take -- Mastering your decision intelligence superpowers -- Ensuring that you have great data sidekicks -- The New Influencers: Decision Masters. Preventing Wrong Influences from Affecting Decisions -- Bad influences in AI and analytics -- The blame game -- Ugly politics and happy influencers -- Risk Factors in Decision Intelligence -- DI and Hyperautomation -- Part 5 The Part of Tens -- Chapter 17 Ten Steps to Setting Up a Smart Decision -- Check Your Data Source -- Track Your Data Lineage -- Know Your Tools -- Use Automated Visualizations -- Impact = Decision -- Do Reality Checks -- Limit Your Assumptions -- Think Like a Science Teacher -- Solve for Missing Data -- Partial versus incomplete data -- Clues and missing answers -- Take Two Perspectives and Call Me in the Morning -- Chapter 18 Bias In, Bias Out (and Other Pitfalls) -- A Pitfalls Overview -- Relying on Racist Algorithms -- Following a Flawed Model for Repeat Offenders -- Using A Sexist Hiring Algorithm -- Redlining Loans -- Leaning on Irrelevant Information -- Falling Victim to Framing Foibles -- Being Overconfident -- Lulled by Percentages -- Dismissing with Prejudice -- Index -- EULA. |
| Record Nr. | UNINA-9911096729203321 |
| Hoboken, N.J., : Wiley, c2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Minding the machines : building and leading data science and analytics teams / / Jeremy Adamson
| Minding the machines : building and leading data science and analytics teams / / Jeremy Adamson |
| Autore | Adamson Jeremy |
| Edizione | [1] |
| Pubbl/distr/stampa | Wiley, 2021 |
| Descrizione fisica | 1 online resource (205 pages) |
| Disciplina | 005.7565 |
| Soggetto topico |
Big data
Quantitative research -- Data processing Information technology Teams in the workplace -- Data processing Leadership |
| ISBN |
1-119-78533-2
1-119-78534-0 9781119785347 |
| Classificazione |
007.6
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Table of Contents -- Title Page -- Foreword -- Introduction -- Chapter 1: Prologue -- For the Leader from the Business -- For the Career Transitioner -- For the Motivated Practitioner -- For the Student -- For the Analytics Leader -- Structure of This Book -- Why Is This Book Needed? -- Summary -- References -- Chapter 2: Strategy -- The Role of Analytics in the Organization -- Current State Assessment -- Defining the Future State -- Closing the Gap -- References -- Chapter 3: Process -- Project Planning -- Project Execution -- Summary -- References -- Chapter 4: People -- Building the Team -- Leading the Team -- Summary -- References -- Chapter 5: Future of Business Analytics -- AutoML and the No‐Code Movement -- Data Science Is Dead -- The Data Warehouse -- True Operationalization -- Exogenous Data -- Edge AI -- Analytics for Good -- Analytics for Evil -- Ethics and Bias -- Analytics Talent Shortages -- Death of the Career Transitioner -- References -- Chapter 6: Summary -- Chapter 7: Coda -- Index -- Copyright -- Dedication -- About the Author -- About the Technical Editor -- About the Foreword Author -- Acknowledgments -- End User License Agreement. |
| Record Nr. | UNINA-9911148197303321 |
| Adamson Jeremy | ||
| Wiley, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Minding the machines : building and leading data science and analytics teams / / Jeremy Adamson
| Minding the machines : building and leading data science and analytics teams / / Jeremy Adamson |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2021 |
| Descrizione fisica | 1 online resource (205 pages) |
| Disciplina | 005.7565 |
| Soggetto topico |
Big data
Quantitative research -- Data processing Information technology Teams in the workplace -- Data processing Leadership |
| ISBN |
1-119-78533-2
1-119-78534-0 9781119785347 |
| Classificazione |
007.6
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Table of Contents -- Title Page -- Foreword -- Introduction -- Chapter 1: Prologue -- For the Leader from the Business -- For the Career Transitioner -- For the Motivated Practitioner -- For the Student -- For the Analytics Leader -- Structure of This Book -- Why Is This Book Needed? -- Summary -- References -- Chapter 2: Strategy -- The Role of Analytics in the Organization -- Current State Assessment -- Defining the Future State -- Closing the Gap -- References -- Chapter 3: Process -- Project Planning -- Project Execution -- Summary -- References -- Chapter 4: People -- Building the Team -- Leading the Team -- Summary -- References -- Chapter 5: Future of Business Analytics -- AutoML and the No‐Code Movement -- Data Science Is Dead -- The Data Warehouse -- True Operationalization -- Exogenous Data -- Edge AI -- Analytics for Good -- Analytics for Evil -- Ethics and Bias -- Analytics Talent Shortages -- Death of the Career Transitioner -- References -- Chapter 6: Summary -- Chapter 7: Coda -- Index -- Copyright -- Dedication -- About the Author -- About the Technical Editor -- About the Foreword Author -- Acknowledgments -- End User License Agreement. |
| Record Nr. | UNINA-9911107924503321 |
| Hoboken, N.J., : Wiley, c2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smarter data science : succeeding with enterprise-grade data and AI projects / / Neal Fishman, Cole Stryker
| Smarter data science : succeeding with enterprise-grade data and AI projects / / Neal Fishman, Cole Stryker |
| Autore | Fishman Neal |
| Pubbl/distr/stampa | Indianapolis : , : John Wiley and Sons, , 2020 |
| Descrizione fisica | 1 online resource (307 pages) |
| Disciplina | 495.932 |
| Soggetto topico |
Management information systems
Database management Business - Management Information storage and retrieval systems - Reliability COMPUTERS - Data Science - Data Modeling & Design |
| ISBN |
1-119-69342-X
1-119-69438-8 1-119-69798-0 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Praise For This Book -- Title Page -- Copyright -- About the Authors -- Acknowledgments -- Contents at a Glance -- Contents -- Foreword for Smarter Data Science -- Epigraph -- Preamble -- Chapter 1 Climbing the AI Ladder -- Readying Data for AI -- Technology Focus Areas -- Taking the Ladder Rung by Rung -- Constantly Adapt to Retain Organizational Relevance -- Data-Based Reasoning Is Part and Parcel in the Modern Business -- Toward the AI-Centric Organization -- Summary -- Chapter 2 Framing Part I: Considerations for Organizations Using AI -- Data-Driven Decision-Making; Using Interrogatives to Gain Insight -- The Trust Matrix -- The Importance of Metrics and Human Insight -- Democratizing Data and Data Science -- Aye, a Prerequisite: Organizing Data Must Be a Forethought -- Preventing Design Pitfalls -- Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time -- Quae Quaestio (Question Everything) -- Summary -- Chapter 3 Framing Part II: Considerations for Working with Data and AI -- Personalizing the Data Experience for Every User -- Context Counts: Choosing the Right Way to Display Data; Ethnography: Improving Understanding Through Specialized Data -- Data Governance and Data Quality -- The Value of Decomposing Data -- Providing Structure Through Data Governance -- Curating Data for Training -- Additional Considerations for Creating Value -- Ontologies: A Means for Encapsulating Knowledge -- Fairness, Trust, and Transparency in AI Outcomes -- Accessible, Accurate, Curated, and Organized -- Summary -- Chapter 4 A Look Back on Analytics: More Than One Hammer -- Been Here Before: Reviewing the Enterprise Data Warehouse -- Drawbacks of the Traditional Data Warehouse -- Paradigm Shift; Modern Analytical Environments: The Data Lake -- By Contrast -- Indigenous Data -- Attributes of Difference -- Elements of the Data Lake -- The New Normal: Big Data Is Now Normal Data -- Liberation from the Rigidity of a Single Data Model -- Streaming Data -- Suitable Tools for the Task -- Easier Accessibility -- Reducing Costs -- Scalability -- Data Management and Data Governance for AI -- Schema-on-Read vs. Schema-on-Write -- Summary -- Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail -- A Need for Organization -- The Staging Zone -- The Raw Zone; The Discovery and Exploration Zone -- The Aligned Zone -- The Harmonized Zone -- The Curated Zone -- Data Topologies -- Zone Map -- Data Pipelines -- Data Topography -- Expanding, Adding, Moving, and Removing Zones -- Enabling the Zones -- Ingestion -- Data Governance -- Data Storage and Retention -- Data Processing -- Data Access -- Management and Monitoring -- Metadata -- Summary -- Chapter 6 Addressing Operational Disciplines on the AI Ladder -- A Passage of Time -- Create -- Stability -- Barriers -- Complexity -- Execute -- Ingestion -- Visibility -- Compliance -- Operate -- Quality. |
| Record Nr. | UNINA-9910555077903321 |
|
Fishman Neal
|
||
| Indianapolis : , : John Wiley and Sons, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smarter data science : succeeding with enterprise-grade data and AI projects / / Neal Fishman with Cole Stryker
| Smarter data science : succeeding with enterprise-grade data and AI projects / / Neal Fishman with Cole Stryker |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2020 |
| Descrizione fisica | 1 online resource |
| Disciplina | 495.932 |
| Soggetto topico |
Big data
Data mining Artificial intelligence |
| ISBN |
1-119-69342-X
1-119-69438-8 1-119-69798-0 9781119694380 |
| Classificazione |
007.609
005.7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Praise For This Book -- Title Page -- Copyright -- About the Authors -- Acknowledgments -- Contents at a Glance -- Contents -- Foreword for Smarter Data Science -- Epigraph -- Preamble -- Chapter 1 Climbing the AI Ladder -- Readying Data for AI -- Technology Focus Areas -- Taking the Ladder Rung by Rung -- Constantly Adapt to Retain Organizational Relevance -- Data-Based Reasoning Is Part and Parcel in the Modern Business -- Toward the AI-Centric Organization -- Summary -- Chapter 2 Framing Part I: Considerations for Organizations Using AI -- Data-Driven Decision-Making; Using Interrogatives to Gain Insight -- The Trust Matrix -- The Importance of Metrics and Human Insight -- Democratizing Data and Data Science -- Aye, a Prerequisite: Organizing Data Must Be a Forethought -- Preventing Design Pitfalls -- Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time -- Quae Quaestio (Question Everything) -- Summary -- Chapter 3 Framing Part II: Considerations for Working with Data and AI -- Personalizing the Data Experience for Every User -- Context Counts: Choosing the Right Way to Display Data; Ethnography: Improving Understanding Through Specialized Data -- Data Governance and Data Quality -- The Value of Decomposing Data -- Providing Structure Through Data Governance -- Curating Data for Training -- Additional Considerations for Creating Value -- Ontologies: A Means for Encapsulating Knowledge -- Fairness, Trust, and Transparency in AI Outcomes -- Accessible, Accurate, Curated, and Organized -- Summary -- Chapter 4 A Look Back on Analytics: More Than One Hammer -- Been Here Before: Reviewing the Enterprise Data Warehouse -- Drawbacks of the Traditional Data Warehouse -- Paradigm Shift; Modern Analytical Environments: The Data Lake -- By Contrast -- Indigenous Data -- Attributes of Difference -- Elements of the Data Lake -- The New Normal: Big Data Is Now Normal Data -- Liberation from the Rigidity of a Single Data Model -- Streaming Data -- Suitable Tools for the Task -- Easier Accessibility -- Reducing Costs -- Scalability -- Data Management and Data Governance for AI -- Schema-on-Read vs. Schema-on-Write -- Summary -- Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail -- A Need for Organization -- The Staging Zone -- The Raw Zone; The Discovery and Exploration Zone -- The Aligned Zone -- The Harmonized Zone -- The Curated Zone -- Data Topologies -- Zone Map -- Data Pipelines -- Data Topography -- Expanding, Adding, Moving, and Removing Zones -- Enabling the Zones -- Ingestion -- Data Governance -- Data Storage and Retention -- Data Processing -- Data Access -- Management and Monitoring -- Metadata -- Summary -- Chapter 6 Addressing Operational Disciplines on the AI Ladder -- A Passage of Time -- Create -- Stability -- Barriers -- Complexity -- Execute -- Ingestion -- Visibility -- Compliance -- Operate -- Quality. |
| Record Nr. | UNINA-9910812216103321 |
| Hoboken, N.J., : Wiley, c2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||