Tabla de Contenidos: “…SAP operational activities after migration to ESS -- 4.1 Tools to manage and monitor your database tasks -- 4.1.1 SAP R/3 DBA Planning Calendar -- 4.1.2 IBM DB2 UDB Control Center -- 4.2 Online backup of the database -- 4.2.1 Use SAP R/3 DBA Planning Calendar -- 4.2.2 Use the IBM DB2 UDB Control Center -- 4.2.3 Use the IBM DB2 command line -- 4.3 Offline backup of the database -- 4.3.1 Use SAP R/3 DBA Planning Calendar -- 4.3.2 Use IBM DB2 UDB Control Center -- 4.3.3 Use the IBM DB2 UDB command line -- 4.4 Archive inactive logs -- 4.4.1 Use SAP R/3 DBA calendar -- 4.5 Back up tablespaces -- 4.5.1 Use the IBM DB2 UDB Control Center -- 4.5.2 Use the IBM DB2 UDB Command Line -- 4.6 Restore databases -- 4.6.1 Use the IBM DB2 Control Center -- 4.6.2 Use the IBM DB2 command line -- 4.7 Restore tablespaces -- 4.7.1 Use the IBM DB2 UDB Control Center -- 4.7.2 Use the IBM DB2 UDB command line -- 4.8 Other daily activities -- 4.8.1 Maintain statistics -- 4.8.2 Update table statistics -- 4.8.3 Reorganize tables…”

Tabla de Contenidos: “….] -- Quantitative analysis on PCA-based statistical 3D face shape modeling / A.Y.A. Maghari, I. …”

Tabla de Contenidos: “…8.2.3 Implementing privacy models with Python -- 8.3 Protecting privacy by modifying the data mining output -- 8.3.1 Association rule hiding -- 8.3.2 Reducing the accuracy of data mining operations -- 8.3.3 Inference control in statistical databases -- 8.4 Privacy protection in data management systems -- 8.4.1 Database security and privacy: Threats and vulnerabilities -- 8.4.2 How likely is a modern database system to leak private information? …”

Tabla de Contenidos: “…4.2 Referencing Practices and Citation Patterns -- 5 Conclusions -- 5.1 Challenges -- References -- Quotation Statistics and Culture in Literature and in Other Humanist Disciplines -- 1 Introduction -- 2 Quotation Culture in the Humanities -- 2.1 Characteristics of Quotations in the Humanities -- 2.2 Disapproval of Bibliometrics and of `Quantities' Per se -- 2.3 The European Reference Index for the Humanities -- 2.4 Rigor and Quotations -- 3 Low Quotation Frequencies in Literature -- 3.1 Materials and Methods -- 3.2 Results -- 3.3 Possible Objections -- 4 Discussion -- References -- Part IV Evaluation of Research in the Humanities in Practice -- Peer Review in the Social Sciences and Humanities at the European Level: The Experiences of the European Research Council -- 1 The European Research Council's Mission -- 1.1 How Does the ERC Work? …”

Tabla de Contenidos: “…-- Definitions and Concepts -- Independent Variables -- Test Units -- Dependent Variables -- Extraneous Variables -- Random Assignment to Experimental and Control Groups -- Experiment -- Experimental Design -- Definition of Symbols -- Validity in Experimentation -- Internal Validity -- External Validity -- Controlling Extraneous Variables -- A Classification of Experimental Designs -- Pre-experimental Designs -- One-Shot Case Study -- One-Group Pretest-Posttest Design -- Static Group Design -- True Experimental Designs -- Pretest-Posttest Control Group Design -- Posttest-Only Control Group Design -- Statistical Designs -- Factorial Design -- Selecting an Experimental Design -- Experimentation on the Web -- Experimental Versus Nonexperimental Designs -- Limitations of Experimentation -- Cost -- Administration -- Application: Test Marketing -- International Marketing Research -- Marketing Research and Social Media -- Ethics in Marketing Research -- Summary -- Companion Website -- Key Terms and Concepts -- Suggested Cases and Video Cases -- Live Research: Conducting a Marketing Research Project -- Acronyms -- Review Questions -- Applied Problems -- Internet Exercises -- Online Video Case 6.1: AFLAC: Marketing Research Quacks a Duck -- Chapter 7: Measurement and Scaling -- Overview -- Learning Objectives -- Measurement and Scaling -- Primary Scales of Measurement -- Nominal Scale -- Ordinal Scale -- Interval Scale -- Ratio Scale -- Comparative and Noncomparative Scaling Techniques -- Comparative Scaling Techniques -- Paired Comparison Scaling -- Rank-Order Scaling -- Constant Sum Scaling -- Noncomparative Scaling Techniques -- Continuous Rating Scale -- Itemized Rating Scales -- Multi-Item Scales -- Scale Evaluation -- Reliability -- Validity -- Relationship Between Reliability and Validity -- Choosing a Scaling Technique…”

Tabla de Contenidos: “…Fixed Window Aggregation over an Event Time -- Sliding Window Aggregation over Event Time -- Aggregation State -- Watermarking: Limit State and Handle Late Data -- Arbitrary Stateful Processing -- Arbitrary Stateful Processing with Structured Streaming -- Handling State Timeouts -- Arbitrary State Processing in Action -- Extracting Patterns with mapGroupsWithState -- User Sessionization with flatMapGroupsWithState -- Handling Duplicate Data -- Fault Tolerance -- Streaming Application Code Change -- Spark Runtime Change -- Streaming Query Metrics and Monitoring -- Streaming Query Metrics -- Monitoring Streaming Queries via Callback -- Monitoring Streaming Queries via Visualization UI -- Streaming Query Summary Information -- Streaming Query Detailed Statistics Information -- Troubleshooting Streaming Query -- Summary -- Chapter 8: Machine Learning with Spark -- Machine Learning Overview -- Machine Learning Terminologies -- Machine Learning Types -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Machine Learning Development Process -- Spark Machine Learning Library -- Machine Learning Pipelines -- Transformers -- Estimators -- Pipeline -- Pipeline Persistence: Saving and Loading -- Model Tuning -- Speeding Up Model Tuning -- Model Evaluators -- Machine Learning Tasks in Action -- Classification -- Model Hyperparameters -- Example -- Regression -- Model Hyperparameters -- Example -- Recommendation -- Model Hyperparameters -- Example -- Deep Learning Pipeline -- Summary -- Chapter 9: Managing the Machine Learning Life Cycle -- The Rise of MLOps -- MLOps Overview -- MLflow Overview -- MLflow Components -- MLflow in Action -- MLflow Tracking -- MLflow Projects -- MLflow Models -- MLflow Model Registry -- Model Deployment and Prediction -- Summary -- Index…”

Tabla de Contenidos: “…House price indices measure developments over time taking account of quality changes -- Coverage is extensive at the national level -- Coverage has expanded but remains more limited at the regional level -- House price levels are a key area for future work -- A measurement agenda for house price statistics is emerging -- Better assess housing vulnerability among households -- Develop more robust data on evictions -- Homelessness is challenging to measure and compare across countries -- A measurement agenda is called for to improve the evidence base on eviction and homelessness -- Measure local land-use regulations -- Measuring land-use regulations poses significant challenges -- Towards internationally comparable data on local land-use planning policies -- References -- Notes…”

Tabla de Contenidos: “…Intro -- Table of contents -- Basic statistics of Canada, 2015 -- Glossary -- Executive summary -- Activity is shifting from energy to non-energy sectors in response to price signals -- Output is adjusting to lower commodity prices -- Vulnerabilities related to housing and household debt are still increasing, albeit at a slower pace -- Household debt and house prices are high -- Productivity growth has been weak until recently -- Multifactor productivity has stagnated -- Assessment and recommendations -- Figure 1. …”

Tabla de Contenidos: “…Intro -- Table of contents -- Basic statistics of Czech Republic, 2015 -- Executive summary -- Growth has picked up temporarily -- Real GDP growth -- Strengthening innovation policies and market regulations to bolster productivity -- Convergence in labour productivity has stalled -- Improving the effectiveness of the public sector -- Public administration performance indicator -- Assessment and recommendations -- Turning to long-term challenges while the economy is growing -- Key challenges for stronger and inclusive growth -- Figure 1. …”

Tabla de Contenidos: “…-- Pandas Series -- Creating a Series Using a Specified Index -- Accessing Elements in a Series -- Specifying a Datetime Range as the Index of a Series -- Date Ranges -- Pandas DataFrame -- Creating a DataFrame -- Specifying the Index in a DataFrame -- Generating Descriptive Statistics on the DataFrame -- Extracting from DataFrames -- Selecting the First and Last Five Rows -- Selecting a Specific Column in a DataFrame -- Slicing Based on Row Number -- Slicing Based on Row and Column Numbers -- Slicing Based on Labels -- Selecting a Single Cell in a DataFrame -- Selecting Based on Cell Value -- Transforming DataFrames…”

“… Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. …”

“…Many experts believe that reliability is not only a matter of statistics but is a multidisciplinary scientific topic, involving materials, tests, simulations, quality tools, manufacturing, electronics, mechatronics, environmental engineering and Big Data, among others. …”

Tabla de Contenidos: “…-- How to Become Certified -- Who Should Buy This Book -- How This Book Is Organized -- Chapter Features -- Bonus Digital Contents -- Conventions Used in This Book -- Google Cloud Professional ML Engineer Objective Map -- How to Contact the Publisher -- Chapter 1 Framing ML Problems -- Translating Business Use Cases -- Machine Learning Approaches -- Supervised, Unsupervised, and Semi-supervised Learning -- Classification, Regression, Forecasting, and Clustering -- ML Success Metrics -- Regression -- Responsible AI Practices -- Summary -- Exam Essentials -- Review Questions -- Chapter 2 Exploring Data and Building Data Pipelines -- Visualization -- Box Plot -- Line Plot -- Bar Plot -- Scatterplot -- Statistics Fundamentals -- Mean -- Median -- Mode -- Outlier Detection -- Standard Deviation -- Correlation -- Data Quality and Reliability -- Data Skew -- Data Cleaning -- Scaling -- Log Scaling -- Z-score -- Clipping -- Handling Outliers -- Establishing Data Constraints -- Exploration and Validation at Big-Data Scale -- Running TFDV on Google Cloud Platform -- Organizing and Optimizing Training Datasets -- Imbalanced Data -- Data Splitting -- Data Splitting Strategy for Online Systems -- Handling Missing Data -- Data Leakage -- Summary -- Exam Essentials -- Review Questions -- Chapter 3 Feature Engineering -- Consistent Data Preprocessing -- Encoding Structured Data Types -- Mapping Numeric Values -- Mapping Categorical Values -- Feature Selection -- Class Imbalance -- Classification Threshold with Precision and Recall -- Area under the Curve (AUC)…”

Tabla de Contenidos: “…The Nature, Tools, and Ethics of Research -- What Research Is Not -- What Research Is -- Philosophical Assumptions Underlying Research Methodologies -- Conceptual Analysis Exercise: Identifying Philosophical Perspectives Underlying Research Questions -- Tools of Research -- The Library and Its Resources -- Computer Technology -- Statistics -- Language -- Practical Application: Communicating Effectively Through Writing -- Guidelines: Writing to Communicate -- The Human Mind -- Critical Thinking -- Deductive Logic -- Inductive Reasoning -- Scientific Method -- Theory Building -- Collaboration with Other Minds -- Ethical Issues in Research -- Honesty -- Transparency -- Respect for Participants' Rights and Well-Being -- Concern for the Greater Good -- Conceptual Analysis Exercise: Identifying Potential Ethical Problems in Research Studies -- Reflections on Noteworthy Research -- Exploring Research in Your Field -- Practical Application: Identifying Important Tools in Your Discipline -- Checklist: Interviewing an Expert Researcher -- Summary -- For Further Reading -- Answers To The Conceptual Analysis Exercise: "Identifying Philosophical Perspectives Underlying Research Questions -- Answers To The Conceptual Analysis Exercise: "Identifying Potential Ethical Problems in Research Studies -- Part II: Focusing Your Research Efforts -- Chapter 2. …”

Tabla de Contenidos: “…. -- Chapter 13: Spatial Statistics and Hotspots -- Selecting features within a specific distance -- Getting ready -- How to do it... -- How it works... -- Finding the mean center of a geographic distribution -- Getting ready -- How to do it... -- There's more... -- Identifying the central feature based on geographic distribution -- Getting ready -- How to do it... -- How it works... -- Calculating the geographic dispersion of data -- Getting ready -- How to do it... -- How it works... -- Chapter 14: 3D Maps and Analysis -- Creating a 3D scene -- Getting ready -- How to do it... -- How it works... -- Enabling your data to store Z coordinates (elevation) -- Getting ready -- How to do it... -- How it works... -- Creating multipatch features from 2D -- Getting ready -- How to do it... -- How it works... -- Creating 3D features -- Getting ready -- How to do it... -- How it works... -- Calculating lines of sight -- Getting ready -- How to do it... -- How it works... -- Calculating the volume of a polygon -- Getting ready -- How to do it... -- How it works... -- Index -- Other Books You May Enjoy…”

“…The book only requires the reader to be knowledgeable in the basics of algebra and statistics. The Mathematical formulae are only fully proven when the proof brings some useful insight. …”

“…The Level I curriculum builds a foundation in both traditional and alternative investment markets--for example, the range of statistics that are used to define investment performance as well as the many types of hedge fund strategies. …”

Tabla de Contenidos: “…-- The evolution of machine learning -- Factors that led to the success of machine learning -- Machine learning, statistics, and AI -- Categories of machine learning -- Supervised and unsupervised machine learning -- Supervised machine learning -- Vehicle Mileage, Number Recognition and other examples -- Unsupervised machine learning -- Subdividing supervised machine learning -- Common terminologies in machine learning -- The core concepts in machine learning -- Data management steps in machine learning -- Pre-processing and feature selection techniques -- Centering and scaling -- The near-zero variance function -- Removing correlated variables -- Other common data transformations -- Data sampling -- Data imputation -- The importance of variables -- The train, test splits, and cross-validation concepts -- Splitting the data into train and test sets -- The cross-validation parameter -- Creating the model -- Leveraging multicore processing in the model -- Summary -- Chapter 8: Machine Learning Deep Dive -- The bias, variance, and regularization properties -- The gradient descent and VC Dimension theories -- Popular machine learning algorithms -- Regression models -- Association rules -- Confidence -- Support -- Lift -- Decision trees -- The Random forest extension -- Boosting algorithms -- Support vector machines -- The K-Means machine learning technique -- The neural networks related algorithms -- Tutorial - associative rules mining with CMS data -- Downloading the data -- Writing the R code for Apriori -- Shiny (R Code) -- Using custom CSS and fonts for the application -- Running the application -- Summary -- Chapter 9: Enterprise Data Science -- Enterprise data science overview…”

Tabla de Contenidos: “…. -- Exploring the data with standard statistics -- How to do it... -- There's more... -- Finding genomic features from sequencing annotations -- How to do it... -- There's more... -- Doing metagenomics with QIIME 2 Python API -- Getting ready -- How to do it... -- There's more... -- Chapter 5: Working with Genomes -- Technical requirements -- Working with high-quality reference genomes -- Getting ready -- How to do it... -- There's more... -- See also -- Dealing with low-quality genome references -- Getting ready -- How to do it... -- There's more... -- See also -- Traversing genome annotations -- Getting ready -- How to do it... -- There's more... -- See also -- Extracting genes from a reference using annotations -- Getting ready -- How to do it... -- There's more... -- See also -- Finding orthologues with the Ensembl REST API -- Getting ready -- How to do it... -- There's more... -- Retrieving gene ontology information from Ensembl -- Getting ready -- How to do it... -- There's more... -- See also -- Chapter 6: Population Genetics -- Managing datasets with PLINK -- Getting ready -- How to do it... -- There's more... -- See also -- Using sgkit for population genetics analysis with xarray -- Getting ready -- How to do it... -- There's more... -- Exploring a dataset with sgkit -- Getting ready -- How to do it... -- There's more... -- See also -- Analyzing population structure -- Getting ready -- How to do it... -- See also -- Performing a PCA…”

“…This textexplains what to do and what to avoid at the bargaining table, facilitated byan integration of theory, scientific research, and real-world application.The 7th Edition contains new and updated exercises,statistics, and examples from business, politics, and personal life spanningthe globe to illustrate effective, as well as ineffective, negotiation skills.Armed with these, students will be ready to improve their relational as well aseconomic outcomes…”