Advanced Analytics with Transact-SQL: Exploring Hidden Patterns and Rules in Your Data

Description

Intermediate-Advanced user level Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Part I: Statistics Chapter 1: Descriptive Statistics Variable Types Demo Data Frequency Distribution of Discrete Variables Frequencies of Nominals Frequencies of Ordinals Descriptive Statistics for Continuous Variables Centers of a Distribution Measuring the Spread Skewness and Kurtosis Conclusion Chapter 2: Associations Between Pairs of Variables Associations Between Continuous Variables Covariance Correlation Interpreting the Correlation Associations Between Discrete Variables Contingency Tables Chi-Squared Test Associations Between Discrete and Continuous Variables Testing Continuous Variable Moments over a Discrete Variable Analysis of Variance Definite Integration Conclusion Part II: Data Preparation and Quality Chapter 3: Data Preparation Dealing with Missing Values NULLs in T-SQL Functions Handling NULLs String Operations Scalar String Functions Aggregating and Splitting Strings Derived Variables and Grouping Sets Adding Computed Columns Efficient Grouping Data Normalization Range and Z-score Normalization Logistic and Hyperbolic Tangent Normalization Recoding Variables Converting Strings to Numerics Discretizing Numerical Variables Conclusion Chapter 4: Data Quality and Information Data Quality Measuring Completeness Finding Inaccurate Data Measuring Data Quality over Time Measuring the Information Introducing Entropy Mutual Information Conditional Entropy Conclusion Part III: Dealing with Time Chapter 5: Time-Oriented Data Application and System Times Inclusion Constraints Demo Data System-Versioned Tables and Issues A Quick Introduction to System-Versioned Tables Querying System-Versioned Tables Surprises Optimizing Temporal Queries Modifying the Filter Predicate Using the Unpacked Form Time Series Moving Averages Conclusion Chapter 6: Time-Oriented Analyses Demo Data Exponential Moving Average Calculating EMA Efficiently Forecasting with EMA ABC Analysis Relational Division Top Customers and Products Duration of Loyalty Survival Analysis Hazard Analysis Conclusion Part IV: Data Science Chapter 7: Data Mining Demo Data Linear Regression Autoregression and Forecasting Association Rules Starting from the Negative Side Frequency of Itemsets Association Rules Look-Alike Modeling Training and Test Data Sets Performing Predictions with LAM Naïve Bayes Training the NB Model Performing Predictions with NB Conclusion Chapter 8: Text Mining Demo Data Introducing Full-Text Search Full-Text Predicates Full-Text Functions Statistical Semantic Search Quantitative Analysis Analysis of Letters Word Length Analysis Advanced Analysis of Text Term Extraction Words Associations Association Rules with Many Items Conclusion Index