Advanced analytics with Transact-SQL : exploring hidden patterns and rules in your data / Dejan Sarka.
2021
QA76.9.B45 S37 2021
Linked e-resources
Linked Resource
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Advanced analytics with Transact-SQL : exploring hidden patterns and rules in your data / Dejan Sarka.
Author
ISBN
9781484271735 (electronic bk.)
1484271734 (electronic bk.)
9781484271728
1484271726
1484271734 (electronic bk.)
9781484271728
1484271726
Published
[Berkeley, CA] : Apress, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations
Item Number
10.1007/978-1-4842-7173-5 doi
Call Number
QA76.9.B45 S37 2021
Dewey Decimal Classification
005.7
Summary
Learn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration. No analysis is good without data quality. Advanced Analytics with Transact-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and survival analysis. Forecasting with exponential moving averages and autoregression is covered as well. Every web/retail shop wants to know the products customers tend to buy together. Trying to predict the target discrete or continuous variable with few input variables is important for practically every type of business. This book helps you understand data science and the advanced algorithms use to analyze data, and terms such as data mining, machine learning, and text mining. Key to many of the solutions in this book are T-SQL window functions. Author Dejan Sarka demonstrates efficient statistical queries that are based on window functions and optimized through algorithms built using mathematical knowledge and creativity. The formulas and usage of those statistical procedures are explained so you can understand and modify the techniques presented. T-SQL is supported in SQL Server, Azure SQL Database, and in Azure Synapse Analytics. There are so many BI features in T-SQL that it might become your primary analytic database language. If you want to learn how to get information from your data with the T-SQL language that you already are familiar with, then this is the book for you. You will learn to: Describe distribution of variables with statistical measures Find associations between pairs of variables Evaluate the quality of the data you are analyzing Perform time-series analysis on your data Forecast values of a continuous variable Perform market-basket analysis to predict customer purchasing patterns Predict target variable outcomes from one or more input variables Categorize passages of text by extracting and analyzing keywords.
Note
Includes index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Description based on print version record.
Available in Other Form
Linked Resources
Record Appears in
Table of Contents
Part I. Statistics
1. Descriptive Statistics.-2. Associations Between Pairs of Variables
Part II. Data Preparation and Quality
3. Data Preparation
4. Data Quality and Information
Part III. Dealing with Time
5. Time-Oriented Data
6. Time-Oriented Analyses
Part IV. Data Science
7. Data Mining
8. Text Mining.
1. Descriptive Statistics.-2. Associations Between Pairs of Variables
Part II. Data Preparation and Quality
3. Data Preparation
4. Data Quality and Information
Part III. Dealing with Time
5. Time-Oriented Data
6. Time-Oriented Analyses
Part IV. Data Science
7. Data Mining
8. Text Mining.