SQL for Data Analytics, Third Edition
(SQL-DA.AJ2) / ISBN : 978-1-64459-485-8
Über diesen Kurs
Entfesseln Sie die Leistungsfähigkeit von SQL in der Welt der Datenanalyse mit unserem Kurs „SQL für Datenanalyse, dritte Ausgabe“. Egal, ob Sie Anfänger sind oder Ihre Datenanalysefähigkeiten verbessern möchten, dieser Kurs vermittelt Ihnen praktische Kenntnisse und praktische Erfahrung im Einsatz von SQL für eine effektive Datenanalyse. Am Ende dieses Kurses verfügen Sie über die Fähigkeiten, SQL für eine effiziente Datenanalyse einzusetzen.
Fähigkeiten, die Sie erwerben werden
Holen Sie sich die Unterstützung, die Sie brauchen. Melden Sie sich für unseren Kurs mit Lehrer an.
Unterricht
10+ Unterricht | 150+ Übungen | 80+ Tests | 33+ Karteikarten | 33+ Glossar der Begriffe
Testvorbereitung
67+ Fragen vor der Beurteilung | 67+ Fragen nach der Bewertung |
Praktische Übungen
17+ LiveLab | 17+ Videoanleitungen | 01:51+ Hours
Preface
- About the Course
- Audience
- About the Lessons
- Conventions
- Setting up Your Environment
- Installing Git
- Loading the Sample Datasets – Windows
- Loading the Sample Datasets – Linux
- Loading the Sample Datasets – macOS
- Running SQL files
- Accessing the Code Files
Understanding and Describing Data
- Introduction
- Data Analytics and Statistics
- Types of Statistics
- Working with Missing Data
- Statistical Significance Testing
- SQL and Analytics
- Summary
The Basics of SQL for Analytics
- Introduction
- The World of Data
- Relational Databases and SQL
- PostgreSQL Relational Database Management System (RDBMS)
- Creating Tables
- Basic Data Types of SQL
- Data Structures: JSON and Arrays
- Column Constraints
- Updating Tables
- SQL and Analytics
- Summary
SQL for Data Preparation
- Introduction
- Assembling Data
- Cleaning Data
- Transforming Data
- Summary
Aggregate Functions for Data Analysis
- Introduction
- Aggregate Functions
- Aggregate Functions with the GROUP BY Clause
- Aggregate Functions with the HAVING Clause
- Using Aggregates to Clean Data and Examine Data Quality
- Summary
Window Functions for Data Analysis
- Introduction
- Window Functions
- Statistics with Window Functions
- Window Frame
- Summary
Importing and Exporting Data
- Introduction
- The COPY Command
- Using Python with your Database
- Going Passwordless
- Summary
Analytics Using Complex Data Types
- Introduction
- Date and Time Data types for Analysis
- Performing Geospatial Analysis in PostgreSQL
- Using Array Data types in PostgreSQL
- Using JSON Data types in PostgreSQL
- Text Analytics Using PostgreSQL
- Summary
Performant SQL
- Introduction
- The Importance of Highly Efficient SQL
- Database Scanning Methods
- Killing Queries
- Functions and Triggers
- Summary
Using SQL to Uncover the Truth: A Case Study
- Introduction
- Case Study
- Summary
Understanding and Describing Data
- Creating a Histogram in Excel
- Exploring Dealership Sales Data
The Basics of SQL for Analytics
- Running the SELECT Query
- Creating and Modifying Tables
SQL for Data Preparation
- Generating a List Using the UNION Query
- Building a Sales Model
Aggregate Functions for Data Analysis
- Analyzing Sales Data Using Aggregate Functions
Window Functions for Data Analysis
- Analyzing Sales Using Window Frames and Window Functions
Importing and Exporting Data
- Reading, Visualizing, and Saving Data in Python
Analytics Using Complex Data Types
- Performing Text Analytics
- Searching and Analyzing Sales
Performant SQL
- Implementing Hash Indexes
- Creating Functions with Arguments
- Creating a Trigger to Track Average Purchases
Using SQL to Uncover the Truth: A Case Study
- Using SQL Techniques to Collect Preliminary Data
- Analyzing the Difference in the Sales Price Hypothesis
- Analyzing the Performance of the Email Marketing Campaign