This is the second course in our Specialization in Teradata and Data Analysis. In the first course, we set up the concepts, principles, and practical basics to install software, load data, and design a logical and physical data model. In this second course, we'll improve our techniques for data analysis, with an eye on efficiency and storage for your real-world applications on the job.
In Module 1, we’ll grow your SQL Toolkit with multi-table, aggregate functions like SUM, AVG, MAX and COUNT. We’ll also expand your concept of primary and foreign keys, so you can make your first JOIN commands in SQL and define relationships between tables.
Our second module is focused on SQL subqueries. We’ll start with single-row subqueries, comparing them to JOIN commands. Then we’ll examine multiple-row subqueries, which allow you to compare a value against multiple values returned from a subquery.
In Module 3, we’ll examine SQL Techniques. We’ll recognize use cases and strategies to use windowed functions in SQL. We’ll define the structure of hierarchical queries in SQL. And we’ll identify for using indexes, so we can optimize our tables for data retrieval.
Overview
Syllabus
- Growing Your SQL Toolkit
- In this first module, we’ll look at effective requirements gathering, the use of aggregate functions, and the principles of normalization to refine our SQL querying skills. To make more valuable SQL queries, our first step is requirements gathering. Requirements Gathering involves detailed specifications about the data's format, quality, and sources. You’ll learn to prioritize data based on potential impact and engage stakeholders to help uncover essential, sometimes hidden, requirements. You will learn the most common aggregate functions available in Teradata: SUM, AVG, MAX, and COUNT. We’ll examine when we would typically use these functions, and how the output of these functions is different from traditional SQL queries. We’ll take a closer look at three levels of data normalization. Normalization reduces redundancy and ensures that each piece of data is stored precisely once, linked directly to a primary key. Finally, we’ll use SQL joins to link data across multiple tables. Using Inner Joins and left Joins which help us tailor our queries to meet specific analytical needs.
- Subqueries in Teradata
- In this module, we will practice some practical applications of SQL subqueries, focusing on both single-row and multiple-row subqueries to enhance your data analysis skills. We'll start by exploring single-row subqueries, an advanced SQL technique perfect for conducting precise data checks within larger queries. You'll learn how to structure these subqueries to compare specific values against results returned by another query, which is crucial for tasks such as verifying if inventory levels meet demand or if a customer's purchase exceeds the average. Following that, we will examine multiple-row subqueries, which allow you to compare a value against multiple values returned from a subquery. This session will cover how to use SQL operators like IN, ANY, or ALL to filter and analyze data effectively. Through detailed examples and structured queries, this module will equip you with the knowledge to apply these techniques directly to real-world business intelligence scenarios, enhancing both the specificity and relevance of your data analysis.
- SQL Techniques
- This module introduces key SQL concepts and techniques to enhance data analysis using Teradata. Window functions enable advanced data aggregation over specified ranges, allowing for dynamic time-based evaluations and facilitating calculations such as running totals, moving averages, and lagging or leading values. Hierarchical queries provide a framework for analyzing parent-child relationships within data, crucial for understanding complex structures like supply chains. This module covers the syntax and practical applications of these queries, highlighting their use in organizing and analyzing hierarchical data effectively. Finally, the module explains the importance of indexes in SQL for quicker data retrieval. Indexes prioritize frequently accessed columns, enhancing query performance and ensuring efficient data processing. These concepts collectively equip data analysts with robust tools for sophisticated data analysis and strategic decision-making.
Taught by
Eric Grose
