Teradata: Improving Analysis and Storage
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...
By LearnQuest on Coursera
About This Course
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.
Topics Covered
Frequently Asked Questions
How much does Teradata: Improving Analysis and Storage cost?
Teradata: Improving Analysis and Storage costs $49. Check the course page for current pricing and available discounts.
Who teaches Teradata: Improving Analysis and Storage?
Teradata: Improving Analysis and Storage is taught by LearnQuest, LearnQuest.
What skill level is Teradata: Improving Analysis and Storage for?
This course is designed for beginner learners.
Similar Courses
Minitab Applied Statistics & Hypothesis Testing Mastery
EDUCBA
Evaluate and Optimize Enterprise Log Analytics
EDUCBA
Linear Algebra from Elementary to Advanced
Johns Hopkins University
Data Science Fundamentals with Python and SQL
IBM