Beginner SQL for Data Analytics - No Coding Experience Req'd

Discover what a database is, its core components—tables, columns, and rows—and how it organizes sales, product, and customer data, with a comparison to Excel's limitations.

Learn how SQL, the structured query language, lets you talk to databases, join data from multiple tables, and pull meaningful insights with precise questions.

learn why sql is essential to access company data stored in databases, analyze it without IT dependency, and boost employability by pulling more relevant data.

Download and set up the required tools: the MySQL database, the Sublime Text editor, and a graphical user interface to write, run, and export SQL queries to Excel.

Download MySQL server to host your data, visit the download link, choose Windows, click the 32 bit installer, and follow the steps to complete configuring.

download and install Sublime Text, a free text editor, and the right GUI—HeidiSQL for PC or Sequel Pro for Mac—and download the database alongside.

Learn how relational databases organize data into uniquely named tables and connect them with shared identifiers like customer_id and product_id to avoid data repetition and streamline information.

Load the rainbow database into MySQL by logging in with your install password, dragging the rainbow database file into the source, and verifying the database appears before using HeidiSQL.

Connect to the rainbow database using Heidi sequel, view tables and data, and learn how primary keys and IDs relate customers, products, sales, and surveys to enable relational queries.

Learn the basic sql code outline, including select, from, where, group by, and the semicolon, and practice filling in the blanks to build queries.

Learn how to use the select and from clauses to pull specific columns from a salary data table, and display them in a results table, such as id and salary.

Learn to set up Sublime Text for SQL, annotate your code with hashes, and write a basic select from a customer table using the asterisk to pull all columns.

Learn to pull specific columns from a table with select and from, alias columns with as, and create a clean results table (first name, last name, zip code) for analytics.

Discover how the where clause filters SQL results with comparison operators, enabling you to pull only matching records from a salary data table.

Use the where clause to filter query results and target customers by zip code, selecting first name, last name, and zip code from the customer table for geographic campaigns.

Learn how to refine where clauses using and and or to filter data, with examples like zip codes 80002 or 80003, and salaries above 55000 in Boston.

Learn to filter data with and/or in SQL using where clauses to pull customers by zip codes from the customer table, and select first name, last name, and zip code.

Use the in operator to pull a range of data by grouping multiple or conditions. For example, pull ID numbers and locations from salary data table for Boston or Denver.

Learn how to use the sql in statement to filter by multiple values and employ between for numeric ranges, demonstrated with selecting customers by zip codes.

Use the between operator in the where clause to pull data within a range, inclusive of the start and end values, as shown with salaries and zip codes.

Learn how to use the distinct function in SQL to pull unique values from a column, with examples using locations and state names from the geography table.

Learn to pull unique values with distinct from the geography table by selecting state_name, and order the results in ascending or descending order for clean data.

learn how to use order by in sql to sort query results, choose ascending or descending order, sort by multiple columns, and use column numbers for clarity, with practical examples.

Show how to query customer data using select from where and order by, filter by a zip range, and sort by last name descending for clean data export and analysis.

Explore min and max arithmetic functions in sql, learning to return the smallest or largest values with simple select and where clauses, using examples from salary and sales data.

Master count, sum, and average in SQL with Excel-like syntax through practical salary data examples, including calculating averages by location and total revenue.

Master the group by clause to aggregate data and count purchases by customer or employees by location, using select from where and optional order by.

Group by organizes data from the sales table into distinct product_id groups and sums revenue to reveal per-product sales, demonstrating a practical SQL workflow.

learn to join tables in a relational database using keys like customer id and product id to pull revenue and customer or geography data across sales, geography, and customer tables.

Learn to join two or more tables by linking related columns with a where clause, ensuring one table has unique values, and selecting data from each table.

Learn how to join the sales and product tables on product_id, then group by category and sum revenue to reveal category-level sales in this example.

Tackle a hands-on challenge to sum sales by manufacturer by joining three tables—sales, product, and manufacturer—using product_id and manufacturer_id to connect data and write the code.

Combine sales, product, and manufacturer tables by joining on product I.D. and manufacturer I.D., then group by manufacturer name and revenue.

Learn how to use the left function to extract a fixed number of characters from text, mirroring Excel, to obtain year and month from dates and to build simple queries.

Pull the last purchase date and month/year for every customer by joining the sales and customer tables, showing customer i.d., first name, last name, with max and left formulas.

Left join the sales and customer tables on customer_id to group by customer_id and show each customer's last purchase year and month using max.

Explore how the having clause filters grouped data in SQL, using group by and count to include only cities with more than one employee.

Learn how to filter grouped data with the having clause. Count customer transactions and select those with 15 or more purchases, then order results for loyalty offers.

Create a query to list inactive customers by joining customer, sales, date tables, filtering last purchase before 2017, and outputting id, first name, last name, email, and last purchase date.

Group by multiple columns to compute total sales by year and quarter, using the year and quarter from the date table to aggregate sales.

Join the sales and date tables on date_id, then group by year and by quarter to sum revenue for each period.

Link the product and sales data to compute total sales by category for weapons and armor, using a where filter and group by category.

Connect sales and product tables on product id, pull category, and compute total revenue by armor and weapon using sum and where with in, then group by category.

Learn to create and drop temporary tables in SQL to store intermediate results, reference them later, and use them with joins to compute revenue and analyze survey data.

Create and name a temporary table to consolidate product data, join the survey and sales by product tables, and produce per-product revenue and average score results.

Create a temporary table with customer IDs and their first purchase date to identify new customers by month, then count new customers per month.

Create a temporary table from sales to capture each customer's first purchase by year-month, then count monthly acquisitions and export results for easy charting in Excel.

Leverage the foundation of sequel to pull data, perform analysis, and link tables using select from where and group by, exporting results to Excel for manipulation.

Practice SQL basics through case studies, starting with select and from, then where and group by, and keep exploring a mock database to build confidence.