Learn SQL for Data Analysis in Layman Terms for Non-Techies!

Explore how SQL, a programming language for relational databases, lets analysts execute queries, retrieve data, and perform scalable aggregations across multiple tables.

Learn how databases organize data into tables with unique column names, understand different database types for various purposes, and prepare to dive into sql for data analysis.

Follow a step-by-step guide to download the Developer Edition, install SQL Server, then install SQL Server Management Studio (SSMS), connect to the database engine, and start writing and executing queries.

Download the AdventureWorks 2019 backup, move it to the SQL Server backup folder, and restore it in SQL Server Management Studio to create a ready-to-practice sample database.

Master basic SQL syntax with select and from in the correct order on the Adventureworks database, viewing all columns or specific columns from human resources.employees.

Replace the asterisk with all the columns names of the table to answer the problem, showing how explicit column lists achieve the result without using asterisk.

Learn that SQL keywords can be written in lowercase and still work, while whitespace variations like a space or line break are treated the same.

Explore why SQL column names avoid spaces and use a dot in table names. Explain when to use double quotes for spaces or rename columns for presentation.

Practice problem solution demonstrates replacing star with explicit column names and renaming columns using double quotes to produce updated column names in the output.

Explore how to use select top and select top percent to limit results, with examples returning 5, 50, and 145 rows. Understand the performance implications on large tables.

Master filtering data with the where clause in SQL by using select, from, and where to display only rows where the gender column equals m.

Master SQL comparison operators on numerical data to filter results, using equal to, not equal to, greater than, less than, and their inclusive versions on metrics like vacation hours.

Identify employees with sick leave hours greater than 30 and vacation hours less than or equal to 10, using the greater than and less than or equal to operators.

Master SQL comparison operators on non-numerical data, focusing on equal to and not equal to for selecting rows that match or exclude non-numeric values. Use single quotes for non-numeric values.

Practice SQL filtering by selecting rows where job title equals research and development manager and where marital status does not equal s, reinforcing equal to and not equal to operators.

Practice sql arithmetic with plus, minus, multiply, and divide operators across columns in a row to compute total hours, and learn about aggregate functions in the next course.

Learn to create derived columns in sql by applying arithmetic operations to column values and constants, such as adding ten to vacation hours and sick leave hours to total hours.

Write and execute the full SQL query using the provided column names, create a new column named total numbers, and review the results.

Use parentheses in SQL to control the order of operations and simplify arithmetic, such as averaging vacation hours and sick leave hours and creating an average hours column.

Practice solving a sql query to display rows from the human resources employee table where both vacation hours and sick leave hours exceed 90, noting 290 total and 157 matching.

Explore how SQL logical operators extend comparison operators to filter data with multiple conditions in a single query, covering like, in, between, is null, and, or, not.

Discover how the SQL like operator enables pattern matching in a where clause using % and _ wildcards to find products starting with C, containing chain, or chain ring.

Apply the sql like operator to filter data ending with a character using a leading percent wildcard, shown with product names ending with m on the production dot product table.

Learn how the sql like operator finds records that contain a specific phrase using percent wildcards before and after the term, demonstrated with product names containing 'lock'.

Discover how to identify 'nut' within a product name using the SQL like operator with percent wildcards, and review the resulting matches.

Identify rows where the product name starts with metal using the sql pattern metal percent. The percent symbol acts as a wildcard, matching any sequence after metal.

Use the SQL in operator to filter data by a list of values, using parentheses and commas, with quotes for strings, shown with purchase order detail and country region examples.

Execute sql queries using the in operator to filter a table by country and region, solving a practice problem.

Use the SQL between operator and the and operator to filter rows within an inclusive range, as shown on the product cost history dataset.

Learn to display the standard cost range from 700 to 1000 by executing a query against the production cost history table.