While R has been around since 2000, and Python since 1991, SQL dates to the early 1970s. Don’t let the vocal, relatively recent adoption of these other languages distract you: SQL is a vital part of the data analysts’s stack of tools. (You also should consider these tools not as substitutes, but as complementary slices of the data analytics stack.)

The below graphic comes from a DataQuest blog post entitled “Want a Job in Data? Learn SQL.” The author mined 25,000 jobs on Indeed containing “data” as a search term.

Before Python, R or any other skill came SQL. So, what is this language, and what do data analysts need to know?

SQL is the language used to manage data held in a relational database. These tasks are summarized by the famous “CRUD” acronym: create, read, update, and delete. Generally, data analysts are responsible for the second activity, or reading information from a database.

That is the focus of this half-day workshop: to give analysts a command over the most common tasks for reading information out of a database for data analysis.

SQL comes in many flavors, from Access to PostgreSQL. For these elementary reading commands, the differences are negligible. I have designed to conduct the class in SQLite, which has the benefit of being fast, flexible and open-source. We will use the SQLiteStudio environment to move from visual interaction with the database to coding.

Lesson 1: Databases and data analytics

Objective: Student can navigate the SQLiteStudio environment to visually inspect tables in a database

Description:

• The basics of working with a database
• Database parts of speech
• The basics of working with SQLite + SQLiteStudio

Exercises: Retrieve dimensions and database types of a table

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 2: Coding in SQL

Objective: Student can write style-compliant SQL scripts to retrieve tables from a field

Description:

• Styling in SQL
• Working with scripts: opening, saving, executing
• Selecting all fields from a table
• Selecting some fields from a table
• Aliasing a field
• Arithmetic operations on a field
• String operations on a field
• Limiting query results

Exercises: Drills

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 3: Sorting & filtering

Objective: Student can sort and filter the results of a query

Description:

• Conditional operators
• Logical operators
• Filtering one or more fields
• Sorting one or more fields

Exercises: Drills

Assets needed: Home prices dataset

Lesson 4: Aggregating

Objective: Student can group and aggregate the results of a SQL query

Description:

• Counting and listing distinct records
• Understanding NULL
• Grouping and field arithmetic
• Aggregating and aliasing
• Filtering aggregation results

Exercises: Drills

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 5: SQL and table joins

Objective: Student can join two or more tables from the same database

Description:

• How relational databases work
• JOINs and NULLs
• INNER JOIN
• LEFT OUTER JOIN

Exercises: Drills

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 6: Intermediate joins

Objective: Student can use less common techniques to join two or more tables from the same database

Description:

• RIGHT OUTER JOIN
• FULL OUTER JOIN

Set operators

Exercises: Drills

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 7: SQL for data analysis

Objective: Student can conduct basic data exploration and analysis in SQL

Description:

• CASE expressions
• Subqueries
• Common table expressions

Exercises: Drills

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 8: SQL for data analysis, continued

Objective: Student can conduct intermediate data exploration and analysis in SQL

Description:

• Window functions
• Correlations
• Regression coefficients

Exercises: Drills

Assets needed: Home prices dataset

Time: 50 minutes

While the next wave in data analytics features tools like Power Query that allow users to accomplish many tasks with no coding, there is something to be said for learning data analysis the old-fashioned way: through SQL commands.

After all, it’s a “structured query language,” and I find that learning SQL tightens and structures your thoughts around data analysis.

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The post Learning guide: Introduction to SQL, one-day workshop first appeared on Stringfest Analytics.