Learn SQL for Data Analysis with Google Big Query

Write and run your first sql query in Google BigQuery, using select from a table, then switch from star to specific columns like name and understand limit and data processing.

Learn to use the except clause in a select statement to return all columns while excluding one or more columns, such as removal date and temporary, using star and except.

Find past queries in the history tab, re-run them in the editor, and save or save as with sharing settings and link sharing.

Filter data with the where statement in SQL, using operators =, !=, >, <, >=, <= on station IDs, returning rows like id = 5 or id <= 5.

Learn to order your result set with the order by clause, choosing ascending or descending order, and apply multi-column sorting to refine results.

Explore sql date filtering in the where clause using equal to, not equal to, greater than or equal to, greater than, and less than, then order results by date.

Filter with the where clause using boolean data types, using true or false to select installed stations; capitalization doesn't affect results, and booleans don't use greater or less than operators.

Apply the and operator to combine filters in a where clause, filtering by install date Jan 1 to Jun 1 and bike count over ten, yielding seven and four results.

Learn to use the or operator in a where clause to combine filters, selecting rows that meet at least one criterion such as stations installed before 2016 or after 2018.

Learn how to use the between operator to filter data in SQL, including inclusive bounds for dates and counts, and simplify where clauses by using between.

Master the in operator to filter by multiple values in Google BigQuery, replacing multiple or statements with a single where id in (5,7,11,14) and optionally include station names.

Filter strings using the = operator with quotes, handle not equals, and respect string data types, including numeric-looking values. Use character length to filter results by name length.

Learn how the like operator filters strings for substrings using percent wildcards, with examples for matches, ends with, and begins with, and note that BigQuery is case sensitive.

Learn to handle case sensitivity in a BigQuery where clause by applying the lower() function to both the column name and the filter value, enabling case-insensitive matches.

Filter null values in sql with where clauses like install date is null and install date is not null, noting that null means missing data and strings may be blank.

Learn to filter data with not operators in SQL: not between, not in, and not like, applying them to dates, IDs, and string patterns to exclude undesired rows.

Learn how to filter rows using the where clause in SQL queries, selecting columns from a project dataset and table, and ordering results, establishing the foundation for BigQuery queries.

Use the count function to tally rows in the cycle hire table or count unique values. Alias the new column as row_count; use underscores in column names.

Learn to count unique values with count distinct in SQL on Google BigQuery, showing how many times a value appears and how to derive unique combinations across multiple columns.

Learn how sum and average, analytical aggregate functions, compute total and mean values on numeric columns, excluding nulls, illustrated by converting bike trip durations from seconds to hours and days.

Apply min and max aggregate functions to calculate min and max values from a column, create descriptive duration columns in minutes, and inspect date ranges to explore dataset quality.

Learn how var_pop and std_dev_pop compute variance and standard deviation over a data column in BigQuery, and apply these to the duration field in seconds and minutes.

Learn how to use the group by clause to aggregate data by a specific dimension in BigQuery, counting trips by bike ID and averaging durations by start station name.

Explore how order by comes after group by to sort query results by trips counted, using count(*) or the alias num trips, in ascending or descending order.

Group by multiple columns to compute the average trip duration between specific start and end stations, referencing the select statement, then use where and order by to filter and rank results.

Explore grouping by column positions in SQL queries for speed, compare with writing full column names for readability, and learn to order by column positions as well.

Learn to use having with group by to filter aggregated values after grouping, and understand why where filters before grouping and how aliases help filter average duration under 15 minutes.

Learn how to join tables in a database to relate data using IDs, such as start station and cycle stations, and select columns with a join on clause in BigQuery.

Learn to alias tables to shorten references in select and on clauses, improving readability and avoiding long full table names in joins.

Learn how to use a left join to keep all rows from the left table while matching on the right table, exposing nulls when there is no match.

Define new columns with the if statement to classify cycle stations by name like Hackney and by large docks when more than 40 bike docks exist, otherwise null.

Learn to use the case statement to return values from multiple conditions, using case when, then, else, end, and create a new string column named doc size description.

Export BigQuery results to Google Sheets or CSV, with options to save to Google Drive or a local file, and export to Google Cloud Storage for sharing and visualizations.

Export your BigQuery data to Google Data Studio to visualize fields and charts, create a table with install date and name, and plot counts versus bike counts for each station.