This is the course I have completed from Udemy which includes SQL queries and explanations cover a wide range of SQL concepts and are a great resource for those learning SQL or looking to deepen their knowledge in SQL data manipulation.
- Finding the Earliest Order: To find the earliest order and the associated account name, you can use a query that involves a JOIN and an ORDER BY clause with LIMIT 1.
- Counting Channel Occurrences: To determine the number of times a particular channel was used in web_events for each region, you can JOIN multiple tables and use GROUP BY, COUNT, and ORDER BY clauses.
- DISTINCT Keyword: The DISTINCT keyword is used in SELECT statements to retrieve unique rows for the specified columns.
- Using DISTINCT to Find Duplicate Accounts: To check if any accounts are associated with more than one region, you can use the DISTINCT keyword in a query involving JOIN operations.
- HAVING Clause: HAVING is used for filtering aggregated data. You need to use HAVING when filtering data based on aggregated values in your query.
- Identifying Sales Reps with Many Accounts: To find sales reps managing more than 5 accounts, you can use a query with a JOIN and GROUP BY, followed by HAVING to filter the results.
- Finding High-Spending Accounts: To discover accounts that spent more than $30,000 in total, you can JOIN the accounts and orders tables, GROUP BY accounts, and apply HAVING for filtering.
- Filtering with DISTINCT and HAVING: Use DISTINCT for aggregating unique values in a column and HAVING for filtering aggregated data based on conditions.
- Subquery for Finding Sales Reps: To identify the number of sales reps with more than 200 accounts, use a subquery within a HAVING clause with COUNT.
- Trends in Yearly Sales Totals: You can find trends in yearly sales totals by using DATE_PART to extract the year and SUM for total sales, ordering the results by year.
- Analyzing Sales in Specific Months: To determine which month had the greatest sales for Parch & Posey, use DATE_PART and WHERE to filter the results.
- Combining Date and Sales Data: By using DATE_TRUNC and JOIN, you can extract information for specific periods like days or weeks and analyze sales data within those periods.
- Combining Account Levels and Total Sales: To classify accounts into different spending levels based on total sales and account name, use a CASE statement and GROUP BY.
- Creating a Column for Orders: You can create a new column that indicates the level of orders based on specific criteria, such as order amount or number of orders.
- Data Cleaning: Techniques like CAST, COALESCE, and CASE can be used for data cleaning, type conversion, and handling missing or inconsistent data.
- Dealing with String Data: Functions like POSITION, STRPOS, LEFT, RIGHT, CONCAT, and manipulation of string data can be applied to extract or format data within columns.
- Email and Password Generation: You can generate email addresses and initial passwords for customers based on specific rules using string manipulation functions and expressions.
- CAST vs. COALESCE: CAST is used for explicit type conversion, while COALESCE is for replacing null values in data. These can be useful in cleaning and transforming data.
- Using COALESCE to Fill Nulls: COALESCE can help replace NULL values in columns with default values, such as 0 in the case of missing data.
- Analyzing Data with NULL Values: The LEFT JOIN can help analyze data with missing or NULL values in the joined columns, and COALESCE can replace them with meaningful data.
- Understanding COALESCE Function: COALESCE not only replaces NULL values but also can choose the first non-NULL value from a list of columns.
- Using DISTINCT: The DISTINCT keyword can be used in SELECT statements to obtain unique rows, removing duplicates from the result set.