Let's break them down: 1. Intuition
The query aims to retrieve information about managers in the Employees table, including their IDs, names, the number of employees reporting to them, and the average age of their reports.
The solution involves using a self join. Since we were told that a manager is also an employee, we will join the table to itself, where the employee_id field is equal reports_to field. On this premise, we aggregate the number of employees who report directly to the manager,
their average age, and names. 2. For the 2nd query, we employed a subquery in our Where clause to filter out those employees that were multiple departments, and the OR operator to ensure those with a primary flag have the 'Y' character. Finally, we select the employee_id...a
...and their respective departments from the employee table, that meets the criteria we had set.
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A few days back, 2 Data Analyst newbies showed me mistakes they encountered during their analysis process. It dawned on me that lots of times, we don't diligently follow through the data analysis process, especially the cleaning phase.
In this short🧵, I break down this process.
PHASE 1: ASK QUESTIONS
The first step in the analysis process is to formulate a problem to solve or a question to answer, such as: are customers aged 21 to 30 more likely to churn than customers aged 41 to 50? As analysts, we must be very curious, we must ASK QUESTIONS!
PHASE 2: Collect & Store Data
Next, we need to collect and store the necessary data, which could require the use of a database or a spreadsheet. For large datasets, we store them in a database. For datasets shy of a million records, spreadsheet software like Excel can be used.
Let's break down each question 1. Logic: Show the classes, where the number of student is more than 5. The word "At least" tells us something, we have to include 5 as part of our filtering condition, hence >=5 2. This looks pretty straight forward. For each user, we want to....
..return the number followers, hence the need to use the Group BY clause. This clause allows us to aggregate data against the unique categorical/qualitative values in a field. 3. The given SQL query finds the maximum value (num) from the "mynumbers" table that appears only once.
Let's break down each code 1. The ending part to the question is where the major conditional logic lies. We were told to return the needed columns, and price for the first year of every product sold. Since we do not have distinct column showing min years for each product,
We have to create that with a subquery in the Where clause. We are concerned about the first year of every product sold, so we aggregate the min years for each product. After doing that, we tell SQL to return the prices of products, the year, and qty when the product was first..