Data Analyst
For data analysis, I think the most important thing is to clarify the business goals, identify the gaps with the goals, and determine the problems that need to be solved.
Core Issue 1: Defining the problem - Identifying the reasons for the gap (requires analytical ability and business sense, both personally and on average for peers)
Core Issue 2: Cause analysis - After finding possible reasons, refine the indicators
Core Issue 3: Analyzing indicators - Find the necessary data to support the analysis (how to find it depends on how to utilize tools)
Core Issue 4: Data analysis - Analysis methods
Core Issue 5: Solution testing - Once the cause is hypothesized, how to judge if the assumption is correct and proceed with the experiment
Core Issue 6: Continuous improvement - How to find predictive indicators to prevent similar problems from recurring and detect them early
For example, when analyzing the overspending of advertising budget:
First, you need to realize that the problem you are analyzing is overspending. Did you have a definition for overspending previously?
Then, what are the reasons?
- Sales
- Price
- Conversion
- Bidding
- Increase in bidding
Overall increase? Local increase? factors? Market factors?
A particular department is having a problem; which product in the department?
How to prevent it?
Why do it?
What to do?
How to do it?
As for SQL statements, such as:
- Execution order
- Table additions, deletions, and reductions, primary key PID
- Aggregate functions
- Date conversion
- Table joins
- Window functions
- Sliding window functions
- Row and column transformations
In the era of GPT, is this really important?