Here are some potential issues identified with the dataset alignment in the provided CSV files:

1. **Inconsistent Column Headers and Data Alignment**:
    - **Issue**: Misaligned columns in `all-ages.csv` and `recent-grads.csv` can lead to incorrect data interpretation.
    - **Evidence**: 
      ```python
      # Display the first few rows to understand their structure
      all_ages.head(), recent_grads.head()
      ```
    - **Description**: The columns in both datasets should align correctly for comparative analysis, but there may be discrepancies. For instance, `Major_code` and `Major` columns exist in both datasets, but their alignment and additional columns like `Rank` in `recent-grads.csv` can cause confusion if not handled properly.

2. **Mismatch in Data Rows**:
    - **Issue**: The number of rows corresponding to specific majors may not align between the two datasets.
    - **Evidence**: 
      ```python
      # Comparing the number of entries in both datasets
      all_ages.shape, recent_grads.shape
      ```
    - **Description**: The `all-ages.csv` and `recent-grads.csv` files might contain different numbers of rows for the same `Major_code` or `Major`, leading to misalignment when merging or comparing the data. Ensuring that each `Major_code` in `all-ages.csv` has a corresponding row in `recent-grads.csv` is crucial for accurate analysis.

3. **Column Order Differences**:
    - **Issue**: Different ordering of columns between datasets can cause misalignment during data merging.
    - **Evidence**:
      ```python
      all_ages.columns, recent_grads.columns
      ```
    - **Description**: The order of columns in `all-ages.csv` and `recent-grads.csv` may not be the same, leading to potential misalignment issues when merging datasets. For example, columns like `Median` salary might appear in different positions across the datasets, complicating direct comparisons.

To resolve these issues, here are some steps:

1. **Standardize Column Headers and Order**: Ensure both datasets have the same column headers and order for columns that need to be compared or merged.

2. **Align Data Rows by Key**: Use a common key such as `Major_code` to align rows between the two datasets accurately.

3. **Check for Missing Values**: Ensure there are no missing values in key columns that could lead to misalignment during data analysis.

4. **Validation Scripts**: Create scripts to validate the alignment of data between the datasets before performing any analysis.

If you need more detailed investigation or scripts to fix these issues, please let me know!