Assignment one

[Yutong2002]

What changes are you trying to make?

I added SQL queries for Assignment 3 that cover filtering, aggregation, joins, and date manipulation using farmersmarket dataset. These include examples of using CASE, JOIN, GROUP BY, and strftime() for extracting month/year.

What did you learn from the changes you have made?

I learned how to:

• Use conditional logic with basic sql statements
• Combine multiple tables using INNER JOIN
• Aggregate data with SUM() grouped by customer or vendor
• Extract month and year parts from dates using strftime() in SQLite

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

I considered writing subqueries instead of CTEs (WITH clauses) for some aggregations, but I used CTEs for better readability and structure.

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

I initially received SQLite syntax errors related to string formatting and date extraction. I resolved these by reviewing the SQLite strftime modifiers documentation and verifying column data types before applying filters.

How were these changes tested?

I ran all SQL queries in the provided .db file using SQLite Browser.
Each query successfully produced the expected results.

A reference to a related issue in your repository (if applicable)

N/A

Checklist

• I can confirm that my changes are working as intended

[Yutong2002]

Assignment 1_section 1

[Yutong2002]

Assignment 1_section 4

In the shared article, Rafia Qadri explores how Pakistan’s national database system (NADRA) defines what constitutes a “family” and, by extension, who belongs, who counts, and who gets excluded. The database translates social relations into data structures. For example, a “family tree” centered on a male head of household. This technical decision embeds the patriarchal value system into the infrastructure: it assumes the male as default and determines legitimacy through him. The result is that widows, divorced women, transgender people, and non-traditional families etc. face exclusion or bureaucratic invisibility.

In daily life, there are various types of biased value system on databases. For instances, medical databases always assume binary gender categories (M/F), excluding intersex and nonbinary identities. Citizenship and immigration often databases treat “nationhood” as fixed and linear, excluding diasporic or stateless populations. These systems seem neutral, but they quietly shape experiences of fairness, belonging, and identity.

[khsergvl]

Thank you for submitting assignment 1.
All tasks are correct.
Mark: 30/30
Please make sure that PR contains only files related to assignment. Your PR contains 34 files modified instead of 3-4 expected.