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I'm learning SQL, Python, and Excel together, here's my Study Guide: Data-Language Foundations (Level 1) so it fits with your new game.

📘 WEEK 1 STUDY GUIDE — “Getting the Data”

🎯 GOAL

Understand how to call, view, and select data in SQL, Python (pandas), and Excel — the foundation of all analytics work.

🧩 1. Concept Overview

ConceptDefinitionWhy It Matters

Data TableA structured collection of data organized in rows and columns.It’s the foundation of relational databases, spreadsheets, and dataframes.

Record (Row)One observation or entry (e.g., one student, one sale).Each row represents a unique data point.

Field (Column)A specific type of information in the table (e.g., Name, Date, Price).Columns describe what is being measured.

QueryA request for data from a database.It’s how you ask questions of your data.

Result SetThe data returned after a query or filter.What you actually analyze afterward.

🧠 2. Comparing Languages Side-by-Side

TaskSQLPython (pandas)Excel

Show all dataSELECT * FROM Sales;salesThe worksheet itself

Preview first 5 rowsSELECT * FROM Sales LIMIT 5;sales.head()Scroll to top rows

View structureDESCRIBE Sales;sales.info()Click “Table Design”

Filter rows (WHERE)SELECT * FROM Sales WHERE Price > 100;sales[sales["Price"] > 100]=FILTER(A2:C100, C2:C100>100)

Select specific columnsSELECT Name, Price FROM Sales;sales[["Name","Price"]]Select those columns

Sort dataSELECT * FROM Sales ORDER BY Price DESC;sales.sort_values("Price", ascending=False)Data → Sort Z→A

🔍 3. Key SQL Commands to Know First

  1. SELECT — retrieve data

  2. FROM — specify which table

  3. WHERE — filter rows

  4. ORDER BY — sort

  5. LIMIT — restrict how many rows

  6. AS — rename columns (SELECT name AS "Customer")

🐍 4. Key Python pandas Functions

  1. pd.read_csv() — load CSV

  2. .head() — preview top rows

  3. .info() — column details

  4. .describe() — summary statistics

  5. .loc[] / .iloc[] — row/column selection

  6. .sort_values() — sort data

  7. .query() — SQL-style filtering

📊 5. Excel Functions for Data Calls

  1. FILTER(range, condition) — pull rows meeting condition

  2. SORT(range, column#, TRUE/FALSE) — order results

  3. INDEX / MATCH — basic lookups

  4. UNIQUE(range) — list distinct values

  5. TAKE(range, n) — preview first n rows

  6. XLOOKUP(lookup_value, lookup_array, return_array) — modern lookup

💡 6. Practice Prompts (Matching Your Game)

  • “Show all customers from the Customers table.”

  • “List only products with price over $100.”

  • “Show first 5 sales ordered by date descending.”

  • “Display all employees who joined after 2024-01-01.”

  • “Select customer name and city only.”

🧭 7. Memory Hooks

  • SQL = Sentence language (“Select name from table where…”).

  • Python = Object language (DataFrame.method).

  • Excel = Formula language (Function(arguments)).

🧩 8. Weekly Focus Flow

Day 1: Understand what tables/rows/columns mean.
Day 2: Practice SELECT and .head() and FILTER().
Day 3: Filtering with WHERE, logical operators (AND, OR).
Day 4: Sorting and limiting results.
Day 5: Mini-project — compare your SQL, Python, and Excel answers for the same 5 prompts.

Would you like me to create this as a printable study guide PDF (with examples + space to write answers) or as an interactive HTML “study mode” page that matches your game’s design?

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