https://github.com/chanupadeshan/basic-data-analytics

basic data analysis project using sql
https://github.com/chanupadeshan/basic-data-analytics

data-analysis eda exploratory-data-analysis sql window-function

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basic data analysis project using sql

• Host: GitHub
• URL: https://github.com/chanupadeshan/basic-data-analytics
• Owner: chanupadeshan
• Created: 2025-10-05T18:51:00.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-10-23T10:44:10.000Z (9 months ago)
• Last Synced: 2025-10-23T12:31:47.244Z (9 months ago)
• Topics: data-analysis, eda, exploratory-data-analysis, sql, window-function
• Language: TSQL
• Homepage:
• Size: 792 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# basic-data-analytics

This repository contains simple example SQL scripts and CSV datasets to explore a small data warehouse-style project using SQL Server (mssql).

Contents

- `datasets/` - sample CSV files (gold.dim_customers.csv, gold.dim_products.csv, gold.fact_sales.csv)

- `scripts/` - useful SQL scripts:

  - `initialize_database.sql` — creates the `DataWarehouseAnalytics` database, `gold` schema, tables, and bulk-loads data from CSV files (this will DROP the database if it exists)

  - `database_exploration.sql` — lists tables and columns

  - `date_exploration.sql` — date range and age queries

  - `dimentsions_exploration.sql` — (typo: "dimentions") explore dimension tables

  - `magnitute_analysis.sql` — aggregation and revenue queries

  - `measures_exploration.sql` — measures and summary report

- `ranking_analysis.sql` — top/bottom ranking queries

  ## EDA primer — Dimensions, Measures and common explorations

  🧩 **Dimensions vs. Measures**

  EDA starts by distinguishing between two types of data fields:

  - **Dimensions** – Qualitative or categorical fields that describe data.

    Examples: Category, Product, Region, Gender, Customer_ID

  - **Measures** – Quantitative or numerical fields that can be aggregated.

    Examples: Sales, Quantity, Profit, Age

  ➡️ Rule of thumb: If a column is numeric and makes sense to sum or average, it’s a measure. Otherwise, it’s a dimension.

  📏 **Dimensions Exploration**

  Dimension exploration helps identify unique values and distribution across categorical fields.

  Common SQL operations:

  - `SELECT DISTINCT column_name`

  - `COUNT(*)` / `COUNT(DISTINCT column_name)`

  - `GROUP BY column_name` with `COUNT()`, `AVG()`, `SUM()`

  - Percentiles / frequency distributions (e.g., COUNT() / total)

  📆 **Date Exploration**

  Dates are crucial for understanding time trends and activity periods.

  Common SQL operations:

  - MIN(order_date), MAX(order_date) — find dataset span

  - DATEDIFF / DATEPART — compute durations or extract year/month/week

  - GROUP BY YEAR(order_date), MONTH(order_date) — time series aggregations

  - Rolling/window functions for moving averages (e.g., OVER(ORDER BY order_date ROWS BETWEEN ...))

  📈 **Measures Exploration**

  Measure exploration summarizes numerical columns using aggregations.

  Common SQL operations:

  - SUM(sales_amount), AVG(price), MIN(), MAX()

  - COUNT(DISTINCT order_number) — orders vs items

  - Distribution checks (histograms, percentiles) using NTILE or PERCENTILE_CONT

  📊 **Magnitude Analysis**

  Magnitude analysis connects measures with dimensions to show how metrics vary across categories.

  Common SQL operations:

  - GROUP BY category -> SUM(sales_amount) to see category revenue

  - ORDER BY SUM(sales_amount) DESC to find largest contributors

  - JOIN fact -> dimension tables to attribute measures to descriptive fields

  🏆 **Ranking Analysis**

  Ranking is used to find top or bottom performers in a dataset.

  Common SQL operations:

  - ROW_NUMBER(), RANK(), DENSE_RANK() with ORDER BY SUM(...) DESC/ASC

  - Use a CTE/window function then filter by rank (e.g., WHERE rank <= 10)

## Part 2: Advanced Data Analytics

For more advanced SQL warehouse exploration, see [advanced-data-analytics](https://github.com/chanupadeshan/advanced-data-analytics).