Navigate to sections in the README:
- Tools & Skills Used
- Collaborators
- Quick Access
- Project Overview
- About Unicorn Company
- Unicorn's Database
- Data Exploration using SQL
- Analysis with Spreadsheets
- Getting Insights using Tableau
- Executive summary and presentation
Meet our team behind the analysis:
| Name | Role | GitHub | |
|---|---|---|---|
| Annelize Krause | Project Co-ordinator & SQL | krauseannelize | annelizekrause |
| Reha Rabi Binti Mat | Spreadsheets & Tableau | reharabi | Coming soon |
| Atukunda Shakirah | Tableau & Report | Shakirah525 | Coming soon |
| Sajjad Mirzapour | Presentation & Report | Coming soon | sajjad-mirzapour |
Jump to key files and deliverables:
- The data
- Raw Data Extract
- Metadata
- PostgreSQL dataset:
postgresql://Test:bQNxVzJL4g6u@ep-noisy-flower-846766-pooler.us-east-2.aws.neon.tech/Unicorn?sslmode=require- SQL data exploration
- Spreadsheet analysis
- Tableau visualization
- Project presentation
This group project forms part of the Masterschool Data programme to combine what we have learned on SQL, Spreadsheets, and Tableau. We explore the sales performance of Unicorn Company, an e-commerce business offering a wide range of products, for the years 2015 to 2018 to identify performance trends, weaknesses, and growth opportunities. The project consists of 4 main parts:
- Data Exploration using SQL
- Analysis with Spreadsheets
- Getting Insights using Tableau
- Executive summary and presentation
Unicorn is a family business led by two highly invested stakeholders. They already have a small data analytics team but want to expand it significantly over the next few months. As part of the interview process for a new DA position, they provide a sample dataset from their sales data. The interview task is to analyze the data, find interesting insights, and identify weak areas and opportunities for Unicorn to boost its business growth.
URL to PostgreSQL dataset:
postgresql://Test:bQNxVzJL4g6u@ep-noisy-flower-846766-pooler.us-east-2.aws.neon.tech/Unicorn?sslmode=requireBelow is a schema of Unicorn's database:
For a more detailed description of the database, consult the metadata sheet.
To kick off the analysis, we use SQL to answer a series of core business questions about the Unicorn sales database, covering customer trends, city profitability, and product performance.
- The SQL Specifications detail the required queries.
- All SQL code and outputs are documented in our Jupyter Notebook.
- The Jupyter Notebook can also be viewed directly in Google Colab.
- The full joined dataset CSV export from the bonus question is also available for review.
In this stage, we use spreadsheets to clean and further analyze the Unicorn dataset, addressing data quality issues and performing targeted business analysis.
- Detailed requirements are in the Spreadsheet Specifications.
- All data cleaning steps, analyses, and answers are documented in our Google Sheets workbook, with a downloadable Excel version (.xlsx) also available.
In this phase, we create interactive dashboards in Tableau to visualize key business metrics and uncover actionable insights from the cleaned and explored Unicorn dataset.
- Suggested metrics to explore and guidelines are in the Tableau Specifications.
- The published Dashboard can be viewed on Tableau Public without any software.
- The Tableau workbook (.twbx) can be explored using Tableau Desktop.
This final section presents a concise overview of our data analysis findings tailored for key stakeholders.
- Read the Executive Summary (PDF) highlighting main insights and recommendations.
- Watch the Video Presentation.
- Review the Presentation Slide Deck (PDF) used during the presentation.