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COURSE TOPIC

Data Analyst Portfolio Project: End-to-End Sales Dashboard

Bring Excel, SQL, Python, and Power BI together in an end-to-end sales analytics portfolio project.

Part of course Data Analyst Course
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Difficulty Level
Intermediate
Estimated Learning Time
6-8 hours
OVERVIEW

About Data Analyst Portfolio Project: End-to-End Sales Dashboard

This portfolio project brings course tools together in a practical workflow. Prepare sales data, analyze it, calculate KPIs, build a dashboard, and explain your findings and limitations.

LEARNING OUTCOMES

What You Will Learn

  • Translate a sales scenario into measurable questions.
  • Prepare and validate data for analysis.
  • Use SQL and Python to calculate and cross-check summaries.
  • Build a Power BI dashboard with useful KPIs and filters.
  • Document findings, workflow, assumptions, and limitations.
TOPIC GUIDE

Learn Data Analyst Portfolio Project: End-to-End Sales Dashboard

Frame the problem

Begin with a question, such as how sales vary by month, category, or region. Define measures before building charts: revenue, order count, average order value, or change over time.

Prepare and validate data

Inspect fields, date ranges, missing values, duplicates, and inconsistent labels. Calculate revenue from quantity and unit price when available. Record corrections and check row counts and totals after preparation.

Analyze with SQL and Python

Use SQL to retrieve records and create grouped summaries. Use Python and Pandas for additional cleaning or exploration. Compare outputs where practical so an unnoticed transformation does not change conclusions.

Build and test the dashboard

Use Power BI to present KPIs, trends, and category or region comparisons. Add useful filters, label measures clearly, and test interactions. Validate key totals independently.

Present the project

Include a concise summary of the problem, data, workflow, findings, and limitations. Keep query logic and preparation notes with the dashboard. Describe next questions rather than overstating what sample data proves.

Practice

Write two business questions, document quality checks, summarize monthly revenue and orders in SQL, validate one result with Python or Excel, and build a dashboard. Finish with a README covering findings, assumptions, limitations, and next steps.

PRACTICAL EXAMPLE

Example / Code

Example 
SELECT DATE_FORMAT(order_date, '%Y-%m') AS sales_month, region,
       SUM(quantity * unit_price) AS revenue,
       COUNT(DISTINCT order_id) AS order_count
FROM sales WHERE order_date IS NOT NULL
GROUP BY DATE_FORMAT(order_date, '%Y-%m'), region;
TRY IT YOURSELF

Practice Task

Apply what you learned with a practical activity.

Create a sales case study: define questions, document data checks, summarize monthly revenue in SQL, validate one result independently, build a dashboard, and write a README with findings and limitations.

TOOLKIT

Tools & Technologies

  • Excel
  • SQL
  • Python
  • Pandas
  • Power BI
  • Power Query
  • DAX
CONTINUE LEARNING

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COURSE TOPICS

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Data Analytics Fundamentals: From Questions to Insights

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Advanced Excel for Data Analysis: Formulas, PivotTables and Reports

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SQL for Data Analysis: Filters, Joins and Window Functions

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Python for Data Analysis with NumPy and Pandas

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Power BI Dashboards: Power Query, Data Models and DAX

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