AdventureWorks is a fictitious multinational manufacturing company. The data set used for this report is from the AdventureWorks2014 version. The goal of this project is to demonstrate how building powerful and beautiful (I hope :)) reports in Power BI by following the data visualization best practices and the data modelling patterns, showing the awesome features of Power BI like Report Page Tooltip, Calculated Groups, Forecasting, What-If parameters, Time Intelligence Functions, complex DAX code, custom charts and how it is easy to integrate Power BI with Python/R script in order to build machine learning models and so creating advanced and predictive analytics reports. Reports Pages are as follow: - P&L Overview: Profit & Loss Report, in which I analyzed the economic status of the company from the point of view of Revenues and Expenditures. In this report page I compared the trend of actual amount and budget amount, unfortunately for 2011 year only (missing data for other years). You can notice the powerful feature of “report page tooltip” in this page, by hovering over the bar charts. - Internet Sales Overview: in this report page I analyzed the sales of products to customers via Internet. You can notice the forecasting feature of Power BI and the use of what-if parameters in order to build advanced analytics reports. Also I made use of calculated groups (for further info about visit https://www.sqlbi.com/articles/introducing-calculation-groups/). - Reseller Sales Overview: similar to the previous one, but this time the focus is on the sales of products to resellers. Things to notice in this report page are the comparison of current sales vs last year sales, the comparison of actual sales amount vs budget/quota sales amount, and the running total and moving/running average charts. - Product Inventory Overview: inventory management analysis of the company. I analyzed the current stock on hold vs the recent sales revenue, showing the value of current stock, the current units in stock and the stock ratio. - Products Overview: in this report page I analyzed the products by category, color, size, “ABC” class too. Two very interesting and powerful things to notice: the basket analysis using DAX and the basket/association rules analysis using R script and apriori machine learning model. I made use of the custom chart called “Network Chart” to represent the association rules between products. Through the basket analysis the user could know which product is likely to be bought with another one. - Customers Overview: customer analysis by age group, location, status, and so on. The customer could be classified as “Bike Buyer” and “Non Bike Buyer”. So I decided to build a machine learning model (in this case I used XGBoost algorithm in Python) in order to predict if a (new) customer would be a bike buyer or not. The model has a prediction accuracy of about 84% which it is not bad. - Employees Overview: employee analysis by sales location, title, age. Thing to notice is the ribbon chart through which I can analyze the performance of employees over time. - Reseller Overview: analysis of the performance of the resellers. Important dimensions to analyze are the business type, the product line, the reseller location.

This project is a Sales Data Analysis Dashboard built using Power BI, leveraging data from the AdventureWorks 2022 Sales Data Mart. It provides key business insights, including total sales, customer segmentation, top-selling products. The dashboard was developed using DAX and Power Query to ensure accurate and efficient data visualization.

Business Intelligence / Sales & Performance Analysis (AdventureWorks)

SQL sales analysis project using the AdventureWorks 2017 dataset, focusing on business performance and profitability.

AdventureWorks sales analysis project using Power BI and SQL to deliver interactive dashboards and actionable business insights.

AdventureWorks Sales Analysis project showcasing data cleaning, transformation, and Power BI reporting. Includes insights on product performance, customer trends, profit margins, and regional sales to inform business decisions.

This project showcases a comprehensive analysis of AdventureWorks business data using Power BI. The dashboard provides actionable insights on key performance areas, including overall business performance, sales and product trends, customer behavior, and regional sales.

AdventureWorks OLTP is a sample database by Microsoft, simulating a retail business for bicycles and accessories. It includes tables for sales, products, customers, suppliers, and HR, ideal for SQL practice in transactional data management and business analysis.

This repository contains a complete data analysis project based on the AdventureWorks sample dataset. The project focuses on transforming raw business data into meaningful insights using SQL, Excel, and Power BI and Tableau.

SQL-driven analysis of sales, customer behavior, time trends and regional performance using the AdventureWorks dataset. Built using Google BigQuery and SQL to uncover key business insights. Data is structured through clean queries and views designed to support product, customer and geographic decisions.

Exploring a series of SQL queries analyzing the AdventureWorks database, providing valuable insights for strategic decision-making in areas such as product selection, pricing strategies, employee demographics, and sales performance. Each query is accompanied by detailed analysis, offering actionable perspectives to enhance business performance.

A complete data analysis project implementing the 3-Tier architecture using the AdventureWorks dataset. The project includes data warehousing, ETL processes, and advanced visualization to deliver actionable business insights. Designed to support strategic decision-making through an executive report and interactive dashboard.

This notebook implements a Silver-layer ETL pipeline for the AdventureWorks sample data, using PySpark on Azure Data Lake Storage (ADLS Gen2). It reads raw CSV files from the Bronze zone, applies business-level transformations to produce clean Parquet tables in the Silver zone, and concludes with a basic sales analysis.

Turning Data into Business Insights | AdventureWorks Analysis

A Power BI analysis of AdventureWorks' business performance.

AdventureWorks Data Analysis and Report Creation for Business Intelligence

This repository contains business analysis projects for the AdventureWorks company, completed as part of a Coursera Business Analyst course by Microsoft. Key analyses include sales trends, profit margins, and cost breakdowns using Excel. The project highlights data-driven insights and decision-making skills.

SQL-driven analysis of AdventureWorks data to uncover business insights

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A SQL-based analytics project built on the AdventureWorksDW data warehouse.

This repository features a business intelligence analysis of AdventureWorks Cycles using Power BI. It includes interactive dashboards and reports for KPIs, regional performance, product trends, and customer analysis. Explore to see how data-driven insights drive strategic decisions.

No description available

No description available

The AdventureWorks Power BI project is a comprehensive sales and business analysis

Financial KPI modeling and business performance analysis using Power BI (AdventureWorks dataset).

Interactive Tableau Story built using the AdventureWorks 2022 dataset for business performance analysis.

End-to-end business performance analysis using Excel, SQL and Power BI on the AdventureWorks dataset, featuring sales, profitability, product insights, and regional performance dashboards.

Business intelligence dashboards for AdventureWorks data: Sales, Product, and Customer analysis in Power BI.

General Data Analysis Approach 📊 Exploratory Data Analysis (EDA) & Insights on AdventureWorks 2019 using Excel & SQL & Python. 🔍 Uncovering business insights from AdventureWorks 2019 through SQL queries & data visualization.

Business Analysis of a fictitious cycle company "AdventureWorks Cycles" and created Interactive Dashboards using Power BI