This dataset is a corrected and enhanced version of the original Car Sales Analysis Dashboard Dataset by Safa Eahb. The original data contained a few inconsistencies which have been fixed in this version for better analysis. Acknowledgements Original dataset by Safa Eahb

Cleaned and analyzed by [https://www.kaggle.com/abhinavgaur07]

Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively, add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

🚖 Uber Data Analysis Dashboard (Power BI)

This dataset is part of a dashboard project that analyzes Uber ride behavior across different time patterns – built using Microsoft Power BI.

🔍 Project Highlights:

• Analyze ride volumes across hours, days, and months
• See peak times and hotspots visually
• Interactive visuals built in Power BI
• Cleaned and prepared Excel dataset also provided

📂 Files:

• Uber Trip Details.xlsx – Cleaned dataset
• Uber.pbix – Power BI Dashboard file

🌐 Related Links:

• 🔗 GitHub Project: https://github.com/ssrAiLab/Uber-PowerBI-Project.git
• 🔗 LinkedIn Post: (to be added after posting)

Feel free to fork, reuse, or share feedback!

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, I had to ask questions that could help extract and explore information that would help decision-makers improve and evaluate performance. But before that, I did some operations in the data to help me to analyze it accurately: 1- Understand the data. 2- Clean the data “By power query”. 3- insert some calculation and columns like “COGS” cost of goods sold by power query. 4- Modeling the data and adding some measures and other columns to help me in analysis. Then I asked these questions: To Enhance Customer Loyalty What is the most used ship mode by our customer? Who are our top 5 customers in terms of sales and order frequency? To monitor our strength and weak points Which segment of clients generates the most sales? Which city has the most sales value? Which state generates the most sales value? Performance measurement What are the top performing product categories in terms of sales and profit? What is the most profitable product that we sell? What is the lowest profitable product that we sell? Customer Experience On Average how long does it take the orders to reach our clients? Based on each Shipping Mode

Then started extracting her summaries and answers from the pivot tables and designing the data graphics in a dashboard for easy communication and reading of the information as well. And after completing these operations, I made some calculations related to the KPI to calculate the extent to which sales officials achieved and the extent to which they achieved the target.

Leveraging Star and Snowflake schemas, I build a comprehensive data model in Power BI, to enhance analytical efficiency and support accurate reporting.

I started with a Star schema which contains a Sales fact table and six-dimensional tables as a solid approach for quick, accessible insights and simplified data relationships.

Afterward, I added a second Star schema, based on a Budget fact table and two-dimensional tables, offering further segmentation and cross-referencing capabilities between sales and budgeting.

Finally, I combined the two schemas into a snowflake structure for optimizing data queries and performance, especially as relationships grow more complex across dimensions.

Adventure Works 2022 dataset

How this Dataset is created?

On the official website the dataset is available over SQL server (localhost) and CSVs to be used via Power BI Desktop running on Virtual Lab (Virtaul Machine). As per first two steps of Importing data are executed in the virtual lab and then resultant Power BI tables are copied in CSVs. Added records till year 2022 as required.

How this Dataset may help you?

this dataset will be helpful in case you want to work offline with Adventure Works data in Power BI desktop in order to carry lab instructions as per training material on official website. The dataset is useful in case you want to work on Power BI desktop Sales Analysis example from Microsoft website PL 300 learning.

How to use this Dataset?

Download the CSV file(s) and import in Power BI desktop as tables. The CSVs are named as tables created after first two steps of importing data as mentioned in the PL-300 Microsoft Power BI Data Analyst exam lab.

Utility GIS Data Quality Services Market Outlook

According to our latest research, the global Utility GIS Data Quality Services market size reached USD 1.37 billion in 2024 and is projected to grow at a robust CAGR of 12.8% from 2025 to 2033, reaching an estimated USD 4.08 billion by 2033. The primary growth factor driving this market is the increasing demand for accurate, real-time geospatial data to optimize utility operations and comply with stringent regulatory requirements. The surge in smart grid deployments and digital transformation initiatives across the utility sector is significantly boosting the adoption of specialized GIS data quality services.

One of the core growth drivers for the Utility GIS Data Quality Services market is the accelerating shift toward digital infrastructure in the utilities sector. Utilities, including electric, water, and gas providers, are increasingly relying on Geographic Information Systems (GIS) for asset management, network optimization, and outage management. However, the effectiveness of these systems is heavily dependent on the accuracy and integrity of the underlying data. As utilities modernize their grids and expand their service offerings, the need for comprehensive data cleansing, validation, and enrichment becomes paramount. This trend is further amplified by the proliferation of IoT devices and smart meters, which generate vast volumes of spatial and operational data, necessitating advanced GIS data quality services to ensure consistency and reliability across platforms.

Another significant factor propelling market growth is the evolving regulatory landscape. Governments and regulatory bodies worldwide are imposing stricter requirements on utilities to maintain high-quality, up-to-date geospatial records for compliance, safety, and disaster response. Inaccurate or outdated GIS data can lead to costly penalties, service interruptions, and reputational damage. As a result, utility companies are investing heavily in data quality services to achieve regulatory compliance and mitigate operational risks. The integration of artificial intelligence and machine learning technologies into GIS data quality processes is also enhancing the efficiency and accuracy of data validation, migration, and integration, further supporting market expansion.

Moreover, the increasing complexity of utility networks and the growing emphasis on sustainability and resilience are driving utilities to adopt advanced GIS data quality services. Utilities are under pressure to optimize resource allocation, minimize losses, and enhance customer service, all of which require high-quality geospatial data. The rise of distributed energy resources, such as solar and wind, and the need to manage bi-directional power flows are adding new layers of complexity to utility networks. GIS data quality services enable utilities to maintain a comprehensive, accurate digital twin of their infrastructure, supporting better planning, predictive maintenance, and rapid response to outages or emergencies. These factors collectively contribute to the sustained growth of the Utility GIS Data Quality Services market.

From a regional perspective, North America currently dominates the Utility GIS Data Quality Services market, driven by large-scale investments in smart grid projects and the presence of major utility companies adopting advanced GIS solutions. However, Asia Pacific is expected to witness the fastest growth over the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives to modernize utility networks. Europe also presents significant opportunities, with increasing focus on sustainability, regulatory compliance, and cross-border energy integration. The Middle East & Africa and Latin America are gradually catching up, with investments in utility infrastructure and digital transformation initiatives gaining momentum. Overall, the global market is poised for substantial growth, underpinned by technological advancements, regulatory mandates, and the evolving needs of the utility sector.

Service Type Analysis

The Utility GIS Data Quality Services market is segmented by service type into data cleansing, data validation, data integration, data migration, data enrichment, and others. Data cleansing services form the backbone of this segment, as they address the critical need to remove inaccuracies, inconsistencies, and redundancies from utility GIS databases. Wit

What is IPGOD?

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.

How do I use IPGOD?

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.

IP Data Platform

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform

References

The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.

• Patents
• Trade Marks
• Designs
• Plant Breeder’s Rights

Updates

Tables and columns

Due to the changes in our systems, some tables have been affected.

• We have added IPGOD 225 and IPGOD 325 to the dataset!
• The IPGOD 206 table is not available this year.
• Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.

Data quality improvements

Data quality has been improved across all tables.

• Null values are simply empty rather than '31/12/9999'.
• All date columns are now in ISO format 'yyyy-mm-dd'.
• All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.
• All tables are encoded in UTF-8.
• All tables use the backslash \ as the escape character.
• The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Data was imported from the BAK file found here into SQL Server, and then individual tables were exported as CSV. Jupyter Notebook containing the code used to clean the data can be found here

Version 6 has a some more cleaning and structuring that was noticed after importing in Power BI. Changes were made by adding code in python notebook to export new cleaned dataset, such as adding MonthNumber for sorting by month number, similar for WeekDayNumber.

Cleaning was done in python while also using SQL Server to quickly find things. Headers were added separately, ensuring no data loss.Data was cleaned for NaN, garbage values and other columns.

What is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD. # How do I use IPGOD? IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar. # IP Data Platform IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform # References The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset. * Patents * Trade Marks * Designs * Plant Breeder’s Rights # Updates ### Tables and columns Due to the changes in our systems, some tables have been affected. * We have added IPGOD 225 and IPGOD 325 to the dataset! * The IPGOD 206 table is not available this year. * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use. ### Data quality improvements Data quality has been improved across all tables. * Null values are simply empty rather than '31/12/9999'. * All date columns are now in ISO format 'yyyy-mm-dd'. * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0. * All tables are encoded in UTF-8. * All tables use the backslash \ as the escape character. * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Burundi BI: GNI: PPP data was reported at 12,620.674 Intl $ mn in 2023. This records an increase from the previous number of 11,885.620 Intl $ mn for 2022. Burundi BI: GNI: PPP data is updated yearly, averaging 4,674.902 Intl $ mn from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 12,620.674 Intl $ mn in 2023 and a record low of 3,071.322 Intl $ mn in 1996. Burundi BI: GNI: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides values for gross national income (GNI. Formerly GNP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. Gross national income is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries. From July 2020, “GNI: linked series (current LCU)” [NY.GNP.MKTP.CN.AD] is used as underlying GNI in local currency unit so that it’s in line with time series of PPP conversion factors, which are extrapolated with linked deflators.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Gap-filled total;

Burundi BI: GNI: PPP: GNI per Capita data was reported at 920.000 Intl $ in 2023. This records an increase from the previous number of 890.000 Intl $ for 2022. Burundi BI: GNI: PPP: GNI per Capita data is updated yearly, averaging 610.000 Intl $ from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 920.000 Intl $ in 2023 and a record low of 510.000 Intl $ in 1997. Burundi BI: GNI: PPP: GNI per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides per capita values for gross national income (GNI. Formerly GNP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Weighted average;

Exploring Online Sales Data with Power BI !!

Another productive day diving into online sales dataset! Here’s a roundup of the insights I uncovered today:

Revenue by Category: Analyzed revenue distribution across different product categories to identify high-performing sectors.

Revenue by Sub-Category: Drilled down into sub-categories for a more granular view of revenue streams.

Revenue by Payment Mode: Examined revenue patterns based on payment methods to understand customer preferences.

Revenue by State: Mapped out revenue by state to pinpoint geographical strengths and opportunities.

Profit by Category: Evaluated profitability across product categories to assess which categories yield the highest profit margins.

Profit by Sub-Category: Explored profit levels at a sub-category level to identify the most profitable segments.

Profit by Payment Mode: Analyzed profit distribution across different payment methods.

Top 5 States by Revenue and Profit: Highlighted the top 5 states driving the most revenue and profit, offering insights into regional performance.

Sales Map by State: Visualized sales data on a map to provide a geographical perspective on sales distribution.

Total Quantity, Revenue, and Profit: Aggregated data to give an overview of total quantities sold, overall revenue, and total profit.

Filter by Category: Added a filter functionality to focus on specific categories and refine data analysis.

Burundi BI: GDP: PPP: 2011 Price:(GDP) Gross Domestic Productper Capita data was reported at 660.330 Intl $ in 2018. This records a decrease from the previous number of 670.777 Intl $ for 2017. Burundi BI: GDP: PPP: 2011 Price:(GDP) Gross Domestic Productper Capita data is updated yearly, averaging 728.186 Intl $ from Dec 1990 (Median) to 2018, with 29 observations. The data reached an all-time high of 1,054.316 Intl $ in 1991 and a record low of 660.330 Intl $ in 2018. Burundi BI: GDP: PPP: 2011 Price:(GDP) Gross Domestic Productper Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2011 international dollars.; ; World Bank, International Comparison Program database.; Weighted Average;

Burundi BI: GNI: PPP: 2017 Price data was reported at 9,173.716 Intl $ mn in 2022. This records an increase from the previous number of 8,992.829 Intl $ mn for 2021. Burundi BI: GNI: PPP: 2017 Price data is updated yearly, averaging 7,107.892 Intl $ mn from Dec 1997 (Median) to 2022, with 26 observations. The data reached an all-time high of 9,173.716 Intl $ mn in 2022 and a record low of 5,073.666 Intl $ mn in 1997. Burundi BI: GNI: PPP: 2017 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. PPP GNI (formerly PPP GNP) is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. Gross national income is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant 2017 international dollars.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Gap-filled total;

Burundi BI: GNI per Capita: PPP: 2017 Price data was reported at 711.716 Intl $ in 2022. This records a decrease from the previous number of 716.491 Intl $ for 2021. Burundi BI: GNI per Capita: PPP: 2017 Price data is updated yearly, averaging 800.582 Intl $ from Dec 1997 (Median) to 2022, with 26 observations. The data reached an all-time high of 883.901 Intl $ in 1998 and a record low of 711.716 Intl $ in 2022. Burundi BI: GNI per Capita: PPP: 2017 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in constant 2017 international dollars.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Weighted average;

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, I had to ask questions that could help extract and explore information that would help decision-makers improve and evaluate performance. But before that, I did some operations in the data to help me to analyze it accurately: 1- Understand the data. 2- Clean the data “By power query”. 3- insert some calculation and columns by power query. 4- Analysis to the data and Ask some Questions About Distribution What is the Number of Bikes Sold? What is the most region purchasing bikes? What is the Ave. income by gender & purchasing bikes? The Miles with Purchasing bikes? What is situation to age by purchasing & Count of bikes sold? About Consumer Behavior Home Owner by purchasing? Single or married & Age by purchasing? Having cars by purchasing? Education By purchasing? Occupation By purchasing?

And I notice the Most Situations Purchasing Bikes is: - North America “Region”. - Commute Distance 0-1 Miles. - The people who are in the middle age and single "169 Bikes". - People that having Bachelor's degree. - The Males who have the average income 60,124$. - People that having Professional occupation. - Home owners “325 Bikes”. - People who having 0 or 1 car. So, I Advise The give those slices more offers to increase the sell value.

Within this, I have added 2 datasets for Book Market Sales Data. These are helpful to generate dashboards on Power BI. Vente.xlsx contains : Ticket number (primary key), Date, Product ID , Market ID , Quantity , Turnover rate whereas Produit.xlsx contains: product ID , author name , gender, age and product price with the bestsellers Magasin.xlsx contains department details with name , id and population Date.xlsx with details about the year and days of publish .

🚀 Excited to Share My Latest Project! 🚀

I recently completed a comprehensive Power BI dashboard focused on analyzing the revenue and growth performance of top companies across various industries. This project was a fantastic opportunity to dive deep into data visualization and business intelligence, turning complex datasets into actionable insights.

Key Highlights: 📊 Revenue Analysis: Visualized the total revenue distribution across different industries and headquarters. 📈 Growth Insights: Analyzed revenue growth trends to highlight top performers and growth opportunities. 🏆 Company Ranking: Ranked companies based on revenue to identify market leaders. 🎯 Interactive Filters: Enabled dynamic data exploration with industry-specific filters. 💼 Key Metrics: Displayed essential KPIs like total revenue, average revenue growth, and employee count. 🎨 User-Friendly Design: Focused on creating a visually appealing and functionally effective dashboard layout.

This project has further honed my skills in Power BI, data visualization, and business intelligence, and I’m thrilled to add it to my portfolio.

This Datasets contains fashion retail products data, including sales, promotional, category, and ratings. The attached present power BI key business insights and recommendations using interactive dashboard.

Dataset Overview: * Rows 226 fashion projects entries * Columns include: 1. product ID 2.promotion 3.Product Category 4.Seasonal 5.Sales volume 6.brand 7.urf 8.sku 9.name 10.description 11.Final price 12.Currency 13.scraped Date 14.terms 15.section

• ETL in Power BI:
• Removed extra Columns (URL, SKU if duplicate)
• Replaced nulls in Name, Description with "not Available"

• Converted scraped_at to only to Date format

• Dashboard Visuals:

• KPI Cards 2.Total Products

• % of Promotion

• Avg Price

• Total Revenue

• Bar charts: 1.Top 10 Product Share

• Pie charts: 1.Seasonal product share 2.promo vs Non-promo Count

• Table: 1.Detailed product list

• Key Insights: 1.promo products 50% high revenue compared to non-promo 2.Shoes and Jackets are the most Common product categories 3.Higher rated items are more likely to be discounted 4.Men product almost given a high profit of the sales and revenue 5.Product position probably Aisle higher than other

• Recommendations: 1.Increase promotions for lower rated or low selling categories 2.ncrease woman sector need to improve products in overall 3.Add give more promotion to get a high visual to visit customer 4.More focus on high level product type (Eg: jacket and shoes) are major profit pf sales

Conclusion: This Power BI project Demonstrates how visual analysis can turn retail data into actional strategy for Zara. The Dashboard helps management understand what's selling, when, and why.

File & Resources: Power BI .pbix file Uploaded Dataset(csv file) Screenshot of Visual attached

Feedback Welcome: if you liked this project, feel free to: Upvote Comment with suggestions