1) Data Introduction • The Power BI Sample Data is a financial sample dataset provided for Power BI practice and data visualization exercises that includes a variety of financial metrics and transaction information, including sales, profits, and expenses.

2) Data Utilization (1) Power BI Sample Data has characteristics that: • This dataset consists of numerical and categorical variables such as transaction date, region, product category, sales, profit, and cost, optimized for aggregation, analysis, and visualization. (2) Power BI Sample Data can be used to: • Revenue and Revenue Analysis: Analyze sales and profit data by region, product, and period to understand business performance and trends. • Power BI Dashboard Practice: Utilize a variety of financial metrics and transaction data to design and practice dashboards, reports, visualization charts, and more directly at Power BI.

A dataset I generated to showcase a sample set of user data for a fictional streaming service. This data is great for practicing SQL, Excel, Tableau, or Power BI.

1000 rows and 25 columns of connected data.

See below for column descriptions.

Enjoy :)

🔍 Total Sales: Achieved $456,000 in revenue across 1,000 transactions, with an average transaction value of $456.00.

👥 Customer Demographics:

Average Age: 41.39 years Gender Distribution: 51% male, 49% female Most active age groups: 31-40 & 41-50 years 🏷️ Product Performance:

Top Categories: Electronics and Clothing led the sales, each contributing $160,000, followed by Beauty products with $140,000. Quantity Sold: Clothing topped the charts with 894 units sold. 📈 Sales Trends: Identified key sales peaks, especially in May 2023, indicating the success of targeted promotional strategies.

Why This Matters:

Understanding these metrics allows for better-targeted marketing, efficient inventory management, and strategic planning to capitalize on peak sales periods. This project demonstrates the power of data-driven decision-making in retail!

💡 Takeaway: Power BI continues to be a game-changer in visualizing and interpreting complex data, helping businesses to not just see numbers but to translate them into actionable insights.

I’m always looking forward to new challenges and projects that push my skills further. If you're interested in diving into the details or discussing data insights, feel free to reach out!

PowerBI #DataAnalysis #RetailSales #DataVisualization #BusinessIntelligence #DataDriven

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

This is a Powerbi Dashboard of sample Superstore with .PBIX file. and below link is for show the preview of dashboard in MS Powerpoint -----> https://docs.google.com/presentation/d/1Z7tjJchygz1WKDQ0vIKeQAhS6t-wgjZR/edit?usp=drive_link&ouid=100074103835193394792&rtpof=true&sd=true

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.

This dataset is based on the Superstore Sales data from Kaggle, containing global order records from 2015 to 2018. It includes detailed information such as order dates, sales revenue, profit, shipping modes, product categories, customer segments, and regional distribution.

The data serves as the foundation for a Power BI dashboard designed to extract actionable business insights. It is ideal for exploring trends in sales performance, market opportunities, and operational efficiency.

Global Power BI Consulting Service market size 2025 was XX Million. Power BI Consulting Service Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Comprehensive dataset containing 1,454 verified Power station businesses in Indonesia with complete contact information, ratings, reviews, and location data.

Domain-Specific Dataset and Visualization Guide

This package contains 20 realistic datasets in CSV format across different industries, along with 20 text files suggesting visualization ideas. Each dataset includes about 300 rows of synthetic but domain-appropriate data. They are designed for data analysis, visualization practice, machine learning projects, and dashboard building.

What’s inside

• 20 CSV files, one for each domain:

  1. Education
  2. E-Commerce
  3. Healthcare
  4. Finance
  5. Retail
  6. Social Media
  7. Manufacturing
  8. Sports
  9. Transport
  10. Hospitality
  11. Telecom
  12. Banking
  13. Real Estate
  14. Gaming
  15. Agriculture
  16. Automobile
  17. Energy
  18. Insurance
  19. Government
  20. Entertainment

20 TXT files, each listing 10 relevant graphing options for the dataset.

MASTER_INDEX.csv, which summarizes all domains with their column names.

Use cases

• Practice data cleaning, exploration, and visualization in Excel, Tableau, Power BI, or Python.
• Build dashboards for specific industries.
• Train beginner-level machine learning models such as classification and regression.
• Use in classroom teaching or workshops as ready-made datasets.

Example

• Education dataset has columns like StudentName, Class, Subject, Marks, AttendancePercent. Suggested graphs: bar chart of average marks by subject, scatter plot of marks vs attendance percent, line chart of attendance over time.

• E-Commerce dataset has columns like OrderDate, Product, Category, Price, Quantity, Total. Suggested graphs: line chart of revenue trend, bar chart of revenue by category, pie chart of payment mode share.

Anonymized data provided by ObviEnce, LLC. Visit their site to learn about their services: www.obvience.com. This data is the property of ObviEnce, LLC and has been shared to demonstrate Power BI functionality with industry sample data. Any use of this data must include this attribution to ObviEnce, LLC.

The Archive''s soil specimens are invaluable time capsules for assessing temporal changes in soil properties. Physical samples are a basic element for reference, study, and experimentation in research. There is an urgent need for better integrating these physical objects into the digital research data ecosystem, both in a global and in an interdisciplinary context to support scientific reuse. The CREA collection, located at the Experimental Farm of Fagna, Scarperia (FI), stores specimens and associated metadata. It covers all major agricultural and forestry soil landscapes in Italy for organic and mineral horizons. Parameters include water impedance, rooting depth, stoniness, Coarse fraction, particle size, pH, organic carbon, and total carbonates, World Reference Base classification. Part of collected samples was recently received and is temporarily stored unordered. With the present work, a tool was developed to expose both metadata, digital research data, displacement to support FAIR principles. The tool was developed by means of Ms Power BI. The original local Ms Access database was stored on the cloud and connected to the tool to allow automatic updates. Geographic and semantic queries are graphically implemented through drop-down menus and pie charts on administrative units- Soil districts- European Environments- Land use- WRB- and Project. The tool expose data collected by 13 different projects from 1986 to 2017. Contains 13,231 analyzed observations (pedological profiles, minipits, or augerings) for a total of 33,523 samples. Soil properties resulted in ranging for Clay 0.1-93.5 (29,9 average)- Sand 0.0-99.4 (17.9), pH (water) 3.9-9.7 (7.5) - Organic carbon 0.0-53.4 (7.8) - Total carbonates 0.0-91.4 (5.5) for the whole dataset. Textural composition of every Reference Soil Group (24 out of 32) is presented as Bar Histogram. A navigation panel allows to preview the site location and storing collocation. Although samples access is restricted, data and storing displacement are exposed to support use of the data and specimen''s reuse. The developed tool represents a first attempt to expose both metadata, soil data and filtering capabilities.

Context This dataset shows real estate listing in USA. It includes the price, zip codes etc

Sources This shows real estate data of company called Realtor - https://www.realtor.com. I downloaded the dataset from kaggle.

About Dataset 1 csv. file contains 10 columns - realtor-data.csv (100k+ entries) - status (Housing status - a. ready for sale or b. ready to build) - bed (# of beds) - bath (# of bathrooms) - acre_lot (Property / Land size in acres) - city (city name) - state (state name) - zip_code (postal code of the area) - house_size (house area/size/living space in square feet) - prev_sold_date (Previously sold date) - price (Housing price, it is either the current listing price or recently sold price if the house is sold recently)

Cover Image Downloaded from Google Stock images.

Disclaimer The data and information in the data set provided here are intended to use for educational purposes only. I do not own any data, and all rights are reserved to the respective owners.

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

Harness AI-Driven Precision for Global Company Insights Leverage cutting-edge AI agents to fetch and validate company registry data in real-time, bypassing obsolete databases. Unlike traditional providers, our service dynamically retrieves data directly from government registries worldwide, ensuring up-to-the-minute accuracy and eliminating outdated records.

Key Features 1. AI-Powered Real-Time Access: Deploy autonomous AI agents to collect and structure data from any national registry, even those with dynamic layouts or authentication barriers.

1. Universal Registry Compatibility: Seamlessly extract data from 250+ countries, including hard-to-access regions, with automatic translation and normalization.

2. Document Processing: Parse financial filings, annual reports, and legal documents (PDF, DOCX) using NLP-driven analysis. Extract key attributes like ownership structures, director details, and compliance status.

3. Format Flexibility: Receive data via API, CSV, JSON, or custom formats (e.g., PostgreSQL DB, Google Sheets) with hourly/daily refresh options.

4. 99% Accuracy Guarantee: Multi-layer validation via AI cross-referencing and human audits ensures error-free datasets.

Data Sourcing & Coverage 1. Sources: Direct integration with 1,800+ government registries of your choice on demand, supplemented by AI-enhanced verification of public filings and regulatory submissions.

1. Attributes: Company name, registration number, directors, shareholders, financials, litigation history, and industry-specific certifications (e.g., ISO, NAICS).

2. Historical Data: 10+ years of archived records, updated in real-time.

Use Cases 1. Due Diligence: Verify company legitimacy for mergers, acquisitions, or partnerships.

1. Compliance: Streamline KYC/AML workflows with automated registry checks.

2. Market Research: Track competitor expansions, ownership changes, or industry trends.

3. Risk Management: Monitor regulatory violations or financial instability signals.

4. Credit Reporting: Automate end-to-end credit report creation process.

Technical Specifications 1. Delivery: API (REST/GraphQL), SFTP, cloud sync (AWS S3, Google Cloud).

1. Integration: Custom connectors for Salesforce, HubSpot, and BI tools (Tableau, Power BI).

2. Latency: Sub-5-second to 60 mins response time for on-demand queries based on the complexity and response time of registry.

Why Choose Us? 1. Pioneers in AI Agent Technology: Outperform static datasets with live registry scraping.

1. GDPR/CCPA Compliance: Data sourced ethically from public registries, with audit trails on output.

2. Free Sample: Test 100 records at zero cost.

Snapshot img

Enterprise Data Warehouse (EDW) Market Size 2025-2029

The enterprise data warehouse (edw) market size is valued to increase USD 43.12 billion, at a CAGR of 28% from 2024 to 2029. Data explosion across industries will drive the enterprise data warehouse (edw) market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 32% growth during the forecast period.
By Product Type - Information and analytical processing segment was valued at USD 4.38 billion in 2023
By Deployment - Cloud based segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 857.82 million
Market Future Opportunities: USD 43116.60 million
CAGR : 28%
APAC: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving landscape, characterized by continuous innovation and adaptation to industry demands. Core technologies, such as cloud computing and big data analytics, are driving the market's growth, enabling organizations to manage and analyze vast amounts of data more effectively. In terms of applications, business intelligence and data mining are leading the way, providing valuable insights for strategic decision-making. Service types, including consulting, implementation, and support, are essential components of the EDW market. According to recent reports, the consulting segment is expected to dominate the market due to the increasing demand for expert advice in implementing and optimizing EDW solutions. However, data security concerns remain a significant challenge, with regulations like GDPR and HIPAA driving the need for robust security measures. Despite these challenges, the market continues to expand, with data explosion across industries fueling the demand for EDW solutions. For instance, the healthcare sector is projected to witness a compound annual growth rate (CAGR) of 15.3% between 2021 and 2028. Furthermore, the market is witnessing a significant focus on new solution launches, with major players like Microsoft, IBM, and Oracle introducing advanced EDW offerings to meet the evolving needs of businesses.

What will be the Size of the Enterprise Data Warehouse (EDW) Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Enterprise Data Warehouse (EDW) Market Segmented and what are the key trends of market segmentation?

The enterprise data warehouse (edw) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Product TypeInformation and analytical processingData miningDeploymentCloud basedOn-premisesSectorLarge enterprisesSMEsEnd-userBFSIHealthcare and pharmaceuticalsRetail and E-commerceTelecom and ITOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Product Type Insights

The information and analytical processing segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with data replication strategies becoming increasingly sophisticated to ensure capacity planning models accommodate expanding data volumes. ETL tool selection and business intelligence platforms are crucial components, enabling query optimization strategies and disaster recovery planning. Data warehouse migration, data profiling methods, and real-time data ingestion are essential for maintaining a competitive edge. Data warehouse automation, data quality metrics, and data warehouse modernization are ongoing priorities, with data cleansing techniques and dimensional modeling techniques essential for ensuring data accuracy. Data warehousing architecture, performance monitoring tools, and high availability solutions are integral to ensuring scalability and availability. Audit trail management, data lineage tracking, and data warehouse maintenance are critical for maintaining data security and compliance. Data security protocols and data encryption methods are essential for protecting sensitive information, while data virtualization techniques and access control mechanisms facilitate self-service business intelligence tools. ETL process optimization and data governance policies are key to streamlining operations and ensuring data consistency. The IT, BFSI, education, healthcare, and retail sectors are driving market growth, with information processing and analytical processing becoming increasingly important. The construction of web-based accessing tools integrated with web browsers is a current trend, enabling users to access data warehouses easily. According to recent studies, the market for data warehousing solutions is projected to grow by 18.5%, while the adoption of cloud data warehou

Veridion’s technographic dataset delivers deterministic, verified insights into the technology stacks that power a company’s digital presence. This is not modeled or probabilistic data — it is extracted from first-party, real-world digital signals sourced from company websites, social media, press releases, and other online assets. The technographic layer is part of Veridion’s broader company profile, which also includes firmographics, business activities, products & services, ESG attributes, ownership structures, and location data. When combined, these layers allow clients to gain a deep, multi-dimensional understanding of both public and private companies across 245+ countries. At the core, technographic data answers critical questions such as: - What software, platforms, and tools does this company use to operate online? - Which content management systems (CMS), analytics tools, marketing automation platforms, payment gateways, or hosting services are in place? - What industry-specific applications or integrations signal the company’s operational maturity or market positioning? - How does the company’s tech adoption compare to competitors or peers in its sector?

Data Sources & Collection Methodology Veridion’s approach to technographic intelligence is built for scale, frequency, and accuracy: - First-Party Digital Footprint Analysis – Veridion’s crawlers scan billions of web pages weekly, capturing up-to-date, verifiable signals from a company’s active online properties. - Multi-Source Validation – Detected technologies are cross-referenced with multiple independent sources, including metadata in site code, integrations disclosed in press releases, and verified vendor references. - Granular Taxonomy – Technologies are classified into structured categories for easy integration with customer workflows — for example: ◦ Web Hosting & Infrastructure (e.g., AWS, Azure, Google Cloud) ◦ Web Development Frameworks (e.g., React, Angular, Vue.js) ◦ Content Management Systems (e.g., WordPress, Shopify, Drupal) ◦ Analytics & BI Tools (e.g., Google Analytics, Mixpanel, Power BI) ◦ Marketing Automation & CRM (e.g., HubSpot, Salesforce, Marketo) ◦ Payment Gateways & E-commerce Platforms (e.g., Stripe, Magento) ◦ Industry-Specific Tools (e.g., hotel booking engines, telehealth platforms) - Weekly Updates: Because digital tech stacks evolve quickly, Veridion refreshes profiles weekly to detect changes, new adoptions, or deprecations. This ensures technographic data reflects the current state, not stale historical footprints.

Core Features - Deterministic Detection – Identified technologies are based on confirmed signals, not statistical guesses. - Global Scale – Coverage of 130M+ operating companies in over 245 countries, including hard-to-find SMBs. - Granular Categorization – Technologies classified into operationally relevant groups to support segmentation and targeting. - Time-Series Tracking – Ability to see when a technology was first detected and track its lifecycle within the company profile. - Integrations Ready – Data is available via API, batch delivery, or through Veridion’s Data Discovery Platform for direct integration into CRM, MDM, ABM, or analytics tools.

Technographic Data Use Cases

1. Sales & Marketing Segmentation Challenge: Go-to-market teams often waste resources targeting broad, undifferentiated segments without knowing which prospects are actually a fit for their solution. Solution with Veridion: Filter and prioritize prospects based on the technologies they use, for example: • SaaS providers targeting companies that use complementary technologies (e.g., selling an SEO tool to companies already using HubSpot or WordPress). • Competitor displacement campaigns targeting companies running a rival product. • Market entry campaigns identifying verticals with high adoption of a given platform. Impact: Increased conversion rates, higher ROI on outbound campaigns, and reduced sales cycle length.

2. Competitive Intelligence Challenge: Companies lack visibility into competitors’ penetration across markets or accounts. Solution with Veridion: Build competitive landscapes by mapping where specific technologies are deployed. Track adoption trends over time to identify market share shifts or early signs of competitive threats.

3. Account-Based Marketing (ABM) Enrichment Challenge: ABM strategies rely on deep account intelligence, yet most CRM data is incomplete or outdated. Solution with Veridion: Enrich target accounts with verified technographics to personalize messaging, content, and offers.

4. Partner & Channel Ecosystem Mapping Challenge: Partner managers need to find integrators, agencies, and resellers that work with specific technologies. Solution with Veridion: Use technographic filters to identify potential partners already experienced in the target technology ecosystem.

5. Market Sizing & Opportunity Analysis Challenge: Pr...

Available data formats for the Analytical Courses Market Size, Share, Opportunities, And Trends By Age Group (Less than 20, 20-25, 25-30, More than 35), By Work Experience (Less than5 years, 5-10 years, 10-15 Years, Above 15 years), By Modules (Data Analytics and Intelligence, Trade Analytics, Big Data Analytics, Web & Social Media Analytics, Others), By Tools (Excel, Python, Power BI, Tableau, SQL, Alteryx, R, Others), And By Geography - Forecasts From 2025 To 2030 report.

Google Ads Sales Dataset for Data Analytics Campaigns (Raw & Uncleaned) 📝 Dataset Overview This dataset contains raw, uncleaned advertising data from a simulated Google Ads campaign promoting data analytics courses and services. It closely mimics what real digital marketers and analysts would encounter when working with exported campaign data — including typos, formatting issues, missing values, and inconsistencies.

It is ideal for practicing:

Data cleaning

Exploratory Data Analysis (EDA)

Marketing analytics

Campaign performance insights

Dashboard creation using tools like Excel, Python, or Power BI

📁 Columns in the Dataset Column Name ----- -Description Ad_ID --------Unique ID of the ad campaign Campaign_Name ------Name of the campaign (with typos and variations) Clicks --Number of clicks received Impressions --Number of ad impressions Cost --Total cost of the ad (in ₹ or $ format with missing values) Leads ---Number of leads generated Conversions ----Number of actual conversions (signups, sales, etc.) Conversion Rate ---Calculated conversion rate (Conversions ÷ Clicks) Sale_Amount ---Revenue generated from the conversions Ad_Date------ Date of the ad activity (in inconsistent formats like YYYY/MM/DD, DD-MM-YY) Location ------------City where the ad was served (includes spelling/case variations) Device------------ Device type (Mobile, Desktop, Tablet with mixed casing) Keyword ----------Keyword that triggered the ad (with typos)

⚠️ Data Quality Issues (Intentional) This dataset was intentionally left raw and uncleaned to reflect real-world messiness, such as:

Inconsistent date formats

Spelling errors (e.g., "analitics", "anaytics")

Duplicate rows

Mixed units and symbols in cost/revenue columns

Missing values

Irregular casing in categorical fields (e.g., "mobile", "Mobile", "MOBILE")

🎯 Use Cases Data cleaning exercises in Python (Pandas), R, Excel

Data preprocessing for machine learning

Campaign performance analysis

Conversion optimization tracking

Building dashboards in Power BI, Tableau, or Looker

💡 Sample Analysis Ideas Track campaign cost vs. return (ROI)

Analyze click-through rates (CTR) by device or location

Clean and standardize campaign names and keywords

Investigate keyword performance vs. conversions

🔖 Tags Digital Marketing · Google Ads · Marketing Analytics · Data Cleaning · Pandas Practice · Business Analytics · CRM Data

This Power BI dashboard provides a comprehensive view of the COVID-19 pandemic, leveraging data from worldometers coronavirus data.The dashboard offers interactive visualizations of key metrics like confirmed cases, deaths, recoveries, and vaccination rates. Users can explore trends over time, compare statistics across different countries, and filter data by specific regions or date ranges. This dashboard is a valuable tool for anyone interested in tracking the global COVID-19 situation, including researchers, policymakers, and the general public.