📊 Sales Data Analysis Using MySQL, Excel & Power BI 🔍 Project Overview This project focuses on analyzing sales data to extract valuable insights, identify trends, and support business decision-making. Using MySQL for querying, Excel for data manipulation, and Power BI for visualization, we explore key sales performance metrics.

🛠 Tools Used ✅ MySQL – Data storage, cleaning, and analysis using SQL queries. ✅ Excel – Data preprocessing, pivot tables, and basic visualization. ✅ Power BI – Interactive dashboards for advanced data visualization.

📂 Dataset Information Source: Kaggle Superstore Sales Dataset Data Size: 10,000+ records Key Features: Sales, Customer Details, Ship Mode, Product Category, Region

📌 Key Business Questions Answered 1️⃣ What are the top-performing sales regions? ✅ Used Power BI Map Visualization to analyze sales distribution by region. ✅ Key Insight: The highest sales were recorded in the West & East regions, while some regions showed potential for improvement.

2️⃣ Which product categories drive the highest revenue? ✅ Used Excel Pivot Tables to aggregate Sales by Category. ✅ Observation: "Technology" products had the highest sales, followed by "Furniture" and "Office Supplies."

3️⃣ Who are the top 10 customers by sales volume? ✅ Extracted top customers using SQL Queries & Power BI Ranking Functions. ✅ Business Insight: Retaining these customers can significantly boost revenue.

4️⃣ Which are the top 5 best-selling products? ✅ Aggregated product sales using MySQL SUM() function. ✅ Result: High-demand products identified, helping in inventory planning.

5️⃣ How does shipping mode affect sales? ✅ Created Power BI Slicer & Bar Chart for Ship Mode Analysis. ✅ Finding: Standard Class was the most used, while Same-Day shipping had lower but high-value orders.

📊 Power BI Dashboard Overview 🔹 Sales by Region – Geographical performance map 🔹 Top 10 Customers – Key customers contributing to revenue 🔹 Category & Sales – Identifying best-performing categories 🔹 Top 5 Products – Sales contribution by product 🔹 Shipping Mode Impact – Analyzing customer shipping preferences

📈 Business Insights & Recommendations 📌 Optimize Marketing Efforts – Focus more on high-performing regions. 📌 Inventory Management – Maintain high stock levels for top-selling products. 📌 Customer Retention Strategies – Prioritize personalized marketing for top customers. 📌 Improve Shipping Efficiency – Explore cost-effective shipping options for increased profitability.

📢 Why This Project? This project helped me strengthen my SQL querying skills, enhance Excel data manipulation, and build Power BI dashboards for professional data storytelling.

💡 Next Steps: Expanding analysis with predictive analytics & machine learning.

📎 Project Files & Resources 📂 Dataset – Available on Kaggle 📊 Power BI Dashboard – Shared in project files 📜 SQL Queries & Excel Reports – Available for reference

🚀 Let's Connect! 👨‍💻 LinkedIn – www.linkedin.com/in/ pooja-akash-lohkare-62a6a5b6

📧 Contact – poojacareer789@gmail.com

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Statistics Software Market Outlook

The global statistics software market size is projected to grow from USD 10.5 billion in 2023 to USD 18.7 billion by 2032, exhibiting a CAGR of 6.5% over the forecast period. The growth of this market is driven by the increasing adoption of data-driven decision-making processes across various industries, the rising need for statistical modeling and analysis tools, and the growing emphasis on advanced analytics to gain competitive advantages. Additionally, the expanding use of artificial intelligence (AI) and machine learning (ML) technologies to enhance the capabilities of statistics software is contributing significantly to market growth.

One of the primary growth factors of the statistics software market is the increasing reliance on data analytics and business intelligence tools across different sectors. Organizations are leveraging statistical software to analyze large volumes of data generated through various digital channels, enabling them to make informed decisions and identify new business opportunities. This trend is particularly evident in the healthcare, finance, and retail sectors, where data-driven insights are crucial for improving operational efficiency, customer satisfaction, and overall performance.

Another key driver for the market is the proliferation of big data and the need for advanced data management solutions. With the exponential growth of data generated by various sources such as social media, IoT devices, and enterprise systems, there is a heightened demand for robust statistical software that can handle complex data sets and perform sophisticated analyses. This has led to increased investments in the development of innovative statistics software solutions that offer enhanced features and capabilities, such as real-time data processing, predictive analytics, and automated reporting.

The integration of AI and ML technologies into statistics software is also significantly boosting market growth. These technologies enable more accurate and efficient data analysis, allowing organizations to uncover hidden patterns and trends that were previously impossible to detect. AI-powered statistical tools can automate repetitive tasks, reduce human error, and provide deeper insights into data, thereby enhancing the overall decision-making process. As a result, there is a growing adoption of AI-driven statistics software across various industries, further propelling market expansion.

Regionally, North America is expected to maintain its dominance in the statistics software market, owing to the presence of numerous leading software providers, high adoption of advanced analytics solutions, and substantial investments in research and development. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, driven by the rapid digital transformation of businesses, increasing awareness of data analytics benefits, and supportive government initiatives promoting technological advancements.

Component Analysis

The statistics software market is segmented by component into software and services. The software segment includes various types of statistical analysis tools, ranging from basic data visualization software to advanced predictive analytics platforms. This segment holds the largest market share due to the widespread adoption of software solutions that enable organizations to analyze and interpret data efficiently. The continuous development of innovative features, such as real-time analytics, data mining, and machine learning capabilities, is further driving the demand for statistics software.

In contrast, the services segment encompasses consulting, implementation, training, and support services provided by software vendors and third-party providers. These services are crucial for organizations to effectively utilize statistical software and maximize its benefits. The growing complexity of data and the need for specialized expertise in data analysis are driving the demand for professional services in the statistics software market. Moreover, as more businesses adopt advanced analytics solutions, the need for ongoing support and training services is expected to increase, contributing to the growth of the services segment.

The integration of cloud computing with statistics software is also influencing the component-wise growth of this market. Cloud-based solutions offer several advantages, such as scalability, flexibility, and cost-effectiveness, making them an attractive option for organizations of all sizes. As a result, there is a

snapshot-tab-pane Data Analytics Market Size 2026-2030The data analytics market size is valued to increase by USD 375.6 billion, at a CAGR of 16.4% from 2025 to 2030. Increasing adoption of data-driven decision making across various industries will drive the data analytics market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 32.6% growth during the forecast period.By Component - Services segment was valued at USD 207.6 billion in 2024By Deployment - Cloud segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 523.6 billionMarket Future Opportunities: USD 375.6 billionCAGR from 2025 to 2030 : 16.4%Market SummaryThe data analytics market is defined by its critical role in converting raw data into strategic intelligence. A primary driver is the enterprise-wide shift toward data-driven decision-making, where organizations use business intelligence tools for everything from supply chain optimization to enhancing the personalized customer experience.Key trends include the integration of machine learning algorithms for predictive modeling and the expansion of cloud-native analytics, which provide scalable and flexible data warehousing solutions.For instance, a retail firm can now use real-time data processing to instantly adjust inventory levels based on live sales data and simultaneously launch targeted marketing campaigns, a process that improves operational efficiency by over 30%.However, the industry faces challenges related to data governance frameworks and a persistent shortage of professionals with skills in advanced statistical analysis, which can hinder the adoption of more complex technologies like natural language processing and federated learning.What will be the Size of the Data Analytics Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Data Analytics Market Segmented?The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD billion" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesApplicationSupply chain managementDatabase managementEnterprise resource planningOthersGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceAPACChinaJapanIndiaMiddle East and AfricaSaudi ArabiaUAESouth AfricaSouth AmericaBrazilArgentinaColombiaRest of World (ROW)By Component InsightsThe services segment is estimated to witness significant growth during the forecast period.The services segment is integral to the data analytics market, encompassing professional consulting, platform deployment, and managed support. Organizations increasingly rely on these services to design and implement embedded analytical frameworks and navigate complex multi-cloud analytics environments.Service providers offer expertise in establishing robust data governance frameworks, which are critical for ensuring regulatory compliance reporting and managing data as a strategic asset.By leveraging specialized consulting for algorithmic decision-making and creating semantic data layers, firms can accelerate their data-driven decision-making capabilities.Partnering with managed service providers has been shown to reduce implementation risks associated with hybrid cloud analytics by up to 40%, ensuring a smoother transition and faster time-to-value. Get Free SampleThe Services segment was valued at USD 207.6 billion in 2024 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisNorth America is estimated to contribute 32.6% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Data Analytics Market Demand is Rising in North America Get Free SampleThe geographic landscape of the data analytics market is characterized by mature, high-value adoption in North America and rapid, scalable growth in APAC.North America leads in deploying advanced data warehousing solutions and embedded analytical frameworks, with a strong focus on regulatory compliance reporting and data security protocols. The region benefits from robust infrastructure supporting real-time data processing for edge computing analytics.In contrast, APAC is the fastest-growing region, expanding at a rate nearly 2% higher than North America, driven by mobile-first economies and massive investments in data pipeline automation.This expansion is fueling demand for marketing campaign optimization and tools that turn data into a strategic asset. Both regions show a strong trend toward cloud-native

This Kaggle dataset comes from an output dataset that powers my March Madness Data Analysis dashboard in Domo. - Click here to view this dashboard: Dashboard Link - Click here to view this dashboard features in a Domo blog post: Hoops, Data, and Madness: Unveiling the Ultimate NCAA Dashboard

This dataset offers one the most robust resource you will find to discover key insights through data science and data analytics using historical NCAA Division 1 men's basketball data. This data, sourced from KenPom, goes as far back as 2002 and is updated with the latest 2026 data. This dataset is meticulously structured to provide every piece of information that I could pull from this site as an open-source tool for analysis for March Madness.

Key features of the dataset include: - Historical Data: Provides all historical KenPom data from 2002 to 2026 from the Efficiency, Four Factors (Offense & Defense), Point Distribution, Height/Experience, and Misc. Team Stats endpoints from KenPom's website. Please note that the Height/Experience data only goes as far back as 2007, but every other source contains data from 2002 onward. - Data Granularity: This dataset features an individual line item for every NCAA Division 1 men's basketball team in every season that contains every KenPom metric that you can possibly think of. This dataset has the ability to serve as a single source of truth for your March Madness analysis and provide you with the granularity necessary to perform any type of analysis you can think of. - 2026 Tournament Insights: Contains all seed and region information for the 2026 NCAA March Madness tournament. Please note that I will continually update this dataset with the seed and region information for previous tournaments as I continue to work on this dataset.

These datasets were created by downloading the raw CSV files for each season for the various sections on KenPom's website (Efficiency, Offense, Defense, Point Distribution, Summary, Miscellaneous Team Stats, and Height). All of these raw files were uploaded to Domo and imported into a dataflow using Domo's Magic ETL. In these dataflows, all of the column headers for each of the previous seasons are standardized to the current 2026 naming structure so all of the historical data can be viewed under the exact same field names. All of these cleaned datasets are then appended together, and some additional clean up takes place before ultimately creating the intermediate (INT) datasets that are uploaded to this Kaggle dataset. Once all of the INT datasets were created, I joined all of the tables together on the team name and season so all of these different metrics can be viewed under one single view. From there, I joined an NCAAM Conference & ESPN Team Name Mapping table to add a conference field in its full length and respective acronyms they are known by as well as the team name that ESPN currently uses. Please note that this reference table is an aggregated view of all of the different conferences a team has been a part of since 2002 and the different team names that KenPom has used historically, so this mapping table is necessary to map all of the teams properly and differentiate the historical conferences from their current conferences. From there, I join a reference table that includes all of the current NCAAM coaches and their active coaching lengths because the active current coaching length typically correlates to a team's success in the March Madness tournament. I also join another reference table to include the historical post-season tournament teams in the March Madness, NIT, CBI, and CIT tournaments, and I join another reference table to differentiate the teams who were ranked in the top 12 in the AP Top 25 during week 6 of the respective NCAA season. After some additional data clean-up, all of this cleaned data exports into the "DEV _ March Madness" file that contains the consolidated view of all of this data.

This dataset provides users with the flexibility to export data for further analysis in platforms such as Domo, Power BI, Tableau, Excel, and more. This dataset is designed for users who wish to conduct their own analysis, develop predictive models, or simply gain a deeper understanding of the intricacies that result in the excitement that Division 1 men's college basketball provides every year in March. Whether you are using this dataset for academic research, personal interest, or professional interest, I hope this dataset serves as a foundational tool for exploring the vast landscape of college basketball's most riveting and anticipated event of its season.