Bank Data Analysis | Real World Project | Power BI In this Visualization, I have followed the process of analyzing Bank dataset using Microsoft Power BI. I have started by importing the data into Power BI and then i performed the data cleaning, transformation, and visualization on the given data to gain insights and create a comprehensive analysis report.

Here i have created the insightful visualizations and interactive reports that can be used for business intelligence and decision-making purposes.

Data Set: Took the support from tutorial by Data Visionary.

You tube Video referred: https://www.youtube.com/watch?v=GZqBefbNP10&t=1581s

Analysis done and Visualization shown on: 1. Balance by Age and Gender 2. Number of Customers by Age and Gender 3. Number of Customers by Region 4. Balance by Region 5. Number of Customers by JobType 6. Balance by Gender 7. Total Customers Joined 8. Cards- i) Max Balance by Age ii) Min Balance by Age iii) Max Customers by Gender

Dear All, Kindly go through the same and please provide me the suggestions and guide me for any changes required and correct me where i need to improve.

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.

🔍 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

Explore the world of data visualization with this Power BI dataset containing HR Analytics and Sales Analytics datasets. Gain insights, create impactful reports, and craft engaging dashboards using real-world data from HR and sales domains. Sharpen your Power BI skills and uncover valuable data-driven insights with this powerful dataset. Happy analyzing!

Tabular dataset for data analysis and machine learning practice. The dataset is about the market and is usable for Power BI practice and data science.

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.
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Pricing Options:

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Experience a seamless journey with Oxylabs:

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

🧮 Complete DAX Practice Dataset — Power BI / DAX Learning Resource

📘 Overview

This synthetic dataset is designed specifically for Power BI and DAX (Data Analysis Expressions) learners and professionals. It provides a complete star schema for practicing DAX measures, relationships, filters, and time intelligence — just like in real-world business analytics projects.

The dataset simulates a multi-year sales environment with customers, employees, products, geographies, and dates — allowing you to perform calculations across multiple business dimensions.

🧩 Dataset Structure

This dataset contains 6 CSV files, forming a clean star schema:

🗂️ Fact Table Fields

📅 DimDate Fields

Includes:

• DateKey (YYYYMMDD)
• Date
• Year, Quarter, Month, MonthName, Day, DayOfWeek
• IsWeekend, IsHoliday

Perfect for DAX time intelligence functions like: TOTALYTD, SAMEPERIODLASTYEAR, DATESINPERIOD, and PARALLELPERIOD.

🌍 Business Scenario

Imagine a mid-sized electronics retailer operating across multiple regions and sales channels. The dataset captures 7 years of simulated performance — including seasonal patterns, regional sales variations, and customer loyalty effects.

🧠 Learning Objectives

This dataset is designed for:

• Practicing Power BI data modeling
• Learning and mastering DAX functions
• Building interactive dashboards
• Applying time intelligence and advanced calculations
• Teaching data modeling concepts in analytics courses

💡 Example DAX Practice Topics

You can use this dataset to practice almost every DAX concept:

Basic Aggregations

Total Sales = SUM(FactSales[SalesAmount])
Total Profit = SUM(FactSales[Profit])

Context & Filters

Online Sales = CALCULATE([Total Sales], FactSales[Channel] = "Online")

Time Intelligence

YTD Sales = TOTALYTD([Total Sales], DimDate[Date])
Sales YoY % = DIVIDE([Total Sales] - [Previous Year Sales], [Previous Year Sales])

Relationship Functions

Shipped Sales = CALCULATE([Total Sales], USERELATIONSHIP(FactSales[ShipDateKey], DimDate[DateKey]))

Ranki...

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...

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.

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 :)

Credit Card Financial Dashboard

This project presents a comprehensive Credit Card Financial Report created using Power BI. It aims to provide a detailed analysis of credit card operations on a weekly basis, offering real-time insights into key performance metrics and trends. The dashboard empowers stakeholders to monitor and analyze credit card operations effectively, facilitating informed decision-making processes.

Project Overview:

The "Credit Card Financial Dashboard" leverages a Kaggle dataset containing anonymized credit card transaction data. The dataset includes information such as transaction volume, transaction types, transaction amounts, customer demographics, spending behavior, and credit limits.

Project Contents:

1. Credit Card Transaction Report: This page provides a detailed analysis of credit card transactions, including transaction volume, types of transactions (e.g., purchases, cash advances), transaction amounts, and any anomalies or trends observed. Visualizations such as bar charts, line graphs, and pie charts are utilized to present the data effectively. 2. Credit Card Customer Report: The second page focuses on analyzing customer-related metrics, such as customer demographics (age, gender), spending behavior (average transaction amount, frequency of transactions), credit limits, and any customer-specific insights that can aid in decision-making processes. Visualizations such as demographic distributions, spending patterns, and credit limit distributions are included to provide a comprehensive overview of customer behavior.

The size of the Business Intelligence Market was valued at USD 33.12 Billion in 2024 and is projected to reach USD 70.38 Billion by 2033, with an expected CAGR of 11.37% during the forecast period. Recent developments include: January 2023: Microsoft unveiled Power BI enhanced experiences in Microsoft Teams in January 2023. The three new features announced are rich broadcasting cards for Conversation in Microsoft Teams and an upgrade for old Power BI tabs for taking notes and learning from experiences and needs., December 2022: Tableau 2022.4 was released in December 2022 for customers and researchers to explore information. It automates creating, analyzing, and communicating insights through data stories, including Data Change Radar, Information Guide, and Explaining the Viz., October 2022: Oracle increased inclusive and included data and analytics capabilities in October 2022 to empower business users. With the extra stuff in Oracle Fusion Analytics for ERP, CX, HCM, and SCM data analysis, business users can track performance against corporate objectives using visualizations, KPIs, and analytics.. Key drivers for this market are: Growing Volume of Data: The increasing generation of data from various sources drives the need for effective data management and analysis capabilities.

Demand for Real-Time Insights: Businesses require real-time data insights to make timely decisions and respond to market changes effectively.

Adoption of Cloud-Based Solutions: Cloud-based BI solutions offer flexibility, cost-effectiveness, and scalability, driving their adoption.. Potential restraints include: Data Security and Privacy Concerns: The handling and storage of sensitive data raise concerns about data breaches and privacy violations.

Integration Complexity: Integrating BI systems with other enterprise applications and data sources can be complex and time-consuming.

Skill Shortage: The lack of skilled professionals with expertise in data analysis and business intelligence poses a challenge.. Notable trends are: Cognitive BI: BI tools are incorporating cognitive technologies to automate data analysis and provide personalized insights.

Predictive Analytics: BI platforms are leveraging predictive analytics to anticipate future events and trends.

Self-Service BI: Self-service BI empowers business users to create their own reports and analyses without the need for technical assistance.

Natural Language Processing (NLP): NLP capabilities enable users to interact with BI tools using natural language queries..

This is the replication data for paper "A Soft Power Challenge, or an Opportunity? A Big Data analysis on Chinese Soft Power During COVID-19 Pandemic." The files include 1) the metadata for the news texts we utilized for the analyses, 2) preprocessed tokens of the news texts, 3) the python code that we used to conduct the DMR analyses and keyword counting, and 4) the results tables.

This project is built on the AdventureWorks dataset, originally provided by Microsoft for SQL Server samples. This comprehensive dataset models a bicycle manufacturer and its sales to global markets, offering a realistic foundation for a data analytics portfolio.

The raw data can be accessed and downloaded directly from the official Microsoft GitHub repository: https://github.com/microsoft/sql-server-samples/tree/master/samples/databases/adventure-works

Project Overview

The work presented in this portfolio project demonstrates my end-to-end data analysis skills, from initial data cleaning and modeling to creating an interactive, insight-driven dashboard. Within this project, you will find examples of various data visualizations and a dashboard layout that follows the F-pattern for optimized user experience.

I encourage you to download the dataset and follow along with my analysis. Feel free to replicate my work, critique my methods, or build upon it with your own creative insights and improvements. Your feedback and engagement are highly welcomed!

The global predictive analytics market is poised for substantial expansion, projected to reach a market size of approximately $XX billion by 2025, with a Compound Annual Growth Rate (CAGR) of XX% expected to propel it to an estimated $XX billion by 2033. This robust growth is primarily fueled by the increasing adoption of big data and advanced analytics across diverse industries seeking to gain a competitive edge through data-driven decision-making. Key drivers include the escalating need for fraud detection and risk management, personalized customer experiences, and optimized operational efficiencies. The proliferation of IoT devices, generating massive volumes of data, further amplifies the demand for predictive analytics solutions to glean actionable insights. Businesses are increasingly leveraging predictive models to forecast future trends, anticipate customer behavior, and proactively address potential issues before they impact operations. The predictive analytics landscape is segmented into services and solutions, with both witnessing significant uptake. Services, encompassing consulting, implementation, and support, are crucial for enabling organizations to effectively deploy and utilize predictive analytics capabilities. Solutions, on the other hand, refer to the software and platforms that power these analyses, ranging from data mining tools to machine learning algorithms. The market is characterized by intense competition, with prominent players like IBM, Oracle, SAP, and Microsoft investing heavily in research and development to enhance their offerings. Geographically, North America currently dominates the market, driven by early adoption and a strong technological infrastructure. However, the Asia Pacific region is anticipated to witness the fastest growth due to increasing digitalization and a burgeoning startup ecosystem. Restraints such as data privacy concerns and the need for skilled data scientists are being addressed through evolving regulations and specialized training programs, indicating a dynamic and adaptive market. This report offers an in-depth analysis of the global Predictive Analytics market, projecting a robust expansion from USD 10,500 million in 2024 to USD 35,000 million by 2033, demonstrating a Compound Annual Growth Rate (CAGR) of 14.4% during the forecast period of 2025-2033. The study meticulously examines historical data from 2019-2024 and presents a detailed outlook for the future, with 2025 serving as both the base and estimated year.

Knowledge Discovery Platform Market Outlook

According to our latest research, the global Knowledge Discovery Platform market size in 2024 stands at USD 17.2 billion, reflecting robust adoption across industries. The market is experiencing a strong growth momentum, with a compound annual growth rate (CAGR) of 18.5% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach an impressive USD 89.7 billion. This rapid expansion is primarily driven by escalating data volumes, the imperative for actionable business intelligence, and the proliferation of artificial intelligence and machine learning technologies. As organizations seek to harness the power of big data for competitive advantage, the demand for advanced Knowledge Discovery Platforms continues to surge globally.

One of the principal growth factors propelling the Knowledge Discovery Platform market is the exponential increase in data generated by enterprises, governments, and consumers. The digital transformation wave has resulted in data being produced at an unprecedented rate, from social media interactions to IoT devices, transactional records, and digital documents. Organizations are under mounting pressure to extract meaningful insights from this sea of information to inform strategic decisions, optimize operations, and enhance customer experiences. Knowledge Discovery Platforms, equipped with sophisticated data mining, text analytics, and visualization tools, enable businesses to uncover hidden patterns, trends, and correlations within massive datasets. This capability is particularly vital in sectors such as BFSI, healthcare, and retail, where timely and accurate insights can directly impact profitability and risk management.

Another significant driver is the growing integration of artificial intelligence and machine learning algorithms into Knowledge Discovery Platforms. These intelligent systems automate complex analytical processes, reducing the reliance on manual data exploration and accelerating time-to-insight. Predictive analytics functionalities, for example, empower organizations to anticipate market trends, customer behaviors, and operational risks with greater precision. As AI and ML technologies mature, their seamless incorporation into knowledge discovery workflows enhances the platforms' ability to handle unstructured data, perform sentiment analysis, and support real-time decision-making. The increasing availability of cloud-based solutions further democratizes access, enabling even small and medium enterprises to leverage advanced analytics without heavy upfront investments in infrastructure.

The regulatory landscape and the need for compliance are also fueling the adoption of Knowledge Discovery Platforms. Industries such as banking, healthcare, and government face stringent requirements around data governance, privacy, and reporting. Advanced platforms help organizations maintain compliance by providing traceable, auditable insights and supporting data lineage tracking. Moreover, the rise of explainable AI and transparent analytics has become crucial for organizations seeking to build trust with regulators, partners, and customers. As regulations evolve to address new data privacy and security concerns, the role of robust knowledge discovery solutions in ensuring organizational resilience and accountability becomes even more pronounced.

From a regional perspective, North America leads the market, driven by early technology adoption, a strong presence of leading vendors, and high enterprise IT spending. Europe follows closely, with substantial investments in digital transformation and data-driven initiatives across key sectors. The Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, expanding digital infrastructure, and government-led smart initiatives. Latin America and the Middle East & Africa are also emerging as promising markets, supported by increasing awareness of data-driven decision-making and the gradual modernization of business processes. Each region presents unique opportunities and challenges, shaped by local regulatory environments, technological readiness, and industry dynamics.

Data Mining Tools are integral to the functionality of Knowledge Discovery Platforms, offering organizations the ability to process and analyze vast amoun

Dataset

This dataset was created by BunY12345

Contents

Adventure Works is a bike manufacturer and seller and in this project I analyze their sales and returns data using Microsoft Power BI Desktop.

This was an end to end project beginning with importing and data cleaning and ending with the creation of visuals and optimization of those visuals.

The Superstore Sales Data dataset, available in an Excel format as "Superstore.xlsx," is a comprehensive collection of sales and customer-related information from a retail superstore. This dataset comprises* three distinct tables*, each providing specific insights into the store's operations and customer interactions.

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.