This dataset is about books. It has 1 row and is filtered where the book is Pro Power BI Desktop : Free Interactive Data Analysis with Microsoft Power BI. It features 7 columns including author, publication date, language, and book publisher.

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.

Power BI Consulting Service Market Outlook

The global Power BI Consulting Service market size was valued at approximately $1.2 billion in 2023 and is projected to reach around $4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.3% during the forecast period. This substantial growth is driven by the increasing adoption of business intelligence and data analytics tools across numerous industries.

One of the primary growth factors for the Power BI Consulting Service market is the escalating demand for data-driven decision-making across various sectors. As organizations increasingly recognize the value of business intelligence tools in extracting actionable insights from raw data, the need for skilled consultants to implement and manage these tools has surged. Moreover, the proliferation of big data and the rising importance of data visualization techniques are further propelling market growth. Companies are looking to leverage Power BI's robust capabilities to transform complex data sets into intuitive and interactive dashboards, thereby enhancing their strategic decision-making processes.

Another significant driver for the market is the rapid digital transformation and the shift towards cloud-based solutions. With the advent of cloud computing, enterprises of all sizes are investing heavily in cloud infrastructure, which offers flexibility, scalability, and cost-effectiveness. Power BI, with its seamless integration with various cloud services and platforms, is becoming a go-to solution for businesses aiming to modernize their data strategies. Consequently, the demand for consultancy services to assist in the smooth adoption and integration of Power BI into existing IT ecosystems is on the rise.

The increasing trend of remote work and the need for real-time data access and collaboration have also contributed to market expansion. As businesses adapt to the new normal brought about by the COVID-19 pandemic, there is a growing requirement for tools that facilitate remote collaboration and instant data sharing. Power BI's capability to provide real-time analytics and its ease of use make it an attractive option for businesses looking to maintain productivity and efficiency in a distributed work environment. This has led to heightened demand for consulting services to ensure that organizations can effectively leverage Power BI to meet their dynamic needs.

Regionally, North America is expected to hold a dominant position in the Power BI Consulting Service market, driven by the presence of numerous technology giants and high adoption rates of advanced analytics tools. However, the Asia Pacific region is anticipated to witness the fastest growth, attributed to the burgeoning IT sector and increasing digital initiatives by governments and businesses. European markets, with their focus on regulatory compliance and data protection, also present significant opportunities for growth in the Power BI consulting domain.

In the realm of business intelligence, Win-Loss Analysis Service is gaining traction as a crucial tool for organizations striving to understand their competitive positioning. This service involves a detailed examination of past business deals, identifying factors that contributed to wins and losses. By leveraging insights from Win-Loss Analysis, companies can refine their strategies, enhance customer engagement, and improve their overall sales effectiveness. The integration of such analysis with Power BI enables businesses to visualize patterns and trends, offering a comprehensive view of market dynamics. As organizations seek to optimize their decision-making processes, the demand for Win-Loss Analysis Service is expected to rise, complementing the growth of Power BI consulting services.

Service Type Analysis

The Power BI Consulting Service market can be segmented by service type into Implementation, Training, Support, and Maintenance. Among these, the implementation segment is expected to hold the largest market share during the forecast period. The increasing complexity of data environments and the need for customized solutions are driving the demand for implementation services. Organizations often require expert assistance to configure and deploy Power BI according to their specific requirements, ensuring that the tool integrates seamlessly with existing systems and processes.

Training services are also gaining prominence as businesses strive to empower thei

About Dataset

Spotify Dashboard in Power BI:

The goal of this Power BI Dashboard is to analyze Spotify data to provide insights into track performance, artist popularity, and playlist or chart trends, enabling stakeholders to make data-driven decisions in the music industry.

From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:

• Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.

• Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.

• Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.

• Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.

This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:

1. A diagram of the MySQL database schema.
2. A SQL dump of the MySQL database schema, excluding the data.
3. A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
4. Raw data from each database table in CSV format.
5. A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
6. A text file containing the definitions of the size categories used in the collection_unit table.

The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:

Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.

Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.

Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.

This Rio Grande and Pecos River Water Operations Dashboard was created using the Microsoft Power BI application and is currently available to the public. This dashboard was created to provide real time data of the Rio Grande and Pecos rivers and reservoirs for water operation managers to assist in monitoring and making decisions. Data includes 15-minute water flow data and reservoir elevation and storage data from the U.S. Geological Survey, Colorado Department of Water Resources, and U.S. Bureau of Reclamation. The water operations dashboard is in an easy to navigate format that allows the user to clearly view current river and reservoir data at a single website to help make operations, management, and planning decisions.

[Dataset Name] - About This Dataset

Overview

This dataset is used in a data cleaning project based on the raw data from Alex the Analyst's Power BI tutorial series. The original dataset can be found here.

Context

The dataset is employed in a mini project that involves cleaning and preparing data for analysis. It is part of a series of exercises aimed at enhancing skills in data cleaning using Pandas.

Content

The dataset contains information related to [provide a brief description of the data, e.g., sales, customer information, etc.]. The columns cover various aspects such as [list key columns and their meanings].

Acknowledgements

The original dataset is sourced from Alex the Analyst's Power BI tutorial series. Special thanks to [provide credit or acknowledgment] for making the dataset available.

Citation

If you use this dataset in your work, please cite it as follows:

How to Use

1. Download the dataset from this link.
2. Explore the Jupyter Notebook in the associated repository for insights into the data cleaning process.

Feel free to reach out for any additional information or clarification. Happy analyzing!

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

This dataset contains the essential files for conducting a dynamic stock market analysis using Power BI. The data is sourced from Yahoo Finance and includes historical stock prices, which can be dynamically updated by adding new stock codes to the provided Excel sheet.

Files Included: Power BI Report (.pbix): The interactive Power BI report that includes various visualizations such as Candle Charts, Line Charts for Support and Resistance, and Technical Indicators like SMA, EMA, Bollinger Bands, and RSI. The report is designed to provide a comprehensive analysis of stock performance over time.

Stock Data Excel Sheet (.xlsx): This Excel sheet is connected to the Power BI report and allows for dynamic data loading. By adding new stock codes to this sheet, the Power BI report automatically refreshes to include the new data, enabling continuous updates without manual intervention.

Overview and Chart Pages Snapshots for better understanding about the Report.

Key Features: Dynamic Data Loading: Easily update the dataset by adding new stock codes to the Excel sheet. The Power BI report will automatically pull the corresponding data from Yahoo Finance. Comprehensive Visualizations: Analyze stock trends using Candle Charts, identify key price levels with Support and Resistance lines, and explore market behavior through various technical indicators. Interactive Analysis: The Power BI report includes slicers and navigation buttons to switch between different time periods and visualizations, providing a tailored analysis experience. Use Cases: Ideal for financial analysts, traders, or anyone interested in conducting a detailed stock market analysis. Can be used to monitor the performance of individual stocks or compare trends across multiple stocks over time. Tags: Stock Market Power BI Financial Analysis Yahoo Finance Data Visualization

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!

What is IPGOD?\r

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.\r \r \r

How do I use IPGOD?\r

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.\r \r \r

IP Data Platform\r

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\r \r

References\r

\r 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.\r \r * Patents\r * Trade Marks\r * Designs\r * Plant Breeder’s Rights\r \r \r

Updates\r

\r

Tables and columns\r

\r Due to the changes in our systems, some tables have been affected.\r \r * We have added IPGOD 225 and IPGOD 325 to the dataset!\r * The IPGOD 206 table is not available this year.\r * 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.\r \r

Data quality improvements\r

\r Data quality has been improved across all tables.\r \r * Null values are simply empty rather than '31/12/9999'.\r * All date columns are now in ISO format 'yyyy-mm-dd'.\r * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.\r * All tables are encoded in UTF-8.\r * All tables use the backslash \ as the escape character.\r * 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.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Beginning big data with Power BI and Excel 2013 : big data processing and analysis using Power BI in Excel 2013. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

About Datasets: - Domain : Finance - Project: Bank loan of customers - Datasets: Finance_1.xlsx & Finance_2.xlsx - Dataset Type: Excel Data - Dataset Size: Each Excel file has 39k+ records

KPI's: 1. Year wise loan amount Stats 2. Grade and sub grade wise revol_bal 3. Total Payment for Verified Status Vs Total Payment for Non Verified Status 4. State wise loan status 5. Month wise loan status 6. Get more insights based on your understanding of the data

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains stacked column chart, Donut chart, Stacked area chart, pie chart, matrix, slicer, treemap, clustered column chart, Map, Dashboard, Page Navigator, card, text box.

Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents

This data contains Table, Relationships.

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 completely based on personal expenses, incomes & savings throughout the year done by my family members. The finance analysis of 2022 year shows how much I had earned, saved and spent on several categories. Here you can see the following attributes like description : It shows the area of finance category: which are the area where money is going and coming sub category: It is the sub part of category category type : Like income, savings & expenses debit amount: includes expenses credit amount: includes incomes and savings Also where I had invested and should I have continue or not, what all things I have to control and what are the savings I can do from my income , this I can analyze from this report very easily. what are my way of spending and how much I'm focusing on my savings after having incomes from several sources. Which sub category shows more expenses and what I can limit that analysis I can do from this and many more to do.

This Power BI dashboard shows the COVID-19 vaccination rate by key demographics including age groups, race and ethnicity, and sex for Tempe zip codes.Data Source: Maricopa County GIS Open Data weekly count of COVID-19 vaccinations. The data were reformatted from the source data to accommodate dashboard configuration. The Maricopa County Department of Public Health (MCDPH) releases the COVID-19 vaccination data for each zip code and city in Maricopa County at ~12:00 PM weekly on Wednesdays via the Maricopa County GIS Open Data website (https://data-maricopa.opendata.arcgis.com/). More information about the data is available on the Maricopa County COVID-19 Vaccine Data page (https://www.maricopa.gov/5671/Public-Vaccine-Data#dashboard). The dashboard’s values are refreshed at 3:00 PM weekly on Wednesdays. The most recent date included on the dashboard is available by hovering over the last point on the right-hand side of each chart. Please note that the times when the Maricopa County Department of Public Health (MCDPH) releases weekly data for COVID-19 vaccines may vary. If data are not released by the time of the scheduled dashboard refresh, the values may appear on the dashboard with the next data release, which may be one or more days after the last scheduled release.Dates: Updated data shows publishing dates which represents values from the previous calendar week (Sunday through Saturday). For more details on data reporting, please see the Maricopa County COVID-19 data reporting notes at https://www.maricopa.gov/5460/Coronavirus-Disease-2019.

This dashboard examines sales performance (2014-2017), detailing total quantity, cost, revenue, and profit by year, sales channel (online/offline), item type, order priority, month, and country.

Providing analytical visions of sales performance by tracking quantities, costs, revenues, profits over the years, sales channels, types of products, and countries.

This Power BI dashboard provides an in-depth analysis of Nike's US sales performance for 2020-21. It includes key insights on revenue trends, top-selling products, regional performance, customer segmentation, and seasonal variations. The interactive visualizations help identify growth opportunities and areas for improvement. Ideal for business analysts, marketers, and data enthusiasts looking to explore Nike’s sales data effectively.

This project focuses on developing a machine learning-driven system to classify hospital claims and treatment outcomes, offering a second opinion on healthcare costs and decision-making for insurance claims and treatment efficacy.Key Features and Tools:Machine Learning Algorithms: Leveraging Python (pandas, numpy, scikit-learn) for predictive modeling to assess claim validity and treatment outcomes.APIs Integration: Used Google Maps API to retrieve and map the locations of private hospitals in Malaysia.GIS Mapping Dashboard: Created a GIS-enabled dashboard in Microsoft Power BI to visualize private hospital distribution across Malaysia, aiding healthcare planning and analysis.Advanced Analytics Tools: Integrated Microsoft Excel, Python, and Google Collab for data processing and automation workflows.