Data Analytics in Financial Market Outlook

The global data analytics in financial market size was valued at approximately USD 10.5 billion in 2023 and is projected to reach around USD 34.8 billion by 2032, growing at a robust CAGR of 14.4% during the forecast period. This remarkable growth is driven by the increasing adoption of advanced analytics technologies, the need for real-time data-driven decision-making, and the rising incidence of financial fraud.

One of the primary growth factors for the data analytics in the financial market is the burgeoning volume of data generated from diverse sources such as transactions, social media, and online banking. Financial institutions are increasingly leveraging data analytics to process and analyze this vast amount of data to gain actionable insights. Additionally, technological advancements in artificial intelligence (AI) and machine learning (ML) are significantly enhancing the capabilities of data analytics tools, enabling more accurate predictions and efficient risk management.

Another driving factor is the heightened focus on regulatory compliance and security management. In the wake of stringent regulations imposed by financial authorities globally, organizations are compelled to adopt robust analytics solutions to ensure compliance and mitigate risks. Moreover, with the growing threat of cyber-attacks and financial fraud, there is a heightened demand for sophisticated analytics tools capable of detecting and preventing fraudulent activities in real-time.

Furthermore, the increasing emphasis on customer-centric strategies in the financial sector is fueling the adoption of data analytics. Financial institutions are utilizing analytics to understand customer behavior, preferences, and needs more accurately. This enables them to offer personalized services, improve customer satisfaction, and drive revenue growth. The integration of advanced analytics in customer management processes helps in enhancing customer engagement and loyalty, which is crucial in the competitive financial landscape.

Regionally, North America has been the dominant player in the data analytics in financial market, owing to the presence of major market players, technological advancements, and a high adoption rate of analytics solutions. However, the Asia Pacific region is anticipated to witness the highest growth during the forecast period, driven by the rapid digitalization of financial services, increasing investments in analytics technologies, and the growing focus on enhancing customer experience in emerging economies like China and India.

Component Analysis

In the data analytics in financial market, the components segment is divided into software and services. The software segment encompasses various analytics tools and platforms designed to process and analyze financial data. This segment holds a significant share in the market owing to the continuous advancements in software capabilities and the growing need for real-time analytics. Financial institutions are increasingly investing in sophisticated software solutions to enhance their data processing and analytical capabilities. The software segment is also being propelled by the integration of AI and ML technologies, which offer enhanced predictive analytics and automation features.

On the other hand, the services segment includes consulting, implementation, and maintenance services provided by vendors to help financial institutions effectively deploy and manage analytics solutions. With the rising complexity of financial data and analytics tools, the demand for professional services is on the rise. Organizations are seeking expert guidance to seamlessly integrate analytics solutions into their existing systems and optimize their use. The services segment is expected to grow significantly as more institutions recognize the value of professional support in maximizing the benefits of their analytics investments.

The software segment is further categorized into various types of analytics tools such as descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics tools are used to summarize historical data to identify patterns and trends. Predictive analytics tools leverage historical data to forecast future outcomes, which is crucial for risk management and fraud detection. Prescriptive analytics tools provide actionable recommendations based on predictive analysis, aiding in decision-making processes. The growing need for advanced predictive and prescriptive analytics is driving the demand for specialized software solut

Abstract

Data Axle (formerly Infogroup) is a data analytics marketing firm that provides marketing data on millions of businesses and consumers.

CMU Libraries houses and facilitates access to two forms of Data Axle historical data:

1. Historic Business Data - address-level data on US businesses and other organizations. Data fields include: street address, phone number, number of employees, SIC codes, and more.

2. Residential Historical Data - geo-referenced data on millions of households and basic consumer profiles. Data fields include: household income, home value, years in residence and more.

The files are organized in state-year format, with each file providing a snapshot of households/businesses at the end of each calendar year. The data can be represented as a time series from 1996-2022 (for business records) and 2006-2022 (for consumer records).

Timeframe Extent: Time series, annual. 1996-2022 (for Historic Business), 2006-2022 (for Residential Historical). NOTE: 2023 data will be available at the end of January 2024.

Geographic Extent: United States of America

Unit of Analysis: Households (for Historical Residential), businesses (for Historic Business)

Usage

**NOTE: **these are proprietary datasets that have been licensed by CMU Libraries for academic research purposes.

You must have current faculty, staff or student status at CMU in order to use these datasets. Please do not share any data with those outside of CMU.

Dataset

This dataset was created by Dspyt

Released under GPL 2

Contents

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million, at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

The data science platform 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. DeploymentOn-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

Smart Insider’s Global Share Buyback Database offers invaluable insights to investors on corporate actions data. We provide detailed, up-to-date share buyback data covering over 55,000 companies globally and over 20K+ from Asia, that’s every company that reports Buybacks through regulatory processes.

Our Share buyback data includes detailed information on all major buyback transactions including source announcements and derived analysis fields. Our platform adds a visual representation of the data, allowing investors to quickly identify patterns and make decisions based on their findings.

Get detailed share buyback insights with Smart Insider and stay ahead of the curve with accurate, historical buyback insight that helps you make better investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as CSV, XML or XLSX via SFTP, API or Snowflake.

Sample dataset for Desktop Service has been provided with limited fields. Upon request, we can provide a detailed Quant sample.

Tags: Equity Market Data, Stock Market Data, Corporate Actions Data, Corporate Buyback Data, Company Financial Data, Insider Trading Data

Analysis of ‘LinkNYC Usage Statistics (Historical Data)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3fd12f6f-57ed-4414-89d4-e56a388e0432 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

LinkNYC is replacing the City’s outdated public telephones with a network of kiosks that provide free high-speed Wi-Fi, nationwide calling, a dedicated 911 button, charging ports for mobile devices, and access to selected websites. This dataset contains data on how the LinkNYC kiosks are being used, including numbers of users, number of Wi-Fi sessions, amount of data transmitted, and other information.

--- Original source retains full ownership of the source dataset ---

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 2025 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 2025 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. - 2025 Tournament Insights: Contains all seed and region information for the 2025 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 2025 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.

Context

I always wanted to have a program that fetch the whole stock market data at once without concerning about new companies that went public recently. So, here it is.

Content

This dataset contains 2 python scripts which one can fetch the data from on their own machine without any special requirements by just running the collect.py . I have done this part in May/21/2021 (Version 2). So, the data is available until then. If one wants to have extend that period, they can run the collect.py .

Columns Description

tickers.csv contains ticker names along with some additional data such as name of the company, sector, industry, and the country of the company.

Each CSV file in stocksData folder named as the company's ticker name. Each file has 8 columns: - Date: as an index. - Open, Close, High, Low: which is in dollars. - Volume: which is number of shares that traded in specific date. - Stock Splits: Show if there is a stock split in specific day as the split ratio. - Dividends: which is in dollars. If a company doesn’t provide dividends for their share holders, this column can be dropped.

Acknowledgements

I've used finviz site and yfinance package to gather this rich data.

Inspiration

I hope one can find this helpful and interesting. If you have any questions don't hesitate to contact me at milad@miladtabrizi.com .

The Europe Clinical Data Analytics In Healthcare Market report segments the industry into By Deployment (On-Premise, Cloud), By Application (Patient Engagement, Clinical Decision Support, Personalized Medicine, Early Disease Detection, Others), By End User (Payers, Providers), and By Country (United Kingdom, France, Germany, Italy, Spain, Rest of Europe). Get five years of historical data alongside five-year market forecasts.

Snapshot img

Big Data Market Size 2025-2029

The big data market size is forecast to increase by USD 193.2 billion at a CAGR of 13.3% between 2024 and 2029.

The market is experiencing a significant rise due to the increasing volume of data being generated across industries. This data deluge is driving the need for advanced analytics and processing capabilities to gain valuable insights and make informed business decisions. A notable trend in this market is the rising adoption of blockchain solutions to enhance big data implementation. Blockchain's decentralized and secure nature offers an effective solution to address data security concerns, a growing challenge in the market. However, the increasing adoption of big data also brings forth new challenges. Data security issues persist as organizations grapple with protecting sensitive information from cyber threats and data breaches.
Companies must navigate these challenges by investing in robust security measures and implementing best practices to mitigate risks and maintain trust with their customers. To capitalize on the market opportunities and stay competitive, businesses must focus on harnessing the power of big data while addressing these challenges effectively. Deep learning frameworks and machine learning algorithms are transforming data science, from data literacy assessments to computer vision models.

What will be the Size of the Big Data Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In today's data-driven business landscape, the demand for advanced data management solutions continues to grow. Companies are investing in business intelligence dashboards and data analytics tools to gain insights from their data and make informed decisions. However, with this increased reliance on data comes the need for robust data governance policies and regular data compliance audits. Data visualization software enables businesses to effectively communicate complex data insights, while data engineering ensures data is accessible and processed in real-time. Data-driven product development and data architecture are essential for creating agile and responsive business strategies. Data management encompasses data accessibility standards, data privacy policies, and data quality metrics.
Data usability guidelines, prescriptive modeling, and predictive modeling are critical for deriving actionable insights from data. Data integrity checks and data agility assessments are crucial components of a data-driven business strategy. As data becomes an increasingly valuable asset, businesses must prioritize data security and privacy. Prescriptive and predictive modeling, data-driven marketing, and data culture surveys are key trends shaping the future of data-driven businesses. Data engineering, data management, and data accessibility standards are interconnected, with data privacy policies and data compliance audits ensuring regulatory compliance.
Data engineering and data architecture are crucial for ensuring data accessibility and enabling real-time data processing. The data market is dynamic and evolving, with businesses increasingly relying on data to drive growth and inform decision-making. Data engineering, data management, and data analytics tools are essential components of a data-driven business strategy, with trends such as data privacy, data security, and data storytelling shaping the future of data-driven businesses.

How is this Big Data Industry segmented?

The big data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


End-user

  BFSI
  Healthcare
  Retail and e-commerce
  IT and telecom
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the realm of big data, on-premise and cloud-based deployment models cater to varying business needs. On-premise deployment allows for complete control over hardware and software, making it an attractive option for some organizations. However, this model comes with a significant upfront investment and ongoing maintenance costs. In contrast, cloud-based deployment offers flexibility and scalability, with service providers handling infrastructure and maintenance. Yet, it introduces potential security risks, as data is accessed through multiple points and stored on external servers. Data

Dataset Card for Solana Pairs History

This dataset card provides an overview of the "Solana Pairs Price History", a collection of historical data related to Solana liquidity pairs. It is intended for use in research and development of financial models, data analysis, and machine learning applications.

  Dataset Details





  Dataset Description

The dataset contains historical trading data for Solana pairs, with each pair represented as a separate JSONL file. The… See the full description on the dataset page: https://huggingface.co/datasets/horenresearch/solana-pairs-history.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Cryptocurrency options markets have grown increasingly sophisticated, requiring reliable data infrastructure to support trading and analysis. Our platform gives you direct access to comprehensive crypto options data through straightforward API connections.

We capture the complete options chain across major crypto derivatives exchanges, delivering real-time and historical cryptocurrency market data that shows exactly what's happening in these complex markets. Each options contract is tracked with precision - strikes, expiration dates, premiums, open interest, and volume metrics all accessible through our standardized data feeds.

The data is available through multiple integration methods depending on your needs. Use our REST API for flexible queries and historical analysis, WebSocket for real-time market monitoring, or FIX protocol for institutional-grade connectivity with minimal latency.

Why work with us?

Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume

Technical Excellence: - 99% uptime guarantee - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

When options traders need reliable market intelligence, they don't leave it to chance. That's why trading desks across five continents, quantitative hedge funds managing billions, and fintech innovators building tomorrow's trading platforms all rely on our data infrastructure. We've established ourselves as a dependable source in a market where accuracy isn't just preferred - it's essential. While others promise comprehensive coverage, we deliver it consistently, trade after trade, day after day.

Analysis of ‘Historical data of Earthquakes(1800-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ramjasmaurya/historical-data-of-earthquakes18002021 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

DATASET gives brief description of Earthquakes that were happened between the years 1800 to 2021.

Dataset has 26 columns all including from date metrics to measure metrics with its damages.

some columns are empty ignore them i will remove them later.

THANKS and KEEP UPVOTING

--- Original source retains full ownership of the source dataset ---

• The global industry was valued at US$ 15.5 Bn in 2023
• It is expected to grow at a CAGR of 39.7% from 2024 to 2034 and reach US$ 614.7 Bn by the end of 2034

Market Introduction

Clinical Data Analytics Market Regional Insights

Global Clinical Data Analytics Market Snapshot

Data Analytics Software Market Outlook

The global Data Analytics Software market size was valued at approximately USD 36.5 billion in 2023, and it is projected to reach around USD 143.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.5% during the forecast period. The driving force behind this growth is the increasing adoption of big data solutions across various industries to enhance decision-making processes, operational efficiencies, and customer experiences.

The surge in data generation across different platforms is a significant growth factor for the Data Analytics Software market. With the proliferation of IoT devices, social media, and digital transactions, there is an immense volume of structured and unstructured data that organizations must analyze to gain actionable insights. This has led to a heightened demand for advanced data analytics tools capable of handling and processing large datasets efficiently. Moreover, advancements in AI and machine learning technologies further augment the capabilities of data analytics software, making them indispensable in todayÂ’s data-driven economy.

Another critical growth driver is the increasing adoption of cloud-based solutions. Cloud deployment offers scalability, cost efficiency, and flexibility, making it an attractive option for organizations of all sizes. The ability to access analytics tools from any location and integrate them seamlessly with existing systems reduces the need for extensive on-premises infrastructure. In addition, the growing trend of remote working and the need for real-time data analysis are propelling the market toward cloud-based data analytics solutions.

Regulatory requirements and the need for compliance also play a pivotal role in driving the Data Analytics Software market. Industries such as BFSI, healthcare, and government sectors are under stringent regulations to maintain data integrity, security, and privacy. Data analytics software provides these organizations with robust solutions to monitor and ensure compliance with regulatory standards. Furthermore, the increasing focus on data governance and protection against fraud and cyber threats is pushing organizations to invest heavily in advanced analytics solutions.

The integration of Analytics and BI Software into data analytics solutions is transforming the way organizations interpret and utilize their data. These tools provide businesses with the ability to not only analyze historical data but also to predict future trends and outcomes. By leveraging business intelligence capabilities, companies can make more informed decisions, optimize their operations, and enhance their strategic planning processes. The synergy between analytics and BI software allows for a more comprehensive understanding of business dynamics, enabling organizations to respond swiftly to market changes and customer needs. As the demand for data-driven insights continues to grow, the role of analytics and BI software in enhancing business performance and competitiveness is becoming increasingly critical.

From a regional perspective, North America holds a substantial share of the Data Analytics Software market, driven by the presence of major tech companies, high adoption rates of innovative technologies, and significant investments in R&D. However, Asia Pacific is expected to witness the highest growth rate, attributed to rapid digitalization, increasing adoption of advanced analytics tools by enterprises, and supportive government initiatives in countries like China and India. Europe, Latin America, and the Middle East & Africa also exhibit promising growth potential, driven by technological advancements and increasing awareness of data analytics benefits.

Component Analysis

The Data Analytics Software market is primarily segmented by component into software and services. The software segment holds the largest market share, driven by the increasing adoption of advanced analytics tools that provide comprehensive data analysis capabilities. These software solutions include business intelligence, data mining, predictive analytics, and data visualization tools that help organizations extract valuable insights from large datasets. Recent advancements in AI and machine learning have further enhanced the capabilities of these software solutions, making them more efficient and accurate.

Services, as a component, is also witnessing substantial growth due to the rising de

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Explore the historical Whois records related to mobile-data-analysis.com (Domain). Get insights into ownership history and changes over time.

Tracking United HealthCare Stock Performance Since IPO

Dataset Description

This dataset provides historical stock data for UnitedHealth Group (UHG), one of the largest healthcare and insurance companies in the world. It covers stock prices, market capitalization, and trading volumes from the company's IPO to the present. As a Fortune 500 company with a significant market presence, analyzing UHG's stock performance can provide valuable insights into healthcare market trends, investment opportunities, and economic indicators.

Dataset Features

• Date – The trading date for the stock data.
• Open Price – Stock price at market open.
• Close Price – Stock price at market close.
• High – Highest stock price during the trading day.
• Low – Lowest stock price during the trading day.
• Volume – The number of shares traded on that day.
• Market Cap – The total market capitalization of UnitedHealth Group.

Dataset Distribution

• Data Volume: Number of records depends on trading days from IPO to present.
• Format: CSV, Excel, or other structured data formats.
• Update Frequency: Weekly.

Usage

This dataset is useful for:

• Stock Market Analysis – Analyzing historical stock price trends.
• Financial Forecasting – Predicting future stock price movements using machine learning.
• Investment Research – Assessing UnitedHealth Group’s stock as part of a portfolio.
• Market Trends – Understanding broader trends in the healthcare insurance sector.

Coverage

• Geographic Coverage: United States (NYSE).
• Time Range: From IPO to present.
• Economic Indicators: Healthcare sector, insurance market trends.

License

CC0 (Public Domain) – This dataset is freely available for public and commercial use.

Who Can Use This Dataset?

• Investors & Traders – To analyze market trends and make informed decisions.
• Economists & Researchers – To study healthcare market impacts.
• Data Scientists – To develop predictive stock models.

Gold rose to 3,354.76 USD/t.oz on July 11, 2025, up 0.92% from the previous day. Over the past month, Gold's price has fallen 0.92%, but it is still 39.14% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.