📈 Netflix (NFLX) Historical Stock Data (2002–2025)

This dataset contains daily historical stock data for Netflix Inc. (NFLX) from May 23, 2002 to April 6, 2025. The data includes essential market indicators that are commonly used in financial analysis, algorithmic trading, and machine learning models.

🧾 Dataset Summary

• Ticker: NFLX
• Company: Netflix, Inc.
• Exchange: NASDAQ
• Date Range: May 23, 2002 — April 6, 2025
• Frequency: Daily

📊 Features

🛠️ Potential Use Cases

• Stock price visualization
• Time series forecasting (e.g., ARIMA, LSTM)
• Technical analysis
• Volatility modeling
• Investment strategy backtesting
• Machine learning model training

📌 Notes

• This dataset does not include dividend payouts or earnings reports.
• Adjusted Close is useful for long-term analysis as it reflects stock splits and dividends.

📚 Source

Data was collected from a reliable financial data provider and formatted for easy use in data science projects.

Feel free to use this dataset for educational, research, or investment simulation purposes.

Contact info:

You can contact me for more data sets if you want any type of data to scrape.

-E_mail

-Linkdin

-Kaggle

-X

-Github

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

The women's digital health market is projected to be valued at USD 7.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of around 7.5%, reaching approximately USD 15 billion by 2034.

Comprehensive dataset containing 84 verified Basic Energy Services locations in United States with complete contact information, ratings, reviews, and location data.

This dataset shows the list of Essential Community Providers (ECPs) that provide dental services enrolled in the Centers for Medicare and Medicaid Services (CMS).

Comprehensive dataset containing 53 verified Basic Energy Services locations in Texas, United States with complete contact information, ratings, reviews, and location data.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Health Care Providers: Ready for a Checkup?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2597 companies listed on the Hong Kong Stock Exchange (XHKG) in Hong Kong. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

Top 5 used data fields in the End-of-Day Pricing Dataset for Hong Kong:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Hong Kong:

Hang Seng Index: The main index that tracks the performance of major companies listed on the Hong Kong Stock Exchange. This index provides an overview of the overall market performance in Hong Kong.

Hang Seng China Enterprises Index (HSCEI): The index that tracks the performance of mainland Chinese companies listed on the Hong Kong Stock Exchange. This index reflects the performance of Chinese companies with significant operations in Hong Kong.

Company A: A prominent Hong Kong-based company with diversified operations across various sectors, such as finance, real estate, or retail. This company's stock is widely traded on the Hong Kong Stock Exchange.

Company B: A leading financial institution in Hong Kong, offering banking, insurance, or investment services. This company's stock is actively traded on the Hong Kong Stock Exchange.

Company C: A major player in the Hong Kong property development or other industries, involved in the construction and management of real estate projects. This company's stock is listed and actively traded on the Hong Kong Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Hong Kong, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Hong Kong ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Hong Kong?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Hong Kong exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct tr...

Comprehensive dataset containing 230 verified Basic-Fit 24/7 locations in France with complete contact information, ratings, reviews, and location data.

Snapshot img

Alternative Data Market Size 2025-2029

The alternative data market size is valued to increase USD 60.32 billion, at a CAGR of 52.5% from 2024 to 2029. Increased availability and diversity of data sources will drive the alternative data market.

Major Market Trends & Insights

North America dominated the market and accounted for a 56% growth during the forecast period.
By Type - Credit and debit card transactions segment was valued at USD 228.40 billion in 2023
By End-user - BFSI segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 6.00 million
Market Future Opportunities: USD 60318.00 million
CAGR from 2024 to 2029 : 52.5%

Market Summary

The market represents a dynamic and rapidly expanding landscape, driven by the increasing availability and diversity of data sources. With the rise of alternative data-driven investment strategies, businesses and investors are increasingly relying on non-traditional data to gain a competitive edge. Core technologies, such as machine learning and natural language processing, are transforming the way alternative data is collected, analyzed, and utilized. Despite its potential, the market faces challenges related to data quality and standardization. According to a recent study, alternative data accounts for only 10% of the total data used in financial services, yet 45% of firms surveyed reported issues with data quality.
Service types, including data providers, data aggregators, and data analytics firms, are addressing these challenges by offering solutions to ensure data accuracy and reliability. Regional mentions, such as North America and Europe, are leading the adoption of alternative data, with Europe projected to grow at a significant rate due to increasing regulatory support for alternative data usage. The market's continuous evolution is influenced by various factors, including technological advancements, changing regulations, and emerging trends in data usage.

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

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

How is the Alternative Data Market Segmented ?

The alternative data 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.

Type

  Credit and debit card transactions
  Social media
  Mobile application usage
  Web scrapped data
  Others


End-user

  BFSI
  IT and telecommunication
  Retail
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.

Alternative data derived from credit and debit card transactions plays a significant role in offering valuable insights for market analysts, financial institutions, and businesses. This data category is segmented into credit card and debit card transactions. Credit card transactions serve as a rich source of information on consumers' discretionary spending, revealing their luxury spending tendencies and credit management skills. Debit card transactions, on the other hand, shed light on essential spending habits, budgeting strategies, and daily expenses, providing insights into consumers' practical needs and lifestyle choices. Market analysts and financial institutions utilize this data to enhance their strategies and customer experiences.

Natural language processing (NLP) and sentiment analysis tools help extract valuable insights from this data. Anomaly detection systems enable the identification of unusual spending patterns, while data validation techniques ensure data accuracy. Risk management frameworks and hypothesis testing methods are employed to assess potential risks and opportunities. Data visualization dashboards and machine learning models facilitate data exploration and trend analysis. Data quality metrics and signal processing methods ensure data reliability and accuracy. Data governance policies and real-time data streams enable timely access to data. Time series forecasting, clustering techniques, and high-frequency data analysis provide insights into trends and patterns.

Model training datasets and model evaluation metrics are essential for model development and performance assessment. Data security protocols are crucial to protect sensitive financial information. Economic indicators and compliance regulations play a role in the context of this market. Unstructured data analysis, data cleansing pipelines, and statistical significance are essential for deriving meaningful insights from this data. New

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2113 companies listed on the Libyan Stock Market (XLSM) in Libya. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Libya:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Libya:

Libyan Stock Market Index: The main index that tracks the performance of domestic companies listed on the Libyan Stock Market. This index provides an overview of the overall market performance in Libya.

Foreign Company Index: The index that tracks the performance of foreign companies listed on the Libyan Stock Market. This index reflects the performance of international companies operating in Libya.

Company A: A prominent Libyan company operating in various sectors, such as telecommunications, energy, or banking. This company's stock is widely traded on the Libyan Stock Market.

Company B: A leading financial services provider in Libya, offering banking, insurance, or investment services. This company's stock is actively traded on the Libyan Stock Market.

Company C: A major player in the Libyan agricultural sector, involved in the production and distribution of agricultural products. This company's stock is listed and actively traded on the Libyan Stock Market.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Libya, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Libya ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Libya?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Libya exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive the data?

‍Techsalerato...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 640 companies listed on the Singapore Exchange* (XSES) in Singapore. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Singapore:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Singapore:

Straits Times Index (STI): The main index that tracks the performance of companies listed on the Singapore Exchange (SGX). The STI provides a comprehensive view of the overall market performance in Singapore.

Company A: A prominent Singaporean company in sectors such as finance, real estate, or technology. This company's stock is among the most actively traded on the SGX and influences market trends.

Company B: A major Singaporean bank or financial institution offering a wide range of financial services. This company's stock is significant in the financial sector and reflects the economic landscape of Singapore.

Singapore Telecommunications Limited (Singtel): A leading telecommunications company in Singapore with international operations. The stock of Singtel is actively traded and has an impact on the communication sector.

Keppel Corporation Limited: A conglomerate in Singapore with interests in various sectors such as offshore and marine, real estate, and infrastructure. The stock of Keppel Corporation is a key player in the Singaporean market.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Singapore, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Singapore ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Singapore?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Singapore exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wir...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2,363 public companies listed on the Douala Stock Exchange (XDSX) in Cameroon. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Cameroon:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Cameroon

Douala Stock Exchange (DSX) Composite Index: The main index that tracks the performance of domestic companies listed on the Douala Stock Exchange in Cameroon.

Douala Stock Exchange (DSX) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Douala Stock Exchange.

Société Anonyme des Brasseries du Cameroun (SABC): A leading brewery company in Cameroon with operations in the production and distribution of beverages.

Afriland First Bank Cameroon: A financial services provider with operations in various African countries, offering banking and financial solutions.

Société Nationale des Hydrocarbures (SNH): The National Hydrocarbons Corporation of Cameroon, involved in the exploration, production, and distribution of oil and gas resources.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Cameroon, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Cameroon ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Cameroon?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Cameroon exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive the data?

‍Techsalerator provides the End-of-Day Pricing Data through multiple delivery methods, such as FTP, SFTP, S3 bucket, or email, ensuring easy access and i...

The Medicare Fee-For-Service Public Provider Enrollment dataset includes information on providers who are actively approved to bill Medicare or have completed the 855O at the time the data was pulled from the Provider Enrollment, Chain, and Ownership System (PECOS). The release of this provider enrollment data is not related to other provider information releases such as Physician Compare or Data Transparency. Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.Resources for Using and Understanding the DataThese files are populated from PECOS and contain basic enrollment and provider information, reassignment of benefits information and practice location city, state and zip. These files are not intended to be used as real time reporting as the data changes from day to day and the files are updated only on a quarterly basis. If any information on these files needs to be updated, the provider needs to contact their respective Medicare Administrative Contractor (MAC) to have that information updated. This data does not include information on opt-out providers. Information is redacted where necessary to protect Medicare provider privacy.

View LSEG's ICE Data Pricing and Reference Data, and find real-time market data, time-sensitive pricing, and reference data for securities trading.

Education and training national achievement rate tables.Academic year: 2021/22 to 2022/23Indicators: Achievement rate, Leavers, Pass rate, Retention rateFilters: Provider, Essential skills subject, Essential skills type, Qualification type, Level, Age group

ACR TV Data Providers Market Outlook

According to our latest research, the global ACR TV Data Providers market size reached USD 2.13 billion in 2024, and is projected to grow at a robust CAGR of 15.7% from 2025 to 2033. By the end of 2033, the market is expected to achieve a value of USD 7.15 billion. This substantial growth is primarily driven by the increasing adoption of smart TVs, the proliferation of content streaming platforms, and the rising demand for granular audience insights among advertisers and broadcasters. The ongoing digital transformation in media consumption patterns continues to fuel the need for advanced data analytics, positioning the ACR TV Data Providers market as a pivotal enabler of next-generation media strategies.

One of the primary growth factors for the ACR TV Data Providers market is the exponential rise in smart TV penetration globally. As households increasingly shift from traditional linear TV to connected smart TVs, the volume of data generated through Automatic Content Recognition (ACR) technology has surged. This technology enables the real-time identification of content being viewed, providing invaluable viewership data that is both comprehensive and granular. Media companies and advertisers are leveraging this data to better understand audience behavior, optimize content delivery, and create more targeted advertising campaigns. The ability to capture cross-platform viewing habits, including streaming and on-demand content, has further elevated the value proposition of ACR TV data providers, making them essential partners in the evolving digital media landscape.

Another significant driver is the growing emphasis on data-driven advertising and personalized content experiences. Advertisers are increasingly seeking actionable insights that go beyond traditional demographic data, focusing instead on behavioral and contextual information. ACR TV data providers offer detailed ad exposure data and audience measurement metrics, enabling advertisers to assess the effectiveness of their campaigns in real time and make data-informed decisions. This capability is particularly crucial in a fragmented media environment where viewers consume content across multiple devices and platforms. The integration of ACR data with advanced analytics and artificial intelligence tools allows for the creation of highly personalized content and advertising experiences, leading to improved viewer engagement and higher ROI for brands.

Furthermore, regulatory shifts and industry standards around data privacy and transparency are shaping the evolution of the ACR TV Data Providers market. As governments and regulatory bodies introduce stricter data protection laws, ACR providers are investing in robust data governance frameworks to ensure compliance. This focus on privacy not only builds consumer trust but also encourages broader adoption of ACR-based solutions by major broadcasters, content providers, and advertisers. The ability to deliver anonymized, aggregated insights without compromising user privacy is becoming a key differentiator in the market. As a result, providers that can balance innovation with compliance are expected to capture a larger share of the growing market.

From a regional perspective, North America currently dominates the ACR TV Data Providers market, accounting for more than 38% of the global revenue in 2024. This leadership position is attributed to the high adoption rate of connected TVs, advanced digital infrastructure, and the presence of leading technology and media companies. Europe follows closely, driven by increasing investments in digital advertising and a strong regulatory framework supporting data-driven media solutions. The Asia Pacific region is anticipated to witness the fastest growth over the forecast period, supported by rapid urbanization, rising disposable incomes, and the proliferation of smart devices. As emerging markets in Latin America and the Middle East & Africa continue to digitize, the demand for ACR-based audience measurement and analytics solutions is expected to rise, further expanding the global footprint of the market.

The primary purpose for the Provider Network Data System is to collect data needed to evaluate the provider networks including physicians, hospitals, labs, home health agencies, durable medical equipment providers, etc., for all types of health plans in New York State. Beginning in 2017, the PNDS includes Medicaid Managed Care (MMC), HIV Special Need Plans (SNP), Health and Recovery Plans (HARP), Child Health Plus (CHP), Programs of All-Inclusive Care for the Elderly (PACE), Non-PACE Managed Long-Term Care (MLTC) plans, Qualified Health Plans (QHP), Essential Plans (EP), and commercial plans. This dataset reflects institutional provider data. Provider Network Data System information is self-reported by health plans. The PNDS data dictionary can be found at http://www.health.ny.gov/health_care/managed_care/docs/dictionary.pdf . To use the NYS Provider & Health Plan Look-Up Tool, click on the following link: https://pndslookup.health.ny.gov/ .

The Big Data Basic Platform market is experiencing robust growth, projected to reach a market size of $150 billion by 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key drivers, including the escalating volume and velocity of data generated across various industries, the increasing demand for real-time data analytics, and the growing adoption of cloud-based solutions for data storage and processing. Furthermore, advancements in technologies like artificial intelligence (AI) and machine learning (ML) are creating new opportunities for businesses to leverage big data for improved decision-making and enhanced operational efficiency. The market is segmented across various deployment models (cloud, on-premise, hybrid), industry verticals (finance, healthcare, retail, etc.), and functionalities (data ingestion, storage, processing, analytics). Key players in this competitive landscape include established technology giants like IBM, Microsoft, and AWS, alongside specialized big data solution providers such as Splunk and Cloudera. The market's growth trajectory is expected to remain strong throughout the forecast period, driven by ongoing digital transformation initiatives across enterprises globally. The significant market expansion reflects a confluence of factors. Businesses are increasingly recognizing the strategic value of big data for competitive advantage, leading to significant investments in platform infrastructure and skilled talent. Geographic expansion is also a notable driver, with developing economies witnessing accelerated adoption. However, challenges remain, including the complexities of data integration, security concerns related to sensitive data, and the need for skilled professionals capable of managing and interpreting large datasets. The market is witnessing increasing consolidation through mergers and acquisitions, as companies strive to broaden their service offerings and strengthen their market positions. The emergence of open-source technologies and the ongoing evolution of cloud computing architectures are further shaping the market's competitive dynamics, driving innovation and lowering the barrier to entry for new entrants. Future growth will likely depend on continued technological advancements, increasing data literacy, and the development of robust data governance frameworks.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Health Care Providers Dow Jones U.S. Select Forecast: Steady Growth Expected.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data