H-1B visa sponsorship trends for Technology Analyst Us, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

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.

Tech Sector Poised for Moderate Growth, Analysts Predict for Dow Jones U.S. Technology Index.

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

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for analyst in information technology in the U.S.

A Civil Service List consists of all candidates who passed an exam, ranked in score order. An established list is considered active for no less than one year and no more than four years from the date of establishment. For more information visit DCAS’ “Work for the City” webpage at: http://www.nyc.gov/html/dcas/html/work/work.shtml

Techsalerator’s Business Technographic Data for North America is an invaluable resource designed to provide businesses, market analysts, and technology vendors with a comprehensive understanding of the technological landscape across North America. This dataset offers an in-depth examination of the technology ecosystems within companies operating in the region, offering a granular view into their technology stacks, digital tools, and IT infrastructure.

Key Features of the Dataset: Technology Stacks:

Detailed information on the technology stacks used by companies, including software, hardware, and platforms. This encompasses data on programming languages, frameworks, databases, cloud services, and enterprise applications that companies rely on. Digital Tools:

Insight into the digital tools and services adopted by businesses, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) solutions, collaboration tools, and marketing automation platforms. IT Infrastructure:

Data on the IT infrastructure of companies, including their network setups, server environments, data storage solutions, and cybersecurity measures. This also covers information on on-premises versus cloud-based infrastructure. Technological Trends:

Analysis of emerging technological trends and innovations being adopted across different sectors and regions. This helps in identifying shifts in technology usage and areas of growth within the North America market. Sector and Regional Breakdown:

Segmentation of data by industry sectors and geographic regions, providing insights into how technology adoption varies across different industries and North America countries.

North Countries Covered: Afghanistan Armenia Azerbaijan Bahrain Bangladesh Bhutan Brunei Cambodia China Cyprus Georgia India Indonesia Iran Iraq Israel Japan Jordan Kazakhstan Kuwait Kyrgyzstan Laos Lebanon Malaysia Maldives Mongolia Myanmar (Burma) Nepal North Korea Oman Pakistan Palestine Philippines Qatar Saudi Arabia Singapore South Korea Sri Lanka Syria Taiwan Tajikistan Thailand Timor-Leste (East Timor) Turkey Turkmenistan United Arab Emirates Uzbekistan Vietnam Yemen

Benefits of the Dataset: Strategic Insights: Businesses can leverage the data to make informed decisions about technology investments, partnerships, and competitive strategies based on a thorough understanding of the technology landscape. Market Analysis: Market analysts can use the data to identify trends, benchmark companies, and assess the technological capabilities of different sectors and regions. Vendor Strategy: Technology vendors can gain insights into the technology stacks and tools being used by potential clients, allowing them to tailor their offerings and sales strategies effectively. Techsalerator’s Business Technographic Data for North America equips stakeholders with essential information to navigate the complex technological environment of North America businesses, enabling more strategic and data-driven decisions.

Analyst Viewpoint

The green technology market is on a robust growth path, driven by government support, rising environmental awareness, and the push for energy efficiency. Analysts observe that the adoption of technologies like IoT in smart homes, green buildings, and electric vehicles is propelling growth. However, the high initial investment cost remains a barrier for some markets.

North America continues to lead in adoption, but emerging economies in Asia-Pacific are quickly catching up due to rapid urbanization. As governments continue to push for cleaner energy solutions, the market will see accelerated growth, especially in sectors such as renewable energy.

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Analysis of ‘ONC Health Information Technology for Economic and Clinical Health (HITECH) Grantee Crosswalk’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0bef4b0e-8f2b-4046-8f8f-bace7f2c467c on 11 February 2022.

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

The Health Information Technology for Economic and Clinical Health (HITECH) Act was passed as part of the American Recovery and Reinvestment Act (ARRA) to invest in the U.S. health IT infrastructure. The Office of the National Coordinator for Health IT (ONC) received over $2 billion of these HITECH funds, which was granted to health and community organizations across the U.S. This crosswalk provides geographic data for the service areas of two of these HITECH programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This data can be linked to program financial and performance data to map and visualize program data. You can access the data in multiple formats below.

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

Techsalerator’s Business Technographic Data for Latam is an invaluable resource designed to provide businesses, market analysts, and technology vendors with a comprehensive understanding of the technological landscape across Latam. This dataset offers an in-depth examination of the technology ecosystems within companies operating in the region, offering a granular view into their technology stacks, digital tools, and IT infrastructure.

Key Features of the Dataset: Technology Stacks:

Detailed information on the technology stacks used by companies, including software, hardware, and platforms. This encompasses data on programming languages, frameworks, databases, cloud services, and enterprise applications that companies rely on. Digital Tools:

Insight into the digital tools and services adopted by businesses, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) solutions, collaboration tools, and marketing automation platforms. IT Infrastructure:

Data on the IT infrastructure of companies, including their network setups, server environments, data storage solutions, and cybersecurity measures. This also covers information on on-premises versus cloud-based infrastructure. Technological Trends:

Analysis of emerging technological trends and innovations being adopted across different sectors and regions. This helps in identifying shifts in technology usage and areas of growth within the Latin America market. Sector and Regional Breakdown:

Segmentation of data by industry sectors and geographic regions, providing insights into how technology adoption varies across different industries and Latin America countries.

Latin America Countries Covered: South America: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama

Benefits of the Dataset: Strategic Insights: Businesses can leverage the data to make informed decisions about technology investments, partnerships, and competitive strategies based on a thorough understanding of the technology landscape. Market Analysis: Market analysts can use the data to identify trends, benchmark companies, and assess the technological capabilities of different sectors and regions. Vendor Strategy: Technology vendors can gain insights into the technology stacks and tools being used by potential clients, allowing them to tailor their offerings and sales strategies effectively. Techsalerator’s Business Technographic Data for Latin America equips stakeholders with essential information to navigate the complex technological environment of Asian businesses, enabling more strategic and data-driven decisions.

Intel's stock faces a pullback as analysts express skepticism over potential deals with TSMC and Broadcom, emphasizing logistical and regulatory challenges.

Analysis of ‘US, Romania, Jamaica and India - META tool: Inputs Transmission Distribution’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/search/dataset/0041744/ on 20 November 2021.

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

Contains information about each delivery technology for US, Romania, Jamaica and India. base year: 2010.

This information is used as input in the Model for Electricity Technology Assessment (META) tool.

META yields levelized costs for generation, transmission, and distribution for each electricity supply technology option from a relatively few input parameters. One of the features of META is that it allows for integration of environmental externalities, such as local pollution and greenhouse gas emissions. Users can easily see the cost of adding or expanding generation from a particular power source if, for example, a carbon price is factored in.

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

Analyst Viewpoint

The Adaptive AI market is transitioning from niche to mainstream adoption at an unprecedented rate. Its real-time learning capabilities are proving invaluable in fast-paced environments where static algorithms fall short. As industries shift towards hyper-automation, AI-driven decision-making is no longer optional but essential.

The technology's adaptive nature allows businesses to pivot strategies in real time—an imperative in today’s volatile markets. Analysts anticipate robust adoption across not just BFSI, but also healthcare, manufacturing, and retail as enterprises look to outpace disruption and harness AI for predictive, personalized, and autonomous decision-making.

Techsalerator’s Business Technographic Data for Oceania is an invaluable resource designed to provide businesses, market analysts, and technology vendors with a comprehensive understanding of the technological landscape across Oceania. This dataset offers an in-depth examination of the technology ecosystems within companies operating in the region, offering a granular view into their technology stacks, digital tools, and IT infrastructure.

Key Features of the Dataset: Technology Stacks:

Detailed information on the technology stacks used by companies, including software, hardware, and platforms. This encompasses data on programming languages, frameworks, databases, cloud services, and enterprise applications that companies rely on. Digital Tools:

Insight into the digital tools and services adopted by businesses, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) solutions, collaboration tools, and marketing automation platforms. IT Infrastructure:

Data on the IT infrastructure of companies, including their network setups, server environments, data storage solutions, and cybersecurity measures. This also covers information on on-premises versus cloud-based infrastructure. Technological Trends:

Analysis of emerging technological trends and innovations being adopted across different sectors and regions. This helps in identifying shifts in technology usage and areas of growth within the Oceania market. Sector and Regional Breakdown:

Segmentation of data by industry sectors and geographic regions, providing insights into how technology adoption varies across different industries and Oceania countries.

Oceania Countries Covered: Countries: Australia Fiji Kiribati Marshall Islands Micronesia (Federated States of) Nauru New Zealand Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu Territories: American Samoa (U.S. territory) French Polynesia (French overseas collectivity) Guam (U.S. territory) New Caledonia (French special collectivity) Northern Mariana Islands (U.S. territory) Wallis and Futuna (French overseas collectivity)

Benefits of the Dataset: Strategic Insights: Businesses can leverage the data to make informed decisions about technology investments, partnerships, and competitive strategies based on a thorough understanding of the technology landscape. Market Analysis: Market analysts can use the data to identify trends, benchmark companies, and assess the technological capabilities of different sectors and regions. Vendor Strategy: Technology vendors can gain insights into the technology stacks and tools being used by potential clients, allowing them to tailor their offerings and sales strategies effectively. Techsalerator’s Business Technographic Data for Oceania equips stakeholders with essential information to navigate the complex technological environment of Africa businesses, enabling more strategic and data-driven decisions.

As per this detailed industry analysis done out by experienced analysts at Fact.MR, the global bioprocess technology market is approximated at US$ 30.7 billion in 2024. Worldwide demand for bioprocess technologies is projected to increase at a CAGR of 14.8% and reach a market value of US$ 121.9 billion by 2034.

Country-wise Analysis

Category-wise Analysis

Analyst Viewpoint

The Agentic AI in Law Enforcement & Surveillance Market is rapidly growing, fueled by increasing government and organizational investment in AI-driven solutions to enhance security, crime prevention, and operational efficiency. Natural Language Processing (NLP) is becoming a critical tool in improving communication, voice analysis, and data interpretation in real time.

Analysts expect continued growth, especially in North America, where regulatory support and technological advancements in surveillance infrastructure are prevalent. As AI becomes more integrated into law enforcement, both on-premises and cloud-based solutions will continue to evolve to address security concerns and optimize crime-fighting efforts.

➤ Want valuable market insights? Request a sample of our latest research today @ https://market.us/report/agentic-ai-in-law-enforcement-and-surveillance-market/free-sample/

The U.S. Private 5G Network Market Size Was Worth USD 3 Billion in 2023 and Is Expected To Reach USD 15 Billion by 2032, CAGR of 24%.

The global digital twin technology market is anticipated to expand at an outstanding CAGR of 22.7% during the forecast period. The market is projected to be worth US$ 89,145.1 million by 2033, up from US$ 11,494.0 million in 2023.

Report Scope

Analyst Viewpoint

The global image sensor market is on a transformative path, driven by automotive innovation, industrial automation, and evolving AI applications. While 2D sensors currently lead, the emergence of 3D imaging solutions marks a critical shift toward higher intelligence and depth processing.

Analysts expect the increasing role of ADAS, robotics, and computer vision to fuel demand for 3D sensors in the near future.

Additionally, strategic collaborations among key players and tech giants are enabling faster innovation cycles. With Asia-Pacific at the helm of manufacturing and R&D, the market is well-positioned to capitalize on future technology demands across verticals.

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Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

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Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

Analysis of ‘US Figure Skating Data - 2020 Regionals Int Ladies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/katiewerner/us-figure-skating-data-2020-regionals-int-ladies on 28 January 2022.

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

Context

US Figure Skating hosts several competitions at the regional level; top skaters from these competitions progress to the next echelon of competition. Depending on the region, there may be qualifying events before the final round to determine the top skaters. In a qualifying round, skaters perform their free skate program for a panel of judges; top qualifiers move on to the final round. The final round consists of each skater performing a short program and their free skate program; the official panels are not necessarily the same between the short program and free skate program events (likewise for qualifying rounds).

Skaters receive scores based on: technical elements, program components, and any deductions incurred. Technical elements performed are assessed by the technical officials on the panel of officials, and the element is assigned a base value. This base value has several considerations beyond just the element performed - for example, if a jump is under-rotated this affects the base value, and if the skater performs an element that receives a bonus, the bonus is added to the base value. The judges on the panel of officials then assign each technical element a grade of execution mark, which may be integers in the range from -5 to +5. The base value is then scaled by the average GOE, dropping the highest and lowest GOEs.

Judges on the official panel are also responsible for assigning program component scores, which consider interpretation of the music, program choreography/composition, performance, transitions between and going into the technical elements, and overall skating skills. Some skating levels have scores for some of the components. Component marks are adjusted based on removing high and low scores, and also adjusted by a set of scalar multipliers, as provided by US Figure Skating.

Deductions may be incurred for time violations, falls, or costume/prop issues. Fall deductions may be more severe depending on the level.

Content

This dataset packaging includes 5 csv files that contain the scraped data from the 2020 Regional Intermedaite Ladies events: - Event Details (event index, round, program type) - Official Details (judge and technical official names, cities) - Skater Details (skater placement, skate order, club, overall scores and deductions) - Technical Score Details (base values, GOEs) - Component Score Details (component scores)

The event index and skater placement columns are keys that can be used to join these datasets together, as appropriate.

Acknowledgements

Many thanks to my husband, for encouraging me to tackle this project during my personal sabbatical. Special shoutout to James Robertson and Joshua Merry for giving this dataset publication a quick once over.

As ever, I also owe thanks to stackoverflow for inspiring code solutions when all hope is seemingly lost.

Photo credit: Greg Pembroke

Inspiration

I created this dataset as simply a fun data project, being data I am intimately familiar and fascinated with the creation of -- indeed, I helped create this data back in October 2019 by serving as a judge on some of these panels! I intend to publish another notebook containing EDA and some models in the near future - stay tuned!

The scripts and notebooks provided are strictly intended to serve as an example to demonstrate my approach to working with data. It is ABSOLUTELY NOT intended to be used to draw any definitive conclusions about: the sport of figure skating, the judges, the technical officials, the skaters, the coaches, the skating clubs, US Figure Skating, any region or section of US Figure Skating, etc.

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