In 2023, the leading market research company in terms of global research revenue was Gartner, generating approximately *** billion U.S. dollars. In the same year, American company IQVIA accrued roughly *** billion U.S. dollars. Market research companies - global players Market research is an important business strategy involving the gathering of information about an organization’s target market. In terms of global research revenue, American company Gartner was the leading market research company worldwide. Headquartered in the United States, the number of employees working for the company totaled ****** people in 2023. The sixth largest market research company in terms of global research revenue was Kantar. Kantar Group is a UK-based international network of ** companies, including Millward Brown and IMRB International. In 2022, the research revenue of Kantar amounted to approximately ***** billion U.S. dollars. Another European leader, and ranked seventh in the global ranking, was Ipsos. The company had a research revenue of over *** billion U.S. dollars. The largest share of market research revenue made by Ipsos was generated in Europe, the Middle East and Africa in 2023. United States dominant in a global market The global revenue of the market research industry reached over ** billion U.S. dollars in 2023, over half of which came from the United States. The number of full-time employees working for the top ten market research companies in the United States totaled almost ****** in 2020.

With 1.8 Million Businesses in Sweden , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

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Techsalerator covers all regions and cities in the country :

Blekinge Karlskrona Dalarna Borlänge Falun Gävleborg/Gavleborg Gävle/Gavle Gotland Visby Halland Halmstad Jämtland Östersund/Osterund Jönköping/Jonkoping Jönköping Kalmar Kalmar Kronoberg Växjö/Vaxjo Norrbotten Kiruna Luleå Örebro/Orebro Örebro Östergötland/Ostergotland Linköping/Linkoping Norrköping/Norrkoping Skåne/Skane Helsingborg Kristianstad Landskrona Lund Malmö/Malmo Trelleborg Södermanland/ Sodermanland Eskilstuna Nyköping/ Nykoping Stockholm Södertälje/ Sodertalje Solna Stockholm Uppsala Uppsala Värmland/ Varmland Karlstad Västerbotten/ Vasterbotten Umeå/ Umea Västernorrland/ Vasternorrland Sundsvall Västmanland/ Vastmanland Västerås/ Vasteras Västra Götaland/ Vastra Gotaland Borås/ Boras Gothenburg Lidköping/ Lidkoping Skara

In 2021, Gartner was the leading company in the market research and data analytics sector in the United States. Roughly ** million U.S. dollars separated the top two companies, as Nielsen generated a revenue of approximately *** billion U.S. dollars compared to the **** billion U.S. dollars generated by Gartner.

With 3.4 Million Businesses in Russia , Techsalerator has access to the highest B2B count of Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

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Market research companies have benefited from research and development (R&D) expenditure growth as companies develop new products to satisfy consumer demand. Downstream companies continue to rely on market research to create new products and campaigns that fit evolving consumer preferences. As companies strive to enhance consumer-centric strategies amid increased consumer spending, demand for tailored market research solutions has surged. A 10.7% surge in corporate profit over the past five years enabled businesses to outsource more of their research operations to professional market researchers. The digital shift has further transformed the landscape, with companies pioneering new research tools to tap into the vast potential of big data to enhance accessibility and participation. These trends have led to revenue growing at a CAGR of 3.8% to an estimated $36.4 billion over the past five years, including an estimated 2.1% boost in 2025 alone. Consumers' and advertisers' growing reliance on the internet has led to new metrics market researchers can use to better understand consumers. These have allowed new companies to enter the industry and driven providers to adjust services and implement new technologies. The rising use of social media to advertise and market new products across platforms like TikTok and Instagram also contributed to the growing demand for market research. These technological advancements improved data collection and analysis methods, offering actionable insights that helped companies refine marketing strategies and develop better products. New opportunities continue to drive revenue growth, but expansions to services and onboarding of new technology cut researchers’ profitability. Moving forward, the industry will benefit from acceleration in R&D budgets and technological and a data procurement evolution. Companies will strengthen their R&D budgets as economic conditions improve, further driving demand for advanced market research tools. The proliferation of online commerce and smart technologies will give researchers unprecedented access to consumer data. Technological developments, such as artificial intelligence (AI), are poised to create new metrics based on human reactions, which companies can leverage to better understand consumer behavior and preferences. Access to these metrics, however, will lead to tightening data privacy regulations, which may result in higher compliance costs that eat into profitability. Finally, growing emphasis on ethical practices, transparency and data security will shape consumer trust and research standards, creating new opportunities and challenges in a rapidly evolving marketplace. Revenue is poised to grow at a CAGR of 2.4% to an estimated $41.0 billion through the end of 2030.

Company Reviews and Ratings

This dataset provides detailed information on company reviews and ratings, encompassing various attributes such as the name of the company, its rating, number of reviews, company type, total employees, headquarters location, years in operation, highly rated aspects, and critically rated aspects.

Columns:

• Name_Of_Company : The name of the company being reviewed.
• Rating : The overall rating of the company, typically on a scale of 1 to 5.
• Reviews : The number of reviews available for the company.
• Company_Type : The type or industry of the company.
• Total_Employees : The total number of employees working in the company.
• Head_Quarter : The location of the company's headquarters.
• Years_In_Operation : The number of years the company has been in operation.
• Highly_Rated_For : Aspects of the company that are highly rated by reviewers.
• Critically_Rated_For : Aspects of the company that are critically rated by reviewers.

Description:

This dataset offers insights into the performance and perception of various companies across different industries. Analysts can use this data to understand trends in company ratings, identify factors influencing positive or negative reviews, and benchmark companies against their competitors.

Potential Use Cases:

• HR and Recruitment : HR professionals can utilize this data to assess the reputation and employee satisfaction of potential employers.

• Investors : Investors can analyze company ratings and reviews to inform investment decisions and evaluate the financial health of companies.

• Market Research : Market researchers can study consumer sentiment towards different companies and industries to guide marketing strategies and product development.

• Business Development : Business development teams can identify areas of improvement based on critical reviews and prioritize efforts to enhance customer satisfaction.

• Academic Research : Researchers can explore this dataset to study patterns in company ratings and reviews, and investigate the impact of various factors on company performance and perception.

By leveraging this dataset, stakeholders can gain valuable insights into the strengths and weaknesses of companies, enabling informed decision-making and strategic planning.

With 1.9 Million Businesses in Hong Kong , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all regions and cities in Hong Kong : ( example) Kowloon Northern Hong Kong Island Tsuen Wan New Town Sha Tin New Town Tuen Mun New Town Tseung Kwan O New Town Aberdeen Tai Po New Town Tin Shui Wai New Town Fanling-Sheung Shui New Town Yuen Long New Town North Lantau New Town

B Corporation Impact Data

Social and environmental impact data of Certified B Corporations worldwide

By B Lab [source]

About this dataset

The Certified B Corporations Impact Data dataset provides valuable information on the social and environmental impact of certified B Corporations worldwide. B Corps are businesses that meet high standards of verified performance, accountability, and transparency in areas such as employee benefits, charitable giving, supply chain practices, and input materials. This certification signifies a commitment to an inclusive, equitable, and regenerative economy.

The dataset includes data from over 5,000 certified B Corps located across various industries around the world. It offers insights into the impact these companies have on society and the environment. The data is derived from the comprehensive B Impact Assessment which measures a company's entire social and environmental performance.

This dataset is a valuable resource for analyzing and understanding how B Corps contribute to positive change in business practices. Whether you're interested in researching sustainability efforts or evaluating companies' societal contributions, this dataset provides detailed information on various impact topics such as governance practices, community engagement, employee well-being, energy efficiency, waste management initiatives, diversity initiatives among others.

By examining this data set researchers will be able to explore each individual Impact Area's subtopics known as Impact Topics and identify patterns or trends within specific industries or geographical regions.

It is important to note that changes have been made to this version of the dataset based on user feedback. For instance decertified companies have been added to allow continued research on companies that were once certified but are no longer active participants in the program.

To facilitate analysis using this dataset effectively it may be helpful for analysts/researchers/scholars/individuals explore a sample B Impact Report provided by bcorporation.net/certification as well familiarize themselves with our hierarchy per question structure starting with questions falling under an respective impact area section eventually specifics can classify those questions into more granular impact topics. Please note that while we provide scores for all Certified B Corps within these Impact Topics/areas unfortunately access to company-specific answers for each question is not disclosed.

In conclusion, this dataset serves as a valuable resource for understanding the impact of B Corps on society and the environment. It enables researchers, analysts, and individuals interested in sustainable business practices to explore and analyze the performance of certified B Corporations across various impact areas and topics

How to use the dataset

How to Use This Dataset:

• Understand Certified B Corporations: Familiarize yourself with the concept of Certified B Corporations and their mission. Visit the B Corporation website to learn more about what it means to be a certified B Corp.

• Explore Data Columns: Review the available data columns in the dataset provided. Each column contains specific information related to impact areas, topics, and individual questions.

• Analyze Impact Areas: Impact areas represent broad categories that encompass various impact topics. Begin by examining the different impact areas in order to gain an overview of the diverse areas in which B Corps have demonstrated their social and environmental performance.

• Review Impact Topics: Dive deeper into each impact area by exploring the associated impact topics within them. These topics provide detailed insights into specific aspects of a company's operations, such as employee benefits, supply chain practices, environmental stewardship, community engagement, governance practices, etc.

• Evaluate Scores: The dataset includes scores for each certified B Corp within each impact topic and area but does not provide individual company answers or responses. Assess these scores carefully as they reflect how well organizations are performing in different dimensions of their social and environmental responsibilities.

• Identify Trends & Patterns: Utilize data visualization techniques or statistical analysis methods to identify trends or patterns across companies within specific industries or geographic regions based on their scores for different impact areas/topics.

• Compare Companies: Compare selected companies' scores with others that operate within similar sectors or regions; this can help identify indust...

The Sea-Based C4ISR Market is Segmented by Type (Command and Control, ISR, Communication, Computer, and Electronic Warfare) and Geography (North America, Europe, Asia-Pacific, Middle-East and Africa, and the Rest of the World). The Report Offers Market Size and Forecast for all the Above Segments in Value (USD).

Market researchers investigate clients' target markets' behaviour, values and opinions, providing insights that allow them to tailor their products, services and marketing. Researchers rely on hefty European research and development expenditure to fuel demand for market research. The surge in digitalisation has opened new doors for market research providers while intensifying competition. Artificial intelligence is increasingly important in analysing, identifying and generating research insights from social media posts using a flood of data. Meanwhile, digital surveys have allowed research companies to expand their outreach, save resources and costs and often attain more accurate and comprehensive insights for clients. Over the five years through 2025, industry revenue is expected to contract at a compound annual rate of 1.1% to reach €25.2 billion. The high inflationary environment in recent years has taken a toll on market research budgets. A sharp contraction in business sentiment squeezed corporate profit in 2022, discouraging companies from investing in research and development activities and negatively affecting professional research providers. A greater availability of data and alternative research methods means that researchers are competing more and more with in-house research departments. In 2025, industry revenue is expected to drop by 0.3% as consumers are finding their research needs met by AI tools such as ChatGPT, however, this trend is expected to be short-lived as research companies will strive to prove their value to clients. Over the five years through 2030, industry revenue is forecast to swell at a compound annual rate of 3.7% to reach €30.3 billion. Over the coming years, market research companies will face higher external competition from technology specialists leveraging insights internally, constraining revenue growth. Nonetheless, researchers will benefit from expanding online advertising activity. Those incorporating advanced data analytics systems and digital market research technology will remain competitive and benefit from greater digitalisation. Smart mobile surveys will also become an invaluable tool for consumer research companies.

US Startup companies over time (Timeseries)

Startup companies in the US from 2008

About this dataset

The dataset contains information on startup companies in the US from 2008, including company name, location, team size, number of founders, and other relevant information. This data can be used to empower the next wave of entrepreneurs by providing insights on what types of startups are being founded, where they are located, and how large their teams are. Additionally, this dataset can be used to understand trends in the startup industry over time.

Research Ideas

• Study the trend of startups over time.
• Track the number of startups in each industry and compare companies and industries.
• Identify geographies with a high concentration of startups for recruiting purposes.
• Map the development cycle of a startup from ideation to successful exit.

Acknowledgements

The dataset was first posted here

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: company.csv | Column name | Description | |:-----------------------|:---------------------------------------------------------------------------| | company_name | The name of the startup company. (String) | | link | A link to the company's website. (String) | | short_description | A short description of the company. (String) | | founded | The year the company was founded. (Integer) | | team_size | The number of people on the company's team. (Integer) | | location | The city where the company is located. (String) | | country | The country where the company is located. (String) | | no_founders | The number of founders of the company. (Integer) | | no_company_socials | The number of social media accounts associated with the company. (Integer) | | no_tags | The number of tags associated with the company. (Integer) |

File: company_social_to_social.csv

File: company_to_tag.csv

File: founder.csv | Column name | Description | |:--------------|:------------------------------------------------| | name | The name of the startup company. (String) | | role | The role of the person in the company. (String) |

File: founder_social_to_social.csv

File: socials.csv | Column name | Description | |:--------------|:-----------------------------------------------------| | social | A list of social media links for the company. (List) |

File: tags.csv | Column name | Description | |:--------------|:------------------------------------------| | tag | The name of the startup company. (String) |

File: ycombinator_all.csv | Column name | Description | |:-----------------------|:---------------------------------------------------------------------------| | company_name | The name of the startup company. (String) | | link | A link to the company's website. (String) | | short_description | A short description of the company. (String) | | founded | The year the company was founded. (Integer) | | team_size | The number of people on the company's team. (Integer) | | location | The city where the company is located. (String) | | no_founders | The number of founders of the company. (Integer) | | no_company_socials | The number of social media accounts associated with the company. (Integer) | | no_tags | The number of tags associated with the company. (Integer) | | country | The country where the company is located. (String) |

**File: ...

Global Customer Success Platforms Market size was valued at USD 17.92 Billion in 2024 and is projected to reach USD 93.50 Billion by 2031 growing at a CAGR of 25.30% from 2024 to 2031.

The Customer Success Platforms market is driven by several key factors, including the growing emphasis on customer retention and expansion, which is critical for subscription-based businesses and SaaS companies. Companies are increasingly investing in these platforms to enhance customer experience, reduce churn, and increase lifetime value. The rise of digital transformation initiatives, coupled with the need for real-time customer insights and personalized engagements, further accelerates the adoption of these platforms. Additionally, the integration of AI and machine learning in Customer Success Platforms is providing advanced analytics and predictive capabilities, making these solutions more effective and attractive to organizations. As businesses continue to shift toward customer-centric models, the demand for robust Customer Success Platforms is expected to grow significantly.

Direct-selling companies retail a range of products from one person to another away from a fixed retail location. The COVID-19 outbreak caused a substantial shift in the industry, as mass layoffs propelled industry participation levels, resulting in heightened performance. However, intense competition from big-box retailers and e-commerce has pressured the industry, as competitors can offer a wider selection of substitute products at lower prices and in a convenient one-stop location. Direct sellers have embraced innovative sales strategies and digital platforms to maintain growth. Direct selling revenue is expected to climb at a CAGR of 5.0% to $75.2 billion through the end of 2025, including growth of 2.3% in 2025 alone. Profit will also improve as rising per capita disposable income levels improve spending on high-priced goods. Direct-selling companies have relatively low start-up costs and some unemployed or underemployed Americans establish direct-selling businesses as a means of income. As the unemployment rate fluctuated but ultimately climbed in recent years, more enterprises entered the industry. As demand and direct sellers' revenue rose, more businesses entered the industry to use it as a flexible, low-commitment way to earn supplemental income. The health and wellness segment has boomed, with consumers seeking natural and sustainable products. This shift has fueled sales of nutritional supplements and skincare products. Direct sellers have harnessed social media to reach wider audiences, creating personal connections that resonate with consumers. Positive economic trends, like rising consumer confidence and spending, will contribute to rising revenue for direct-selling companies in the coming years. However, rising incomes and consumer spending will also lead many consumers to shop at substitute industries, like mass retailers and online competitors. As e-commerce continues to expand, direct sellers will further integrate digital tools and platforms to enhance customer engagement and streamline sales processes. Artificial intelligence and data analytics will enable companies to fine-tune marketing strategies, personalize shopping experiences and optimize inventory management. Sustainability will continue to be a critical focus, with consumers demanding greater transparency and environmentally friendly practices. Regulatory scrutiny remains a wildcard, as the industry must navigate potential challenges to ensure ethical practices and the protection of both consumers and sellers. Revenue is expected to expand at a CAGR of 3.0% to $87.0 billion through the end of 2030.

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

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➡️ Multiple APIs designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

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Coresignal's employee and company data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including investment, sales, and HR technology.

✅ For investors

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal's global Employee Data and Company Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase

✅ For HR tech

Coresignal's global Employee Data and Company Data enable you to build and improve AI-based talent-sourcing and other HR technology solutions.

Use cases

1. Build AI-based tools
2. Find qualified candidates
3. Enrich existing hiring data

✅ For sales tech

Companies use our large-scale datasets to improve their lead generation engines and power sales technology platforms.

Use cases

1. Extract targeted lead lists
2. Fill in the gaps in your lead data
3. Enable data-driven sales strategies

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

With 1.3 Million Businesses in Finland , Techsalerator has access to the highest B2B count of Data/Business Data in the country.

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

Province of Nyland and Tavastehus Uudenmaan ja Hämeen lääni Nylands och Tavastehus län Helsinki / Hämeenlinna

Province of Ostrobothnia Pohjanmaan lääni Österbottens län Oulu / Vaasa

Province of Viborg and Nyslott Viipurin ja Savonlinnan lääni Viborgs och Nyslotts län Vyborg

Province of Kexholm Käkisalmen lääni Kexholms län Kexholm

Province of Kymmenegård and Nyslott Savonlinnan ja Kymenkartanon lääni Kymmenegårds och Nyslotts län Lappeenranta

Province of Savolax and Kymmenegård Kymenkartanon ja Savon lääni Savolax och Kymmenegårds län Loviisa

Province of Vaasa Vaasan lääni Vasa län Vaasa

Province of Oulu Oulun lääni Uleåborgs län Oulu

Province of Kymmenegård Kymenkartanon lääni Kymmenegårds län Heinola

Province of Savolax and Karelia Savon ja Karjalan lääni Savolax och Karelens län Kuopio

Province of Viipuri Viipurin lääni Viborgs län Vyborg

Province of Uusimaa Uudenmaan lääni Nylands län Helsinki

Province of Häme Hämeen lääni Tavastehus län Hämeenlinna

Province of Mikkeli Mikkelin lääni St. Michels län Mikkeli

Province of Kuopio Kuopion lääni Kuopio län Kuopio

Province of Åland Ahvenanmaan lääni Ålands län Mariehamn

Province of Petsamo Petsamon lääni Petsamo län Pechenga

Province of Lapland Lapin lääni Lapplands län Rovaniemi

Province of Kymi Kymen lääni Kymmene län Kouvola

Province of Central Finland Keski-Suomen lääni Mellersta Finlands län Jyväskylä

Province of North Karelia Pohjois-Karjalan lääni Norra Karelens län Joensuu

Province of Southern Finland Etelä-Suomen lääni Södra Finlands län Hämeenlinna

Province of Western Finland Länsi-Suomen lääni Västra Finlands län Turku

Province of Eastern Finland Itä-Suomen lääni Östra Finlands län Mikkeli

Market researchers investigate clients' target markets' behaviour, values and opinions, providing insights that allow them to tailor their products, services and marketing. Researchers rely on hefty European research and development expenditure to fuel demand for market research. The surge in digitalisation has opened new doors for market research providers while intensifying competition. Artificial intelligence is increasingly important in analysing, identifying and generating research insights from social media posts using a flood of data. Meanwhile, digital surveys have allowed research companies to expand their outreach, save resources and costs and often attain more accurate and comprehensive insights for clients. Over the five years through 2025, industry revenue is expected to contract at a compound annual rate of 1.1% to reach €25.2 billion. The high inflationary environment in recent years has taken a toll on market research budgets. A sharp contraction in business sentiment squeezed corporate profit in 2022, discouraging companies from investing in research and development activities and negatively affecting professional research providers. A greater availability of data and alternative research methods means that researchers are competing more and more with in-house research departments. In 2025, industry revenue is expected to drop by 0.3% as consumers are finding their research needs met by AI tools such as ChatGPT, however, this trend is expected to be short-lived as research companies will strive to prove their value to clients. Over the five years through 2030, industry revenue is forecast to swell at a compound annual rate of 3.7% to reach €30.3 billion. Over the coming years, market research companies will face higher external competition from technology specialists leveraging insights internally, constraining revenue growth. Nonetheless, researchers will benefit from expanding online advertising activity. Those incorporating advanced data analytics systems and digital market research technology will remain competitive and benefit from greater digitalisation. Smart mobile surveys will also become an invaluable tool for consumer research companies.

The Office of the Privacy Commissioner of Canada (OPC) commissioned Phoenix Strategic Perspectives (Phoenix SPI) to conduct quantitative research with Canadian businesses on privacy‐related issues. To address its information needs, the OPC conducts surveys with businesses every two years to inform and guide outreach efforts. The objectives of this research were to collect data on the type of privacy policies and practices businesses have in place; on businesses’ compliance with the law; and on businesses’ awareness and approaches to privacy protection. The findings will be used to help the OPC provide guidance to both individuals and organizations on privacy issues; and enhance its outreach efforts with small businesses, which can be an effective way to achieve positive change for privacy protection. A 13‐minute telephone survey was administered to 1,003 companies across Canada between November 29 and December 19, 2019. The target respondents were senior decision makers with responsibility and knowledge of their company’s privacy and security practices. Businesses were divided by size for sampling purposes: small (1 to 19 employees); medium (20 to 99 employees); and large (100 employees or more). The results were weighted by size, sector and region using Statistics Canada data to ensure that they reflect the actual distribution of businesses in Canada. Based on a sample of this size, the results can be considered accurate to within ±3.1%, 19 times out of 20.

The graph shows internal expenditures on research and development (R&D) of industrial enterprises in China until 2024. In 2024, industrial companies invested about **** trillion yuan in R&D.

Global IT Jobs Analysis

A Detailed Investigation of Salary, Location, and Job Requirements

By [source]

About this dataset

This dataset contains valuable insights into current job opportunities in the information technology (IT) sector all around the world. It offers an overview of available jobs and relevant data such as company, location, salary and links to further information. With this insight, one has the chance to better understand what it takes to land a remote or data-science job in today's global market. The ever increasing demand for IT workforce puts technical skills at a premium, so understanding exactly what employers are searching for can give potential employees an edge in catching the eye of these businesses! Digging through this dataset can provide details on current trends in terms of salary expectations and geographical locations where these roles are most popular. Beyond that, get an idea about which abilities seem most valuable when it comes to remote or data-science positions. Use this arsenal of knowledge to take your career goals into your own hands now!

More Datasets

For more datasets, click here.

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• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an opportunity to explore the remote and data-science job opportunities around the world. Using this dataset, you can analyze trends in job requirements, salary packages offered, location of available jobs and more. With the knowledge gained from this data set, individuals and companies can make more informed decisions about pursuing a certain path in their career or hiring for their business.

The dataset includes columns with important information such as Job title, Company offering the job, Location of the position , Salary offered for that position and a Link to its respective posting. Using these columns you can analyze various factors regarding global IT Jobs availability over different locations in alignment with salary offered for positions and any specific skill sets sought out by companies .

To get executable insights from this data set users should first load it into their respective computing environment (Python or R). After loading it in your environment users should start off by exploring Groupby statements along factors like Companies offering jobs ,Salary offered ,Location etc. followed by descriptive statistics like mean & median of Salary Levels per country/region etc. After getting basic insight about summary statistics for various factors belonging all together within “Job” range user could move forward to look over individual cases (specific skill sets) after which they could filter out & generate valueable insights needed .

With our comprehensive understanding of global supply & demand rates individuals/corporations could always use these datasets to help them keep track on talent acquisition landscape when they hire globally or relocating teams as companies who need such information would greatly benefit from versatile tools like this one that offer valuable actionsable insights on an ongoing basis depending upon dayers choosing!

Research Ideas

• Identifying the most in-demand skills and employment requirements for remote data science and IT jobs, across different countries and regions.
• Developing a prediction model to forecast future salary expectations for data science professionals based on location, company, job type, etc.
• Building an interactive dashboard with visualizations showing differences in job requirements (by level of experience or education), salary comparison across geographies as well as potential career paths one can pursue within the IT or Data Science fields

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Job_listing.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Job | The title of the job listing. (String) | | Company | The name of the company offering the job. (String) | | Location | The geographic location of the job. (String) | | Salary | The salary offere...