There is an increasing need for analysing demand for skills at labour markets. While most studies aggregate skills in groups or use available proxies for them, the authors analyse companies’ demand for individual competences. Such an analysis better reflects reality, because companies usually require from future workers particular competences rather than generally defined groups of skills. However, no method exists to analyse on a large scale which competences are required by employers. At a detailed level, there are hundreds of competences, so this demand cannot be measured in a sample survey. The authors propose a method for a continuous and efficient analysis of demand for new workers’ competences. The method is based on gathering internet job offers and analysing them with data mining and text analysis tools. They applied it to analyse transversal competences on a Polish labour market during November 2012–December 2015. The authors used the detailed European Commission classification of transversal competences. They found that within the general groups of competences, companies required only certain ones, especially ‘language and communication competences’ and neglected others. The companies’ requirements were countercyclical, that is, they increased them during recession and decreased them during economic expansion. However, the structure of the demanded competences did not change during the analysed period, suggesting that the structure is relatively stable, at least over the business cycle. The method can be used continuously. Various institutions can analyse and publish up-to-date information on the current demand for competences as well as tendencies in this demand.

In 2023, the tech skill most in demand by recruiters was web development. This was closely followed by DevOps and database software skills. Interestingly, over ** percent of recruiters were actively seeking individuals with cybersecurity skills. Not far behind, AI/Machine learning/Deep learning ranked fourth, with approximately ** percent of respondents identifying it as their most sought-after tech skill. These preferences align with the skills that developers worldwide are keen to acquire, particularly web development and AI/Machine learning/Deep learning. AI at the forefront of IT skills Since the release of ChatGPT in late 2022, demand for AI and automation skills has increased across all sectors. In 2023, ChatGPT was the leading technology skill globally according to topic consumption on Udemy Business, experiencing a massive growth of over ***** percent in global topic consumption. In the same year, over ** percent of software developers reported using AI to help write code in the development workflow, while another ** percent said they currently use it for debugging code. Different languages for different needs JavaScript and Java, commonly used for back-end and front-end web development, were the most demanded programming languages worldwide in 2022, followed by SQL and Python. By industry, JavaScript and Java hold the fort in the IT services and aviation industries, while SQL was more popular in the healthcare sector as well as the marketing and advertising industries. Python, well suited for data science applications, was more commonly used in the manufacturing, education, and energy industries.

In 2024, ChatGPT Assistants API unsurprisingly emerged as the most proficient skill among artificial intelligence (AI) engineers worldwide, reporting an intermediate-level average score of ** in DevSkiller assessments. This suggests that while AI development is advancing, the overall proficiency of even the top skills might be influenced by the relatively recent surge of interest in AI development. Azure Databricks and AI-code assistant Tabnine both scored **, respectively. Notably, ethical AI usage scored around **, reflecting a growing awareness and emphasis on responsible AI practices. Arrival of AI disrupts IT skills The advent of AI recently has led companies to rapidly adopt and integrate AI technologies into their business operations. Therefore, it comes as no surprise that AI/machine learning and data analytics were the IT skills expected to see the highest increase in demand globally in 2023, driven by the growing adoption of generative artificial intelligence. AI penetrates all IT tasks Using artificial intelligence as a product is not the only application in the tech world. In fact, it has become an integral tool for developers in their daily work. As of 2024, a significant majority of developers, approximately ** percent, reported actively using AI tools to assist in writing code. This trend extends beyond just coding, with developers leveraging AI technology for searching answers to their queries, debugging, and documenting code among a wide range of applications.

The data science software market is experiencing robust growth, driven by the increasing adoption of big data analytics across diverse industries. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors. The proliferation of cloud-based solutions offers scalability and cost-effectiveness, attracting both large enterprises and small and medium-sized businesses (SMEs). Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are integrating seamlessly into data science platforms, enhancing analytical capabilities and fostering wider adoption. The growing demand for data-driven decision-making across sectors like healthcare, finance, and retail is a significant driver. However, the market also faces challenges. Data security concerns and the need for skilled data scientists to effectively utilize these tools represent significant restraints. The market is segmented by application (large enterprises and SMEs) and type (cloud-based and on-premises), with cloud-based solutions witnessing faster growth due to their flexibility and accessibility. Key players like IBM SPSS, SAS, Tableau, and RapidMiner are competing intensely, focusing on innovation and strategic partnerships to maintain their market share. Geographically, North America and Europe currently hold the largest market share, but the Asia-Pacific region is expected to demonstrate significant growth in the coming years fueled by rising digitalization and technological advancements in developing economies like India and China. The competitive landscape is dynamic, with established players facing challenges from emerging startups offering specialized solutions. This necessitates continuous innovation and adaptation to evolving market needs. The demand for user-friendly interfaces and intuitive tools is increasing, driving the development of more accessible data science software. Furthermore, integration with other business intelligence and analytics platforms is crucial for seamless data flow and comprehensive insights. The focus on enhancing data security features and compliance with regulations like GDPR is also gaining importance, impacting the development and adoption of new solutions. Long-term growth prospects are favorable, driven by the expanding scope of data science applications and the rising importance of data-driven decision-making in various aspects of business and society. However, addressing the skill gap in data science professionals remains crucial for realizing the full potential of this rapidly evolving market.

Recent developments include: January 2022: IBM and Francisco Partners disclosed the execution of a definitive contract under which Francisco Partners will purchase medical care information and analytics resources from IBM, which are currently part of the IBM Watson Health business., October 2021: Informatica LLC announced an important cloud storage agreement with Google Cloud in October 2021. This collaboration allows Informatica clients to transition to Google Cloud as much as twelve times quicker. Informatica's Google Cloud Marketplace transactable solutions now incorporate Master Data Administration and Data Governance capabilities., Completing a unit of labor with incorrect data costs ten times more estimates than the Harvard Business Review, and finding the correct data for effective tools has never been difficult. A reliable system may be implemented by selecting and deploying intelligent workflow-driven, self-service options tools for data quality with inbuilt quality controls.. Key drivers for this market are: Increasing demand for data quality: Businesses are increasingly recognizing the importance of data quality for decision-making and operational efficiency. This is driving demand for data quality tools that can automate and streamline the data cleansing and validation process.

Growing adoption of cloud-based data quality tools: Cloud-based data quality tools offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness. This is driving the adoption of cloud-based data quality tools across all industries.

Emergence of AI-powered data quality tools: AI-powered data quality tools can automate many of the tasks involved in data cleansing and validation, making it easier and faster to achieve high-quality data. This is driving the adoption of AI-powered data quality tools across all industries.. Potential restraints include: Data privacy and security concerns: Data privacy and security regulations are becoming increasingly stringent, which can make it difficult for businesses to implement data quality initiatives.

Lack of skilled professionals: There is a shortage of skilled data quality professionals who can implement and manage data quality tools. This can make it difficult for businesses to achieve high-quality data.

Cost of data quality tools: Data quality tools can be expensive, especially for large businesses with complex data environments. This can make it difficult for businesses to justify the investment in data quality tools.. Notable trends are: Adoption of AI-powered data quality tools: AI-powered data quality tools are becoming increasingly popular, as they can automate many of the tasks involved in data cleansing and validation. This makes it easier and faster to achieve high-quality data.

Growth of cloud-based data quality tools: Cloud-based data quality tools are becoming increasingly popular, as they offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness.

Focus on data privacy and security: Data quality tools are increasingly being used to help businesses comply with data privacy and security regulations. This is driving the development of new data quality tools that can help businesses protect their data..

Snapshot img

Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs 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. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand

🔗 Skill Taxonomy Data US | AI-Powered Title & Skill Taxonomy Matched with Job Postings for Talent Intelligence & Workforce Planning

Our Skill Taxonomy Data product offers a comprehensive, AI-driven hierarchical mapping of job titles and associated skills, certifications, and qualifications — precisely matched to millions of job postings across the US labor market. This data empowers HR professionals, talent managers, and workforce planners with unparalleled insights for strategic decision-making.

Harnessing state-of-the-art AI and large language models (LLMs) validated by human experts, our skill taxonomy data surpasses traditional keyword matching by leveraging advanced entity recognition, contextual filtering, and skill relevance ranking. This ensures highly accurate, actionable intelligence for talent acquisition, HR analytics, and workforce development.

🧠 What the Skill Taxonomy Data Solves Our data transforms unstructured job market information into a dynamic, normalized taxonomy of skills and titles — addressing key questions such as:

• What specific hard and soft skills are demanded across job titles and industries? • How do certifications and qualifications align with workforce requirements? • Which skills are emerging or declining in demand for targeted workforce planning? • How can organizations benchmark and close skill gaps effectively? • What hierarchical skill relationships support career development and succession planning?

⚙️ Key Features & Capabilities ✅ Normalized Titles & Skills • ~70,000 unique normalized job titles (e.g., Human Resources Generalist) • Up to 20 hard skills per title (~37,000 unique skills, e.g., Python) • Up to 10 soft skills per title (~400 unique, e.g., Communication) • ~3,000 unique certifications (e.g., PMP) and ~8 standardized qualification levels (e.g., Bachelor’s) • Weighted relevance scores based on frequency, uniqueness, and contextual significance

🧠 Advanced AI & NLP Enrichment • Contextual entity recognition using NER, embeddings, MinHash, and AI-based filtering • Disambiguation of skill variants (e.g., ML → Machine Learning) • Exclusion of irrelevant entities based on job role context

🗂 Hierarchical Skill Mapping & Descriptions • Structured taxonomy from broad categories to granular capabilities • Clear skill definitions for use in HR, L&D, and employee development

📊 Demand & Supply Analytics • Real-time and historical tracking of skill demand from 600M+ job postings • Visibility into skill clusters, their market value, and projected workforce needs

🔎 Interactive Search & Insights • Searchable skill-to-title mapping for recruitment and internal mobility • Support for targeted training programs and career pathing initiatives

🔁 Continuous Updates & Market Responsiveness • Monthly data refreshes reflecting changes in the labor market and evolving tech landscape

🧩 Comprehensive Workforce Management Support • Actionable insights to optimize hiring, reskill existing employees, and plan succession effectively

📈 Scalable & Customizable Solutions • Adaptable across industries and organization sizes • Customizable to support bespoke strategic HR and analytics initiatives

🧠 Use Cases & Benefits • Human Resources (HR): Streamline recruitment by identifying critical role-specific skills and certifications • Talent Management: Design L&D programs aligned with in-demand and emerging skills • HR Consulting: Deliver evidence-based talent diagnostics and strategic workforce solutions • Human Resources Planning: Forecast and prepare for evolving organizational skill needs • HR Analytics: Detect and analyze skill trends for better workforce and talent decisions

💡 What Makes This Skill Taxonomy Data Unique • AI-Validated Skill Recognition: Goes beyond keyword matching using contextual LLMs and AI models • Data Depth & Breadth: Covers over 600M US job postings across industries, refreshed monthly • Weighted Skill Importance: Context-aware scoring system suppresses generic skills and highlights role-specific needs • Rich Metadata: Includes weights, skill definitions, certifications, and qualifications — integrated into a structured hierarchy • Seamless Integration: Easily embedded into HRIS, ATS, L&D, and workforce analytics tools

👥 Who Uses Our Skill Taxonomy Data • HR & Talent Acquisition Teams – For sourcing, screening, and candidate-job matching • Learning & Development Managers – For designing targeted training and upskilling programs • Workforce Planning & Analytics Teams – To anticipate future hiring and skill needs • HR Consultants & Analysts – For delivering actionable talent strategy and diagnostics • Business Leaders & Strategy Teams – To inform competitive workforce and organizational strategies

🏢 About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipe...

According to Cognitive Market Research, The Global Pre Employment Assessment Tools market size is USD 1851.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 7.50% from 2023 to 2030.

North America Pre Employment Assessment Tools held the major market of more than 40% of the global revenue with a market size of USD 740.48 million in 2023 and will grow at a compound annual growth rate (CAGR) of 5.7% from 2023 to 2030.
Europe Pre Employment Assessment Tools accounted for a share of over 30% of the global market size of USD 555.36 million in 2023.
Asia Pacific Pre Employment Assessment Tools held the market of more than 23% of the global revenue with a market size of USD 425.78 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.5% from 2023 to 2030.
Latin America Pre Employment Assessment Tools market of more than 5% of the global revenue with a market size of USD 92.56 million in 2023 and will grow at a compound annual growth rate (CAGR) of 6.9% from 2023 to 2030.
Middle East and Africa Pre Employment Assessment Tools held the major market of more than 32% of the global revenue with a market size of USD 37.2 million in 2023 and will grow at a compound annual growth rate (CAGR) of 7.2% from 2023 to 2030.
The demand for Pre Employment Assessment Tools is rising due to the development of the Workforce.
Demand for web based remains higher in the Pre Employment Assessment Tools market.
The SME application category held the highest Pre Employment Assessment Tools market revenue share in 2023.

Development of the Workforce to Provide Viable Market Output

As firms expand their talent search abroad, Pre-Employment Assessment Tools are critical for overcoming language and cultural hurdles. These tools provide standardized evaluation methodologies to ensure that varied candidates receive fair and consistent assessments. Employers can use psychometric assessments, cognitive tests, and skill evaluations to discover suitable candidates regardless of linguistic or cultural variances. This increases inclusivity in the recruiting process and assists firms in developing diverse and high-performing teams. Pre-Employment Assessment Tools' adaptation to global talent challenges is critical for developing fair, unbiased, and effective hiring methods across varied international landscapes.

Remote Work to Propel Market Growth

The rise of remote work highlights the need for Pre-Employment Assessments since evaluating skills and work styles becomes critical without the need for traditional in-person interviews. These exams provide a thorough insight into candidates' talents, ensuring a match with the remote work requirements. Employers can use technology and tailored examinations to evaluate individuals' suitability for remote employment, contributing to effective talent acquisition. This move demonstrates Pre-job Assessment Tools' versatility in aligning hiring processes with the changing landscape of remote work, promoting efficiency and informed decision-making in a virtual job setting.

Market Restraints of the Pre Employment Assessment Tools

Data Privacy and Security to Restrict Market Growth

Storing and analyzing candidate data with Pre-Employment Assessment Tools raises security and privacy problems. To prevent these risks, businesses must deploy strong security measures like encryption and safe storage solutions. It is critical to follow data privacy rules like GDPR or HIPAA, ensuring that candidate information is handled ethically and legally. Establishing strict access rules, conducting frequent security audits, and stressing transparency with applicants about data usage are critical steps in maintaining confidence and protecting sensitive information. Adhering to these guidelines is critical for the responsible and secure use of Pre-Employment Assessment Tools during recruitment. Storing and analyzing candidate data with Pre-Employment Assessment Tools raises security and privacy problems. To prevent these risks, businesses must deploy strong security measures like encryption and safe storage solutions. It is critical to follow data privacy rules like GDPR or HIPAA, ensuring that candidate information is handled ethically and legally. Establishing strict access rules, conducting frequent security audits, and stressing transparency with applicants about data usage are critical steps in maintaining confide...

Techsalerator’s Job Openings Data in Latin America provides a thorough and insightful dataset designed to deliver businesses, recruiters, labor market analysts, and job seekers with a comprehensive view of employment opportunities across the Latin American region. This dataset aggregates job postings from a diverse array of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available throughout Latin America.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates job postings from a variety of sources, including company career sites, job boards, recruitment agencies, and professional networking platforms. This comprehensive coverage ensures that users receive a broad spectrum of job opportunities from multiple channels. Daily Updates:

Data is updated daily, providing real-time insights into job market conditions. This frequent updating ensures that the dataset reflects the latest job openings and market trends. Sector-Specific Data:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This segmentation allows users to analyze trends and opportunities within specific industries. Regional Breakdown:

Detailed information is provided on job openings across different countries and key regions within Latin America. This regional breakdown helps users understand job market dynamics and opportunities in various geographic areas. Role and Skill Analysis:

The dataset includes information on job roles, required skills, qualifications, and experience levels. This feature assists job seekers in identifying opportunities that match their expertise and helps recruiters find candidates with the desired skill sets. Company Insights:

Users can access information about the companies posting job openings, including company names, industries, and locations. This data provides insights into which companies are hiring and where demand for talent is highest. Historical Data:

The dataset may include historical job posting data, enabling users to perform trend analysis and comparative studies over time. This feature supports understanding changes and developments in the job market. Latin American 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 Caribbean: Cuba Dominican Republic Haiti (Note: Primarily French-speaking, but included due to geographic and cultural ties) Jamaica Trinidad and Tobago Benefits of the Dataset: Strategic Recruitment: Recruiters and HR professionals can use the data to identify hiring trends, understand competitive practices, and optimize their recruitment strategies based on real-time market insights. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill gaps, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive and updated list of job openings tailored to their skills and preferred locations, enhancing the efficiency and effectiveness of their job search. Workforce Planning: Companies can gain valuable insights into the availability of talent across Latin America, assisting with decisions related to market entry, expansion, and talent acquisition. Techsalerator’s Job Openings Data in Latin America is an essential tool for understanding the diverse and evolving job markets across the region. By providing up-to-date and detailed information on job postings, it supports effective decision-making for businesses, job seekers, and labor market analysts.

Coding Assessment Tool Market Size And Forecast

Coding Assessment Tool Market size was valued at USD 3.5 Billion in 2023 and is projected to reach USD 5.4 Billion by 2031, growing at a CAGR of 6.07% during the forecast period 2024-2031.

Global Coding Assessment Tool Market Drivers

The market drivers for the Coding Assessment Tool Market can be influenced by various factors. These may include:

Increased Demand For Technical Skills: The rapid evolution of technology has created an increased demand for technical skills across various industries. Organizations seek candidates proficient in programming and software development to maintain competitive advantage. This heightened demand drives companies to adopt coding assessment tools to streamline their hiring processes and ensure they identify the most qualified candidates. As businesses expand their digital presence, the necessity for skilled coders grows, compelling HR and recruitment teams to invest in robust assessment solutions that efficiently evaluate candidates' coding proficiency and problem-solving abilities, ultimately improving the quality of hires and reducing recruitment costs. Shift To Remote Hiring: The recent shift toward remote work has transformed traditional hiring practices, necessitating the implementation of digital solutions for candidate evaluation. As companies embrace remote hiring, coding assessment tools become indispensable for assessing applicants from diverse geographical locations. These platforms enable employers to effectively evaluate coding skills in a virtual environment, ensuring that they can remotely attract talent without geographical constraints. This transition not only expedites the recruitment process but also allows companies to tap into a wider talent pool, thereby meeting the increasing demands for remote roles and fostering a more inclusive hiring culture. Emphasis On Data-Driven Recruitment: Data-driven recruitment has gained traction as organizations recognize the importance of analytics in the hiring process. Coding assessment tools often incorporate data analytics capabilities, allowing recruiters to measure and analyze candidates' performance objectively. By leveraging data insights, companies can identify skill gaps, refine their hiring criteria, and enhance their overall recruitment strategy. This emphasis on quantifiable metrics shifts focus away from subjective judgments, leading to more informed hiring decisions. As businesses strive to foster diversity and inclusivity while minimizing biases, data-driven approaches supported by coding assessment tools are increasingly adopted in recruitment practices. Rise Of Edtech And Online Learning: The burgeoning EdTech sector has led to the proliferation of online learning platforms offering coding courses and bootcamps. As more individuals pursue coding education through these accessible channels, the demand for reliable assessment tools has surged. Companies are keen to evaluate the skills acquired by candidates through such programs, ensuring their competencies align with organizational needs. Coding assessment tools facilitate this evaluation process, helping employers identify strong candidates who have enhanced their skills through these modern learning methodologies. Consequently, the growth of EdTech and online learning directly fuels the demand for effective coding assessment solutions in the market. Global Hiring Challenges: With the globalization of the workforce, companies encounter unique hiring challenges that coding assessment tools can effectively address. Issues such as varying educational standards, diverse coding practices, and different levels of practical experience complicate the recruitment process across borders. Coding assessment tools provide standardized evaluation mechanisms that ensure fair and consistent assessments regardless of candidates' backgrounds. This helps organizations to navigate global hiring complexities, allowing them to identify top talent while maintaining a fair recruitment process. Consequently, the need for efficient and unbiased assessment methodologies drives demand for innovative coding assessment solutions in the marketplace.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The STEP target population is the urban population aged 15 to 64 included. Lao PDR sampled both urban and rural areas (with road) of the country. Areas are classified as rural or urban based on each country's official definition.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The STEP target population is the population aged 15 to 64 included, living in urban areas, as defined by each country's statistical office. Are excluded from the sample: - Residents of institutions (prisons, hospitals, etc) - Residents of senior homes and hospices - Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc - Persons living outside the country at the time of data collection

Laos' Target Population Description The target population comprises all non-institutionalized persons 15 to 64 years of age (inclusive) living in urban and rural areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations. There will be no exclusions for the target population. The survey tool is designed for Lao language only (the sole national language). IRL will make every effort to interview non-Lao speaking persons through the use of local translators. In most such cases, the household and individual modules will be carried out with the assistance of a translator when available. However, Modules 6 and 9 will not be administered to those respondents who do not speak or read Lao as per STEP technical standards.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Laos sample design is a 3 stage stratified sample design. The stratification variable is urban-rural indicator.

First Stage Sample The primary sample unit (PSU) is a village. The sampling objective was to conduct interviews in 134 urban villages and 54 rural villages. The villages were selected with probability proportional to size (PPS), where the measure of size was the number of households in a village.

Second Stage Sample The second stage sample unit (SSU) is a household. In the second stage, the number of households selected in each selected PSU was proportional to the size of the selected PSUs. The households were selected from a list of households in each selected PSU by systematic equal probability sampling. At the same time, a reserve sample of the same number of households as the target sample in each PSU was selected for use when needed to ensure that the target sample size is achieved.

Third Stage Sample The third stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: - a Background Questionnaire developed by the WB STEP team - a Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. The WB STEP team and ETS collaborated closely with the Lao PDR survey firm during the process and reviewed the adaptation and translation to Lao using a back translation.

The survey instruments were both piloted as part of the survey pre-test.

The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP data management process:

1) Raw data is sent by the survey firm 2) The World Bank (WB) STEP team runs data checks on the background questionnaire data. Educational Testing Services (ETS) runs data checks on the Reading Literacy Assessment data. Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check if the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the background questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information on data processing in STEP surveys is provided in “Guidelines for STEP Data Entry Programs” document, available in external resources. The template do-file used by the STEP team to check raw background questionnaire data is provided as an external resource, too.

Response rate

An overall response rate of 95% was achieved in the Lao PDR STEP Survey.

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. All country weighting documentations are provided as an external resource.

The global data science software market is experiencing robust growth, driven by the increasing adoption of big data analytics across various industries. The market, estimated at $15 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), exhibiting a Compound Annual Growth Rate (CAGR) of approximately 15%. This growth is fueled by several key factors, including the rising volume of data generated by businesses, the need for improved decision-making through data-driven insights, and the increasing availability of cloud-based data science platforms. The market is segmented by application (large enterprises and SMEs) and type (cloud-based and on-premises), with cloud-based solutions witnessing faster adoption due to their scalability, cost-effectiveness, and ease of access. Key players like IBM SPSS, Matlab, SAS, Tableau, and RapidMiner are driving innovation and competition, constantly enhancing their offerings to meet evolving business requirements. Geographic expansion, particularly in emerging economies of Asia-Pacific and regions with burgeoning technological infrastructure, contributes significantly to market expansion. However, factors such as the high cost of implementation, the need for skilled data scientists, and concerns around data security and privacy act as restraints on wider adoption. The market's future trajectory will depend on the ongoing development of advanced analytics techniques, artificial intelligence integration within data science platforms, and the continued growth of data-driven businesses across all sectors. The North American market currently holds a dominant share, driven by early adoption and a strong technological ecosystem. However, Europe and Asia-Pacific regions are witnessing significant growth, presenting lucrative opportunities for market players. The competitive landscape is characterized by a mix of established vendors offering comprehensive suites of tools and newer entrants specializing in niche applications or specific analytical techniques. The increasing demand for data scientists and skilled professionals is expected to fuel the growth of specialized training programs and certifications, further bolstering market expansion. Focus on user-friendliness, accessibility, and integration with existing business intelligence tools will be crucial for future success in this dynamic and rapidly evolving market.

The global Data Load Tool (DLT) market is projected to reach a value of USD 23.4 billion by 2033, exhibiting a CAGR of 9.4% over the forecast period (2023-2033). The market growth is primarily attributed to the increasing adoption of cloud-based data management solutions, the growing need for data integration and management, and the rising demand for real-time data analysis. The market is segmented based on type (on-premise and cloud-based) and application (large enterprises and small and medium-sized enterprises). The cloud-based segment is expected to hold a significant market share due to its scalability, flexibility, and cost-effectiveness. The key drivers of the Data Load Tool (DLT) market include the increasing adoption of cloud-based data platforms, the growing need for data integration and management, the rising demand for real-time data analysis, and the growing adoption of big data technologies. However, the market growth is restrained by factors such as the lack of skilled professionals, data security and privacy concerns, and the high cost of implementation. The market is highly competitive with several key players, including Pennant Technologies, IBM, Amazon Web Services, Microsoft, Oracle, SAP, Skillsoft, XLM Solutions, and others. The market is consolidated with the top players holding a significant market share. The key strategies adopted by these players include product innovation, mergers and acquisitions, and partnerships to strengthen their market position and gain a competitive edge.

The Big Data Analysis Platform market, currently valued at $63,750 million (2025), is projected for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 10.6% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data across diverse industries—including finance, healthcare, and manufacturing—demand sophisticated analytical tools for extracting actionable insights. Furthermore, the rising adoption of cloud-based solutions and advanced analytics techniques, such as artificial intelligence (AI) and machine learning (ML), are significantly boosting market growth. The market's segmentation by application (Computer, Electronic Communication, Energy, Machinery, Chemical Industries) and by analysis type (Descriptive, Diagnostic, Predictive, Normative) highlights the diverse needs and applications of these platforms. The competitive landscape is dominated by established technology giants like IBM, Microsoft, and Google, alongside specialized players such as iTechArt and InData Labs. Geographic distribution shows strong presence in North America and Europe, with Asia Pacific emerging as a high-growth region. Growth constraints may include the high cost of implementation, the need for skilled data scientists, and data security concerns. However, ongoing technological advancements and increasing awareness of the value of data-driven decision-making are expected to offset these challenges. The forecast period (2025-2033) promises significant expansion, with the market size exceeding $150 billion (a conservative estimate based on the provided CAGR and current market value). Continuous innovation in areas such as real-time analytics, big data visualization, and advanced analytics capabilities will likely drive future growth. Companies focusing on user-friendly interfaces and integrating advanced AI/ML capabilities are expected to gain a competitive edge. The focus on industry-specific solutions will further enhance market penetration and address the varied data analysis requirements across sectors. The demand for skilled professionals remains a key factor impacting market growth, pushing educational institutions and corporations to invest in big data training and skill development programs.

The global soft skills assessment software market is experiencing robust growth, projected to reach $766 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 8.3% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing recognition of soft skills as crucial differentiators in today's competitive job market drives demand for sophisticated assessment tools. Organizations are actively seeking efficient and objective methods to evaluate candidates' communication, teamwork, problem-solving, and critical thinking abilities. Secondly, technological advancements, particularly in AI-powered assessment platforms, enable more accurate and insightful evaluations, leading to improved hiring decisions and reduced recruitment costs. The rise of remote work and virtual hiring processes further accelerates adoption, as these platforms facilitate seamless assessment regardless of geographical location. Finally, the growing emphasis on data-driven decision-making in HR promotes the use of software solutions that provide quantifiable insights into candidate profiles. Leading players like Predictive, eSkill, Plum, and others are actively shaping this market through continuous innovation and expansion of their product portfolios. The market segmentation is likely diverse, encompassing solutions tailored to specific industries (e.g., healthcare, technology, education) and offering varying levels of functionality (e.g., basic screening tools to comprehensive talent management systems). Geographic variations in market penetration are expected, with developed regions such as North America and Europe leading the adoption curve, while emerging economies witness increasing demand driven by expanding businesses and improved digital infrastructure. Restraints to market growth could include the initial investment costs associated with implementing new software, concerns over data privacy and security, and the need for effective integration with existing HR systems. However, the overall market outlook remains positive, driven by the increasing strategic importance placed on soft skills assessment within organizational talent acquisition strategies.

This dataset provides the raw anonymised (quantitative) data from the EDSA demand analysis. This data has been gathered from surveys performed with those who identify as data scientists and manages of data scientists in different sectors across Europe. The coverage of the data includes level of current expertise of the individual or team (data scientist and manager respectively) in eight key areas. The dataset also includes the importance of the eight key areas as capabilities of a data scientist. Further the dataset includes a breakdown of key tools, technologies and training delivery methods required to enhance the skill set of data scientists across Europe. The EDSA dashboard provides an interactive view of this dataset and demonstrates how it is being used within the project. The dataset forms part of the European Data Science Academy (EDSA) project which received funding from the European Unions's Horizon 2020 research and innovation programme under grant agreement No 643937. This three year project ran/runs from February 2015 to January 2018. Important note on privacy: This dataset has been collected and made available in a pseudo anonymous way, as agreed by participants. This means that while each record represents a person, no sensitive identifiable information, such as name, email or affiliation is available (we don't even collect it). Pseudo anonymisation is never full proof, however the projects privacy impact assessment has concluded that the risk resulting from the de-anonymisation of the data is extremely low. It should be noted that data is not included of participants who did not explicitly agree that it could be shared pseudo anonymously (this was due to a change of terms after the survey had started gathering responses, meaning any early responses had come from people who didn't see this clause). If you have any concerns please contact the data publisher via the links below.

Skills Graph Platform Market Outlook

According to our latest research, the global Skills Graph Platform market size is valued at USD 2.15 billion in 2024, with a robust growth trajectory expected over the coming years. The market is forecasted to reach USD 9.87 billion by 2033, expanding at a notable CAGR of 18.4% during the period from 2025 to 2033. This surge is primarily driven by the increasing demand for intelligent talent management, dynamic workforce planning, and the integration of advanced analytics and artificial intelligence into human capital management systems. As organizations worldwide seek to optimize talent acquisition, upskilling, and workforce agility, the adoption of Skills Graph Platforms is experiencing unprecedented momentum.

The primary growth factor fueling the Skills Graph Platform market is the accelerating digital transformation initiatives across enterprises and institutions. Organizations are increasingly recognizing the value of mapping, analyzing, and leveraging employee skills data at scale to drive business outcomes. The proliferation of remote and hybrid work models, coupled with the need to bridge skill gaps in a rapidly evolving labor market, is compelling businesses to invest in sophisticated platforms that provide real-time insights into workforce competencies. These platforms enable data-driven decisions for hiring, upskilling, and redeployment, which are critical in maintaining competitiveness amid technological disruption and shifting market demands. The rise of AI-powered talent analytics and the integration of machine learning algorithms further enhance the accuracy and predictive capabilities of these platforms, making them indispensable tools for future-ready enterprises.

Another significant driver is the increasing focus on personalized learning and development strategies within organizations and educational institutions. As the demand for continuous learning intensifies, Skills Graph Platforms play a pivotal role in identifying individual skill gaps, recommending tailored learning paths, and tracking progress. This level of personalization not only improves employee engagement and retention but also ensures that organizations can cultivate a highly skilled and adaptable workforce. Educational institutions, too, are leveraging these platforms to align curricula with industry requirements, thereby enhancing student employability and fostering stronger industry-academia collaborations. The integration of Skills Graph Platforms with existing Learning Management Systems (LMS) and Human Resource Information Systems (HRIS) is streamlining talent development processes and delivering measurable ROI.

The market is further bolstered by the growing emphasis on data-driven workforce planning and diversity, equity, and inclusion (DEI) initiatives. Skills Graph Platforms provide granular visibility into workforce demographics, skills distribution, and talent mobility, enabling organizations to design targeted interventions for workforce diversification and succession planning. Governments and regulatory bodies are also encouraging the adoption of such platforms to support national upskilling agendas and enhance labor market transparency. As businesses navigate complex regulatory environments and strive to meet ESG (Environmental, Social, and Governance) objectives, the ability to track and report on workforce competencies becomes a strategic imperative. This regulatory push, combined with the need for agile, skills-based workforce strategies, is expected to sustain market growth throughout the forecast period.

Regionally, North America continues to dominate the Skills Graph Platform market, owing to its mature HR technology ecosystem, high digital adoption rates, and a strong presence of leading solution providers. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, increasing investments in digital infrastructure, and a burgeoning demand for skilled talent. Europe also presents significant growth opportunities, particularly in sectors such as manufacturing, BFSI, and healthcare, where workforce agility and compliance are paramount. Latin America and the Middle East & Africa are witnessing steady adoption, supported by government-led digital skills initiatives and the expansion of multinational enterprises in these regions.

Component Analysis

The Skills Graph Platform market is segmented by component into Software and <

The global Soft Skills Assessment Software market is poised to grow from a size of USD 766 million in 2025 to USD 1,476.3 million by 2033, exhibiting a CAGR of 8.3% during the forecast period. This growth can be attributed to the increasing adoption of soft skills assessment tools by organizations to identify and evaluate the non-technical abilities of their employees, such as communication, problem-solving, and teamwork. Moreover, the rising need for effective employee screening and selection processes is driving the demand for soft skills assessment software, which provides comprehensive insights into candidates' behavioral traits and interpersonal skills. The market is segmented based on application into large enterprises and small and medium-sized enterprises (SMEs). Large enterprises are expected to hold a larger market share due to their extensive focus on employee development and talent management. In terms of types, the market is classified into cloud-based and web-based software. Cloud-based software is gaining popularity due to its scalability, cost-effectiveness, and ease of access. Additionally, the market is segmented by region into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America is the dominant market, followed by Europe. However, Asia Pacific is expected to witness the highest growth rate during the forecast period due to the rapid adoption of technology and the increasing number of startups in the region.

📊 LinkedIn Company Data for Company Analysis, Valuation & Portfolio Strategy LinkedIn company data is one of the most powerful forms of alternative data for understanding company behavior, firmographics, business dynamics, and real-time hiring signals. Canaria’s enriched LinkedIn company data provides detailed company profiles, including hiring activity, job postings, employee trends, headquarters and branch locations, and verified metadata from Google Maps. This LinkedIn corporate data is updated weekly and optimized for use in company analysis, startup scouting, private company valuation, and investment monitoring. It supports BI dashboards, risk models, CRM enrichment, and portfolio strategy.

🧠 Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn company insights transform opaque business landscapes into structured, analyzable data. Whether you’re conducting M&A due diligence, tracking high-growth companies, or benchmarking performance, this dataset empowers fast, confident decisions.

🔍 Company Analysis • Identify a company’s size, industry classification, and headcount signals using LinkedIn firmographic data • Analyze social presence through LinkedIn follower metrics and employee engagement • Understand geographic expansion through branch locations and hiring distribution • Benchmark companies using LinkedIn profile activity and job posting history • Monitor business changes with real-time LinkedIn updates

📈 Company Valuation & Financial Benchmarking • Feed LinkedIn-based firmographics into comps and financial models • Use hiring velocity from LinkedIn job data as a proxy for business growth • Strengthen private market intelligence with verified non-financial signals • Validate scale, structure, and presence via LinkedIn and Google Maps footprint

⚠️ Company Risk Analysis • Detect red flags using hiring freezes or drop in profile activity • Spot market shifts through location downsizing or organizational changes • Identify distressed companies with decreased LinkedIn job posting frequency • Compare stated presence vs. active behavior to identify risk anomalies

📊 Business Intelligence (BI) & Strategic Planning • Segment companies by industry, headcount, growth behavior, and hiring activity • Build BI dashboards integrating LinkedIn job trends and firmographic segmentation • Identify geographic hiring hotspots using Maps and LinkedIn signal overlays • Track job creation, title distribution, and skill demand in near real-time • Export filtered LinkedIn corporate data into CRMs, analytics tools, and lead scoring systems

📁 Portfolio Management & Investment Monitoring • Enhance portfolio tracking with LinkedIn hiring data and firmographic enrichment • Spot hiring surges, geographic expansions, or restructuring in real-time • Correlate LinkedIn growth indicators with strategic outcomes • Analyze competitors and targets using historical and real-time LinkedIn data • Generate alerts for high-impact company changes in your portfolio universe

🌐 What Makes This LinkedIn Company Data Unique

🧠 Includes Real-Time Hiring Signals • Gain visibility into which companies are hiring, at what scale, and for which roles using enriched LinkedIn job data

📍 Verified Location Intelligence • Confirm branch and HQ locations with Google Maps coordinates and public company metadata

🔁 Weekly Updates • Stay ahead of the market with fresh, continuously updated LinkedIn company insights

🔗 Clean & Analysis-Ready Format • Structured, deduplicated, and taxonomy-mapped data that integrates with CRMs, BI platforms, and investment models

🎯 Who Benefits from LinkedIn Company Data • Hedge funds, VCs, and PE firms analyzing startup and private company activity • Portfolio managers and financial analysts tracking operational shifts • Market research firms modeling sector momentum and firmographics • Strategy teams calculating market size using LinkedIn company footprints • BI and analytics teams building company-level dashboards • Compliance and KYC teams enriching company identity records • Corp dev teams scouting LinkedIn acquisition targets and expansion signals

📌 Summary Canaria’s LinkedIn company data delivers high-frequency, high-quality insights into U.S. companies, combining job posting trends, location data, and firmographic intelligence. With real-time updates and structured delivery formats, this alternative dataset enables powerful workflows across company analysis, financial modeling, investment research, market segmentation, and business strategy.

🏢 About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our AI-powered pipeline is developed by a seasoned team of machine learning experts from Google, Meta, and Amazon, and by alumni of S...

The global talent assessment tools market is experiencing robust growth, driven by the increasing need for organizations to optimize their recruitment and employee development strategies. The market, estimated at $8 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $20 billion by 2033. This growth is fueled by several key factors, including the rising adoption of digital technologies within Human Resources (HR), the growing emphasis on data-driven decision-making in talent management, and the increasing demand for skilled professionals across diverse industries. The market is segmented by application (large enterprises dominating the share followed by SMEs), and by type (certification software and assessment center tools, with certification software holding a slightly larger share). North America and Europe currently represent the largest regional markets, but significant growth is anticipated from the Asia-Pacific region, driven by rapid economic expansion and increasing investments in HR technology. However, factors such as the high cost of implementation and integration of some assessment tools, along with concerns about data privacy and security, pose challenges to market expansion. The competitive landscape is highly fragmented, with a mix of established players and emerging technology providers. Companies like SHL, AON, and Korn Ferry hold significant market share due to their extensive experience and established client base. However, innovative start-ups offering specialized solutions and cost-effective alternatives are gaining traction. The market is witnessing a shift towards cloud-based and AI-powered talent assessment tools, offering greater scalability, accessibility, and analytical capabilities. Future growth will be shaped by the increasing integration of these tools with other HR technologies, creating a more holistic and data-driven approach to talent management. Furthermore, a heightened focus on improving candidate experience and ensuring fairness and bias mitigation within assessment processes will continue to drive innovation and adoption within this rapidly evolving market.