Guided by common values, Covid Act Now is a multidisciplinary team of technologists, epidemiologists, health experts, and public policy leaders working to provide disease intelligence and data analysis on COVID in the U.S.

APIs, Visualizations and csv files of data are available for public use.

To assess use of external evidence for overall survival (OS) estimation in oncology single-technology appraisals (STAs) by the National Institute for Health and Care Excellence (NICE). STAs for oncology drugs appraised by NICE between January 2021 and March 2023 were identified. For each eligible STA, OS extrapolation methods used, the rationale for using external data, the source and type of data, and information on acceptance by the evidence review group (ERG) and the appraisal committee were extracted. Initially, 215 STAs were identified, of which 82 were eligible for the study. Of these, 32 STAs used external data for OS extrapolation, including trial data (44%), real-world data (47%), clinical opinion (25%), meta-analysis (1%) and previous STA (1%). External data were used more frequently in state-transition models for post-event transitions and cure assumptions, and in partitioned-survival models to replace pivotal trial OS, inform long-term survival estimates or to estimate OS based on surrogacy analysis. Sensitivity analyses on use of external data was explored in 16 (50%) of the STAs. The committee accepted use of external data in half of the analysed STAs, acknowledging uncertainty in OS extrapolation. The analysis was limited to the STAs published between 2021 and 2023 and publicly available materials on the NICE website. This study provides an overview of external data used to estimate OS in oncology STAs conducted by NICE in recent years. External data, including trial data, real-world data and clinical opinions, were incorporated into recent oncology STAs at various modelling stages. ERGs and appraisal committees were generally accepting of the use of external data. However, it is crucial to conduct a sensitivity analysis and provide a justification for the methods and data source selection.

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Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offerings, are driving innovatio

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

Data Analytics Outsourcing Market size was valued at USD 10.2 Billion in 2024 and is projected to reach USD 55.44 Billion by 2032, growing at a CAGR of 26% from 2026 to 2032. Global Data Analytics Outsourcing Market DriversGrowing Volume of Big Data: The increasing volume of big data is leading firms to outsource analytics. According to IDC, the global datasphere is expected to increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This tremendous rise in data volume has compelled firms to seek external expertise for efficient data management and analytics.Cost-Effectiveness of Outsourcing: Outsourcing data analytics can be more cost-effective than having an in-house team. According to a Deloitte poll, 59% of organizations outsource primarily to save money. According to the same poll, 47% of organizations saved between 10 and 25% of their costs through outsourcing.Shortage of Skilled Data Professionals: Due to a shortage of experienced data analytics workers, organizations are increasingly outsourcing. The U.S. Bureau of Labor Statistics predicts that employment of data scientists and mathematical scientific occupations will expand 31% between 2019 and 2029, substantially faster than the national average, indicating a significant skills gap.

In 2023, ******* was the European country with the highest share of construction companies using data analytics. On average, over ** percent of construction enterprises in the European Union used data analytics, either performed by their employees or by an external provider.

This dataset is daily time series of stocks data of (NYSE COMPOSITE, TSX Composite, Nikkei 225, Global X DAX Germany, and HANG SENG INDEX) along with Weather and Pandemic data(covid-19) to extract and identify the features that can be correlated with financial market data. It is found that a number of factors are derived from Covid-19 data as well as weather data.

We conducted an analysis to confirm our observations that only a very small percentage of public research data is hosted in the Institutional Data Repositories, while the vast majority is published in the open domain-specific and generalist data repositories.

For this analysis, we selected 11 institutions, many of which have been our evaluation partners. For each institution, we counted the number of datasets published in their Institutional Data Repository (IDR) and tracked the number of public research datasets hosted in external data repositories via the Data Monitor API. External tracking was based on the corpus of 14+ mln data records checked against the institutional SciVal ID. One institution didn’t have an IDR.

We found out that 10 out of 11 institutions had most of their public research data hosted outside of their institution, where by research data we mean not only datasets, but a broader notion that includes, for example, software.

We will be happy to expand it by adding more institutions upon request.

Note: This is version 2 of the earlier published dataset. The number of datasets published and tracked in the Monash Institutional Data Repository has been updated based on the information provided by the Monash Library. The number of datasets in the NTU Institutional Data Repository now includes datasets only. Dataverses were excluded to avoid double counting.

There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined. We propose that this situation can be viewed as a missing data problem in which some outcomes are entirely missing within some trials, and use multiple imputation to fill in missing measurements. We apply our method to 5 longitudinal adolescent depression trials where 4 studies used one depression measure and the fifth study used a different depression measure. None of the 5 studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigating whether external information is appropriately incorporated into the imputed values.

This file contains replication data and files for "External Validity: Framework, Design, and Analysis". Additionally, it contains the an online supplementary materials with analytical derivations, additional simulation results, supporting information for the literature review, and numerical results for all figures in the main manuscript an supplementary materials.

This dataset contains current job postings available on the City of New York’s official jobs site (http://www.nyc.gov/html/careers/html/search/search.shtml). Internal postings available to city employees and external postings available to the general public are included.

How informative are treatment effects estimated in one region or time period for another region or time? In this article, I derive bounds on the average treatment effect in a context of interest using experimental evidence from another context. The bounds are based on (a) the information identified about treatment effect heterogeneity due to unobservables in the experiment and (b) using differences in outcome distributions across contexts to learn about differences in distributions of unobservables. Empirically, using data from a pair of remedial education experiments carried out in India, I show the bounds are able to recover average treatment effects in one location using results from the other while the benchmark method cannot.

This is an external dataset used for analysis on the Bellabeat case study. The complete dataset can originally be accessed on the FitBit Fitness page here. The original dataset contains 18 csv files, which I only used four. The used ones are the ones without "Clean" and "Analyzed" attached to the file names. The ones with "Clean" and "Analyzed" are the ones which have already been cleaned and analyzed using pivot tables in Google Docs and ready for visuaalization. The others are not cleaned.

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

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Business Information Market Size 2025-2029

The business information market size is forecast to increase by USD 79.6 billion, at a CAGR of 7.3% between 2024 and 2029.

The market is characterized by the increasing demand for customer-centric solutions as enterprises adapt to evolving customer preferences. This shift necessitates the provision of real-time, accurate, and actionable insights to facilitate informed decision-making. However, this market landscape is not without challenges. The threat of data misappropriation and theft looms large, necessitating robust security measures to safeguard sensitive business information. As businesses continue to digitize their operations and rely on external data sources, ensuring data security becomes a critical success factor. Companies must invest in advanced security technologies and implement stringent data protection policies to mitigate these risks. Navigating this complex market requires a strategic approach that balances the need for customer-centric solutions with the imperative to secure valuable business data.

What will be the Size of the Business Information Market during the forecast period?

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

In today's data-driven business landscape, the continuous and evolving nature of market dynamics plays a pivotal role in shaping various sectors. Data integration solutions enable seamless data flow between different systems, enhancing cloud-based business applications' functionality. Data quality management ensures data accuracy and consistency, crucial for strategic planning and customer segmentation. Data infrastructure, data warehousing, and data pipelines form the backbone of business intelligence, facilitating data storytelling and digital transformation. Data lineage and data mining reveal valuable insights, fueling data analytics platforms and business intelligence infrastructure. Data privacy regulations necessitate robust data management tools, ensuring compliance and protecting sensitive information.

Sales forecasting and business intelligence consulting offer valuable industry analysis and data-driven decision making. Data governance frameworks and data cataloging maintain order and ethics in the vast expanse of big data analytics. Machine learning algorithms, predictive analytics, and real-time analytics drive business intelligence reporting and process modeling, leading to business process optimization and financial reporting software. Sentiment analysis and marketing automation cater to customer needs, while lead generation and data ethics ensure ethical business practices. The ongoing unfolding of market activities and evolving patterns necessitate the integration of various tools and frameworks, creating a dynamic interplay that fuels business growth and innovation.

How is this Business Information Industry segmented?

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

End-user

  BFSI
  Healthcare and life sciences
  Manufacturing
  Retail
  Others


Application

  B2B
  B2C


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW). 

By End-user Insights

The bfsi segment is estimated to witness significant growth during the forecast period.

In the dynamic business landscape, data-driven insights have become essential for strategic planning and decision-making across various industries. The market caters to this demand by offering solutions that integrate and manage data from multiple sources. These include cloud-based business applications, data quality management tools, data warehousing, data pipelines, and data analytics platforms. Data storytelling and digital transformation are key trends driving the market's growth, enabling businesses to derive meaningful insights from their data. Data governance frameworks and policies are crucial components of the business intelligence infrastructure. Data privacy regulations, such as GDPR and HIPAA, are shaping the market's development.

Data mining, predictive analytics, and machine learning algorithms are increasingly being used for sales forecasting, customer segmentation, and churn prediction. Business intelligence consulting and industry analysis provide valuable insights for organizations seeking competitive advantage. Data visualization dashboards, market research databases, and data discovery tools facilitate data-driven decision making. Sentiment analysis and predictive analytics are essential for marketing automation and business process

The materials of this dataset are used in: - N. Arınık, V. Labatut and R. Figueiredo, "Characterizing measures for the assessment of cluster analysis and community detection", Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques (MARAMI), 2020, url: https://hal.archives-ouvertes.fr/hal-02993542/- N. Arınık, V. Labatut and R. Figueiredo, "Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection", in IEEE Access, vol. 9, pp. 20255-20276, 2021, doi:https://doi.org/10.1109/ACCESS.2021.3054621.This dataset contains:* figs.zip which contains the plot files* data&results.zip which contains the necessary data to perform our analysis, as well as result files

The size of the Data Quality Tools Industry market was valued at USD XX Million in 2024 and is projected to reach USD XXX Million by 2033, with an expected CAGR of 17.50% during the forecast period. Recent developments include: September 2022: MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) spin-off DataCebo announced the launch of a new tool, dubbed Synthetic Data (SD) Metrics, to help enterprises compare the quality of machine-generated synthetic data by pitching it against real data sets., May 2022: Pyramid Analytics, which developed its flagship platform, Pyramids Decision Intelligence, announced that it raised USD 120 million in a Series E round of funding. The Pyramid Decision Intelligence platform combines business analytics, data preparation, and data science capabilities with AI guidance functionality. It enables governed self-service analytics in a no-code environment.. Key drivers for this market are: Increasing Use of External Data Sources Owing to Mobile Connectivity Growth. Potential restraints include: Lack of information and Awareness about the Solutions Among Potential Users. Notable trends are: Healthcare is Expected to Witness Significant Growth.

Cuckoo Sandbox is the leading open sourceautomated malware analysis system. You can throw any suspicious file atit and in a matter of seconds Cuckoo will provide you back some detailedresults outlining what such file did when executed inside an isolatedenvironment.

Cuckoo Sandbox is free software that automated the task of analyzing any malicious file under Windows, OS X, Linux, and Android.

What can it do?

Cuckoo Sandbox is an advanced, extremely modular, and 100% open source automated malware analysis system with infinite application opportunities. By default it is able to:


Analyze many different malicious files (executables, office documents, pdf files, emails, etc) as well as malicious websites under Windows, Linux, Mac OS X, and Android virtualized environments.
Trace API calls and general behavior of the file and distill this into high level information and signatures comprehensible by anyone.
Dump and analyze network traffic, even when encrypted with SSL/TLS. With native network routing support to drop all traffic or route it through InetSIM, a network interface, or a VPN.
Perform advanced memory analysis of the infected virtualized system through Volatility as well as on a process memory granularity using YARA.


Due to Cuckoo s open source nature and extensive modular design one may customize any aspect of the analysis environment, analysis results processing, and reporting stage. Cuckoo provides you all the requirements to easily integrate the sandbox into your existing framework and backend in the way you want, with the format you want, and all of that without licensing requirements.

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The global data analytics market in the insurance industry is projected to reach USD 21,180 million by 2033, exhibiting a CAGR of 7.3% from 2025 to 2033. The growing need for risk assessment, fraud detection, and enhanced customer experience drives market expansion. Insurance companies leverage data analytics to analyze vast amounts of data from various sources, including customer demographics, policy history, and external market trends. This analysis enables them to tailor risk profiles, optimize pricing premiums, and identify fraudulent claims effectively, leading to improved underwriting decisions and reduced operational costs. Moreover, data analytics helps insurers gain valuable insights into customer behavior, preferences, and risk appetite, allowing them to develop personalized products and enhance customer engagement. The market is segmented based on type (service and software) and application (pricing premiums, fraud prevention, waste reduction, and customer insights). Geographically, North America holds a dominant position, followed by Europe and Asia-Pacific. Key market players include Deloitte, Verisk Analytics, IBM, SAP AG, and LexisNexis. Strategic collaborations and partnerships among technology providers and insurance companies are expected to drive innovation and fuel growth in the data analytics market for insurance. The integration of advanced technologies like artificial intelligence (AI), machine learning (ML), and cloud computing will further enhance the accuracy and efficiency of data analysis, creating new growth opportunities in the market. Data analytics has revolutionized the insurance industry, empowering insurers to make data-driven decisions, optimize operations, and enhance customer experiences. This report provides a comprehensive overview of the data analytics market in insurance, covering key trends, market dynamics, and competitive landscapes.

Abstract The purpose of the present study was to analyze the sex-related differences in beach handball workload. A total sample of 105 handballers (male, n=50; female, n=55) belonging to six U’16 teams, seven U’18 teams and eight senior teams were monitored in the final round of 2018-2019 beach handball tournament celebrated during 3-days congested-fixture design. The external load variables Steps, Jumps, Player Load, Total Impacts (>2G) and Total Impacts per Intensities (very low, 2-4G; low, 4-6G; moderate, 6-8G; high, 8-10G; very high, >10G) through WIMUTM inertial devices. Statistical analysis was composed by t-test and Cohen’s d for anthropometrical variables and by MANOVA and omega partial square for sex and categories related differences. Greater values in male handballers were found in height, weight and age in each categories (U’16: p<0.05; d=0.50-2.26; U’18: p<0.05; d=0.95-2.21; senior: p<0.05; d=1.01-1.99), except in age in U’18 (p=0.97; d=0.01). Respect to external workload, differences were found related to category (p<0.01; ωp²= 0.02-0.05, small) and sex (p<0.01; ωp²= 0.04-0.21, small to high), except in Steps (p=0.47; ωp²= 0.00), finding the greatest sex-related differences in U’16 category. From the differences found in anthropometrical characteristics and external workload, their evaluation during competition allows designing specific training sessions with the purpose of sports performance enhancement in beach handball.