Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

Across industries, organizations are increasing their hiring efforts to build larger data science arsenals: from 2020 to 2021, the percentage of surveyed organizations that employed ** data scientists or more increased from ** percent to almost ** percent. On average, the number of data scientists employed in a organization grew from ** to **.

Crime isn't a topic most people want to use mental energy to think about. We want to avoid harm, protect our loved ones, and hold on to what we claim is ours. So how do we remain vigilant without digging too deep into the filth that is crime? Data, of course. The focus of our study is to explore possible trends between crime and communities in the city of Calgary. Our purpose is visualize Calgary criminal behaviour in order to help increase awareness for both citizens and law enforcement. Through the use of our visuals, individuals can make more informed decisions to improve the overall safety of their lives. Some of the main concerns of the study include: how crime rates increase with population, which areas in Calgary have the most crime, and if crime adheres to time-sensative patterns.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for master in business administration business statistics data analytics in the U.S.

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for engineering data analytics and statistics in the U.S.

This statistic shows the ways that companies are using data and analytics worldwide as of 2018. Around ** percent of respondents stated that one of the top uses of data and analytics in their company was as a driver of strategy and change.

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

This dataset is about books. It has 2 rows and is filtered where the book is Data analysis with SPSS : a first course in applied statistics. It features 7 columns including author, publication date, language, and book publisher.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

The Data Analytics market is rapidly evolving, standing as a cornerstone for modern decision-making across various industries. With a current market size estimated to exceed billions of dollars, it has grown substantially over the past decade, driven by the explosion of big data and the increasing need for organizat

In 2023, Morningstar Advisor Workstation was by far the most popular data analytics software worldwide. According to a survey carried out between December 2022 and March 2023, the market share of Morningstar Advisor Workstation was ***** percent. It was followed by Riskalyze Elite, with ***** percent, and YCharts, with ***** percent.

The Data Analytics-as-a-Service (DaaS) market has emerged as a transformative force in the realm of data management and analysis, providing businesses with scalable solutions to harness the power of data without the burden of extensive infrastructure or technical expertise. DaaS enables organizations to access advan

Exploratory Data Analysis (EDA) Tools play a pivotal role in the modern data-driven landscape, transforming raw data into actionable insights. As businesses increasingly recognize the value of data in informing decisions, the market for EDA tools has witnessed substantial growth, driven by the rapid expansion of dat

As of 2023, most surveyed companies in the United States and Europe, or ** percent, claim to be either industry leaders in terms of data, analytics, and artificial intelligence (AI) function advancements or about the same as their industry peers.

The Manufacturing Data Analytics market has emerged as a pivotal force in the industrial landscape, transforming how manufacturers leverage data to enhance operational efficiency and drive informed decision-making. This sector focuses on harnessing large volumes of data generated from manufacturing processes, supply

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for applied statistics and data analytics in the U.S.

The Big Data Analytics Software market has emerged as a crucial component of modern business strategies, harnessing vast amounts of data to drive informed decision-making across various industries. As organizations scramble to gain a competitive edge, the ability to analyze and interpret complex datasets is indispen

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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 offeri

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.