Electronic health records (EHRs) have been widely adopted in recent years, but often include a high proportion of missing data, which can create difficulties in implementing machine learning and other tools of personalized medicine. Completed datasets are preferred for a number of analysis methods, and successful imputation of missing EHR data can improve interpretation and increase our power to predict health outcomes. However, use of the most popular imputation methods mainly require scripting skills, and are implemented using various packages and syntax. Thus, the implementation of a full suite of methods is generally out of reach to all except experienced data scientists. Moreover, imputation is often considered as a separate exercise from exploratory data analysis, but should be considered as art of the data exploration process. We have created a new graphical tool, ImputEHR, that is based on a Python base and allows implementation of a range of simple and sophisticated (e.g., gradient-boosted tree-based and neural network) data imputation approaches. In addition to imputation, the tool enables data exploration for informed decision-making, as well as implementing machine learning prediction tools for response data selected by the user. Although the approach works for any missing data problem, the tool is primarily motivated by problems encountered for EHR and other biomedical data. We illustrate the tool using multiple real datasets, providing performance measures of imputation and downstream predictive analysis.

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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

The 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.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

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The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request F

Exploratory data analysis.

This data set contains example data for exploration of the theory of regression based regionalization. The 90th percentile of annual maximum streamflow is provided as an example response variable for 293 streamgages in the conterminous United States. Several explanatory variables are drawn from the GAGES-II data base in order to demonstrate how multiple linear regression is applied. Example scripts demonstrate how to collect the original streamflow data provided and how to recreate the figures from the associated Techniques and Methods chapter.

The size of the Big Data in Oil & Gas Exploration and Production Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 10.20">> 10.20% during the forecast period. The oil and gas exploration and production (E&P) sector is undergoing a transformation due to the impact of big data, which significantly improves decision-making, streamlines operations, and boosts overall efficiency. Given the industry's reliance on intricate, data-heavy processes, big data technologies empower organizations to process extensive information from diverse sources, including seismic surveys, drilling data, and production metrics, in real-time. This capability enhances forecasting accuracy, optimizes reservoir management, and refines exploration strategies. Utilizing advanced analytics and machine learning algorithms allows for the detection of previously hidden patterns and trends, thereby promoting more informed decision-making and effective risk management. For instance, predictive maintenance models can foresee equipment failures, thereby reducing downtime and lowering maintenance expenses. Furthermore, big data analytics facilitate the optimization of drilling methods and production workflows, resulting in improved resource recovery and operational efficiency. The incorporation of big data within the oil and gas industry also fosters innovation in subsurface modeling, reservoir simulation, and production monitoring, enabling firms to maximize output while minimizing operational risks. Nevertheless, the implementation of big data technologies presents challenges, including data security concerns, the necessity for skilled personnel, and substantial initial investment requirements. Despite these obstacles, the adoption of big data in E&P is on the rise, propelled by its capacity to significantly enhance operational efficiency and profitability within the energy sector. Recent developments include: Cloud-based technology and solutions have become an essential tool for the energy sector, especially in the Middle East, to store data and analyze it. The COVID-19 pandemic boosted the growing cloud computing in the oil and gas industry in recent years.. Key drivers for this market are: 4., Uninterrupted and Reliable Power Supply and Heavy Deployment of DG (diesel generator) Set4.; Improvement in Technology of Diesel Generator. Potential restraints include: 4., The Growing Trend of Renewable Power Generation. Notable trends are: Big Data Software to Dominate the Market.

Exploration Services Market Outlook

The global exploration services market size was valued at approximately USD 15 billion in 2023 and is projected to reach around USD 25 billion by 2032, growing at a compound annual growth rate (CAGR) of about 6%. This growth can be attributed to the increasing demand for natural resources, technological advancements in exploration techniques, and the rising focus on sustainable and efficient resource management.

The primary growth driver for the exploration services market is the escalating global demand for energy and minerals. With the world economy consistently expanding, there is a heightened need for oil, gas, and minerals to power industries and provide materials for manufacturing. Exploration services, including geophysical, geological, and geochemical services, play a critical role in identifying and assessing these essential resources. Additionally, the transition to renewable energy sources and the increased exploration of resources such as lithium for batteries underscore the market's importance.

Technological advancements represent another significant growth factor. Innovations in exploration technologies, including remote sensing, 3D seismic imaging, and machine learning algorithms, have revolutionized the way resources are discovered and evaluated. These advanced techniques enhance the accuracy and efficiency of exploration activities, reducing costs and minimizing environmental impact. As technology continues to evolve, it will further drive the growth of the exploration services market by improving the success rates of exploration projects.

Sustainability and environmental concerns are also fueling market growth. Governments and organizations worldwide are placing greater emphasis on sustainable practices and environmental stewardship. Exploration services companies are increasingly adopting eco-friendly methods and technologies to minimize the environmental impact of their activities. This shift toward sustainability is not only a regulatory requirement but also a market differentiator, appealing to investors and stakeholders who prioritize environmental responsibility.

Regionally, the exploration services market is witnessing varied growth patterns. North America remains a dominant player, driven by substantial investments in oil and gas exploration and the presence of major mining companies. Meanwhile, Asia Pacific is experiencing rapid growth due to increasing demand for minerals and energy resources in countries like China and India. Europe is focusing on sustainable exploration practices and technological advancements, while Latin America and the Middle East & Africa are capitalizing on their abundant natural resources.

Service Type Analysis

The exploration services market is segmented by service type into geophysical services, geological services, geochemical services, drilling services, and others. Geophysical services, which include seismic surveys, magnetic and gravity surveys, and remote sensing, are essential for understanding subsurface conditions. These services provide critical data for identifying potential resource deposits and assessing their viability. The adoption of advanced technologies in geophysical services, such as 3D and 4D seismic imaging, has significantly enhanced the accuracy and efficiency of exploration activities, making this segment a key growth driver in the market.

Geological services, encompassing field mapping, sample collection, and analysis, are integral to the exploration process. These services provide valuable insights into the geological characteristics of an area, aiding in the identification of resource-rich zones. The increasing deployment of geological information systems (GIS) and other digital tools has streamlined geological data management and interpretation, further propelling the growth of this segment. Additionally, the demand for experienced geologists and advanced analytical techniques is on the rise, driven by the complexity of modern exploration projects.

Geochemical services, which involve the analysis of soil, rock, and water samples to detect the presence of minerals and hydrocarbons, are gaining prominence. Innovations in geochemical analysis, including the use of portable X-ray fluorescence (XRF) analyzers and mass spectrometry, have improved the speed and accuracy of these services. The growing focus on sustainable exploration practices is also driving the adoption of non-invasive geochemical methods, minimizing environmental impact while providing reliable data.

Market Analysis: The global Big Data in Oil & Gas Exploration & Production market is projected to surge from $674.52 million in 2025 to $1,664.15 million by 2033, registering a CAGR of 7.43% during the forecast period. The rising adoption of advanced technologies such as machine learning, data analytics, and cloud computing in oil and gas exploration and production is driving market growth. These technologies enable companies to improve data-driven decision-making and optimize operations, leading to increased efficiency and reduced costs. Key Trends and Dynamics: The market for Big Data in Oil & Gas Exploration & Production is segmented into application, technology, deployment type, end use, and region. The upstream segment accounted for the dominant share in 2025 due to the growing need for data analytics and machine learning techniques in reservoir characterization, drilling optimization, and production monitoring. Artificial intelligence (AI) is emerging as a key trend, with its applications including predictive maintenance, automated data analysis, and optimization of exploration and production processes. Cloud-based deployments are gaining traction, providing cost savings and scalability benefits to the industry. Recent developments include: , Recent developments in the Global Big Data in the Oil and Gas Exploration and Production Market highlight a significant trend toward digital transformation and advanced analytics. Companies like Halliburton and Schlumberger are increasingly integrating AI-driven solutions to enhance exploration efficiency and reduce operational costs. Additionally, Amazon Web Services and Microsoft are expanding their cloud services tailored for the oil and gas sector, enabling companies like TotalEnergies and Baker Hughes to leverage seamless data integration and analytics. Notably, several organizations are focusing on mergers and acquisitions to strengthen their data capabilities; for instance, IBM's acquisition of cloud-based analytics firms enhances its position in the market., The growth of data analytics technologies is also reflected in the valuation of companies such as Oracle and GE Oil and Gas, which are witnessing increased investments. Moreover, Weatherford and HPE are targeting collaborations to optimize data management solutions for upstream operations, potentially impacting efficiency and decision-making processes across the sector. The collective movement towards embracing big data technologies signifies a robust shift in the oil and gas industry's approach to exploration and production, ultimately driving competitive advantages and operational improvements., Big Data in Oil and Gas Exploration and Production Market Segmentation Insights, Big Data in Oil and Gas Exploration and Production Market Application Outlook. Key drivers for this market are: Enhanced reservoir management, Predictive maintenance solutions; Real-time data analytics; Improved drilling efficiency; AI-driven exploration techniques. Potential restraints include: data integration challenges, regulatory compliance pressures; advanced analytics demand; cost optimization requirements; real-time decision-making needs.

These interview data are part of the project "Looking for data: information seeking behaviour of survey data users", a study of secondary data users’ information-seeking behaviour. The overall goal of this study was to create evidence of actual information practices of users of one particular retrieval system for social science data in order to inform the development of research data infrastructures that facilitate data sharing. In the project, data were collected based on a mixed methods design. The research design included a qualitative study in the form of expert interviews and – building on the results found therein – a quantitative web survey of secondary survey data users. For the qualitative study, expert interviews with six reference persons of a large social science data archive have been conducted. They were interviewed in their role as intermediaries who provide guidance for secondary users of survey data. The knowledge from their reference work was expected to provide a condensed view of goals, practices, and problems of people who are looking for survey data. The anonymized transcripts of these interviews are provided here. They can be reviewed or reused upon request. The survey dataset from the quantitative study of secondary survey data users is downloadable through this data archive after registration. The core result of the Looking for data study is that community involvement plays a pivotal role in survey data seeking. The analyses show that survey data communities are an important determinant in survey data users' information seeking behaviour and that community involvement facilitates data seeking and has the capacity of reducing problems or barriers. The qualitative part of the study was designed and conducted using constructivist grounded theory methodology as introduced by Kathy Charmaz (2014). In line with grounded theory methodology, the interviews did not follow a fixed set of questions, but were conducted based on a guide that included areas of exploration with tentative questions. This interview guide can be obtained together with the transcript. For the Looking for data project, the data were coded and scrutinized by constant comparison, as proposed by grounded theory methodology. This analysis resulted in core categories that make up the "theory of problem-solving by community involvement". This theory was exemplified in the quantitative part of the study. For this exemplification, the following hypotheses were drawn from the qualitative study: (1) The data seeking hypotheses: (1a) When looking for data, information seeking through personal contact is used more often than impersonal ways of information seeking. (1b) Ways of information seeking (personal or impersonal) differ with experience. (2) The experience hypotheses: (2a) Experience is positively correlated with having ambitious goals. (2b) Experience is positively correlated with having more advanced requirements for data. (2c) Experience is positively correlated with having more specific problems with data. (3) The community involvement hypothesis: Experience is positively correlated with community involvement. (4) The problem solving hypothesis: Community involvement is positively correlated with problem solving strategies that require personal interactions.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This GDR submission includes those weights. The weighting was done using two different approaches: one based on expert opinions, and one based on statistical learning. The weights are intended to describe how useful a particular exploration method is for imaging each component of each play type. They may be adjusted based on the characteristics of the resource under investigation, knowledge of the quality of the dataset, or simply to reduce the impact a single dataset has on the resulting outputs. Within the DEEPEN PFA, separate sets of weights are produced for each component of each play type, since exploration methods hold different levels of importance for detecting each play component, within each play type. The weights for conventional hydrothermal systems were based on the average of the normalized weights used in the DOE-funded PFA projects that were focused on magmatic plays. This decision was made because conventional hydrothermal plays are already well-studied and understood, and therefore it is logical to use existing weights where possible. In contrast, a true PFA has never been applied to superhot EGS or supercritical plays, meaning that exploration methods have never been weighted in terms of their utility in imaging the components of these plays. To produce weights for superhot EGS and supercritical plays, two different approaches were used: one based on expert opinion and the analytical hierarchy process (AHP), and another using a statistical approach based on principal component analysis (PCA). The weights are intended to provide standardized sets of weights for each play type in all magmatic geothermal systems. Two different approaches were used to investigate whether a more data-centric approach might allow new insights into the datasets, and also to analyze how different weighting approaches impact the outcomes. The expert/AHP approach involved using an online tool (https://bpmsg.com/ahp/) with built-in forms to make pairwise comparisons which are used to rank exploration methods against one-another. The inputs are then combined in a quantitative way, ultimately producing a set of consensus-based weights. To minimize the burden on each individual participant, the forms were completed in group discussions. While the group setting means that there is potential for some opinions to outweigh others, it also provides a venue for conversation to take place, in theory leading the group to a more robust consensus then what can be achieved on an individual basis. This exercise was done with two separate groups: one consisting of U.S.-based experts, and one consisting of Iceland-based experts in magmatic geothermal systems. The two sets of weights were then averaged to produce what we will from here on refer to as the "expert opinion-based weights," or "expert weights" for short. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. More information on this approach along with the dataset used to produce the statistical weights may be found in the linked dataset below.

Expert systems are artificial intelligence tools that store and implement expert opinions and methods of analysis. The goal of this project was to test and prove the ability of expert systems to enhance the exploration process and to allow the rapid, simultaneous evaluation of numerous prospects. The project was designed to create two case-study fuzzy expert exploration (FEE) tools, one for the Lower Brushy Canyon formation of the New Mexico portion of the Delaware Basin, and the second for the Siluro-Devonian carbonates of southeast New Mexico.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments.

As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models.

Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type.

Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type.

Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model

The values of betweenness, closeness, and Eigenvector centrality for one particular subset within the analyzed medical curriculum.

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Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for business intelligence and AI-powered insights. With the rising complexity and voluminous data being generated across industries, there is a pressing need for effective data visualization tools to make data-driven decisions. This trend is particularly prominent in sectors such as healthcare, finance, and retail, where large datasets are common. Moreover, the automation of data visualization is another key driver, enabling organizations to save time and resources by streamlining the data analysis process. However, challenges such as data security concerns, lack of standardization, and integration issues persist, necessitating continuous innovation and investment in advanced technologies. Companies seeking to capitalize on this market opportunity must focus on addressing these challenges through user-friendly interfaces, security features, and seamless integration capabilities. Additionally, partnerships and collaborations with industry leaders and emerging technologies, such as machine learning and artificial intelligence, can provide a competitive edge in this rapidly evolving market.

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

Request Free SampleThe market is experiencing growth, driven by the increasing demand for intuitive and interactive ways to analyze complex data. The market encompasses a range of solutions, including visual analytics tools and cloud-based services. The services segment, which includes integration services, is also gaining traction due to the growing need for customized and comprehensive data visualization solutions. Small and Medium-sized Enterprises (SMEs) are increasingly adopting these tools to gain insights into customer behavior and enhance decision-making. Cloud-based data visualization tools are becoming increasingly popular due to their flexibility, scalability, and cost-effectiveness. Security remains a key concern, with data security features becoming a priority for companies. Additionally, the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), augmented reality (AR), and virtual reality (VR) is transforming the market, enabling more and interactive data exploration experiences. Overall, the market is poised for continued expansion, offering significant opportunities for businesses seeking to gain a competitive edge through data-driven insights.

How is this Data Visualization Tools Industry segmented?

The data visualization tools 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. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilMiddle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced substantial growth due to the increasing demand for data-driven insights in businesses. On-premises deployment of these tools allows organizations to maintain control over their data, ensuring data security, privacy, and adherence to regulatory requirements. This deployment model is ideal for enterprises dealing with sensitive information, as it restricts data transmission to cloud-based solutions. In addition, cloud-based solutions offer real-time data analysis, innovative solutions, integration services, customized dashboards, and mobile access. Advanced technologies like artificial intelligence (AI), machine learning (ML), Augmented Reality (AR), Virtual Reality (VR), and Business Intelligence (BI) are integrated into these tools to provide strategic insights from unstructured data. Data collection, maintenance, sharing, and analysis are simplified, enabling businesses to make informed decisions based on customer behavior and preferences. Key players in this market include , , and others, providing professional expertise and resources for data scientists and programmers using various programming languages.

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The On-premises segment was valued at USD 4.15 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 31% to the growth of the global market during the forecast period.Technavio’s an

Hello! Welcome to the Capstone project I have completed to earn my Data Analytics certificate through Google. I chose to complete this case study through RStudio desktop. The reason I did this is that R is the primary new concept I learned throughout this course. I wanted to embrace my curiosity and learn more about R through this project. In the beginning of this report I will provide the scenario of the case study I was given. After this I will walk you through my Data Analysis process based on the steps I learned in this course:

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

The data I used for this analysis comes from this FitBit data set: https://www.kaggle.com/datasets/arashnic/fitbit

" This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. "

The Exploration and Production (E&P) Software market is projected to reach a value of $11,110 million by 2033, registering a Compound Annual Growth Rate (CAGR) of 9.3% during the study period 2025-2033. The growth of the market is attributed to the increasing adoption of digital technologies in the oil and gas industry, rising demand for real-time data analysis, and the need for efficient reservoir management. Key drivers that are contributing to the growth of the market include the rising demand for E&P software solutions to optimize drilling operations, improve reservoir modeling, and enhance production forecasting. The increasing complexity of oil and gas exploration and production processes, the need for efficient data management, and the adoption of cloud computing are also driving market growth. The market is segmented by type, application, and region. Cloud Foundation is the dominant type segment, while Large Enterprise is the largest application segment. North America is the largest regional segment, followed by Europe and Asia Pacific.

Sonification for Exploratory Data Analysis #### Chapter 8: Sonification Models In Chapter 8 of the thesis, 6 sonification models are presented to give some examples for the framework of Model-Based Sonification, developed in Chapter 7. Sonification models determine the rendering of the sonification and possible interactions. The "model in mind" helps the user to interprete the sound with respect to the data. ##### 8.1 Data Sonograms Data Sonograms use spherical expanding shock waves to excite linear oscillators which are represented by point masses in model space. * Table 8.2, page 87: Sound examples for Data Sonograms File: Iris dataset: started in plot (a) at S0 (b) at S1 (c) at S2
10d noisy circle dataset: started in plot (c) at S0 (mean) (d) at S1 (edge)
10d Gaussian: plot (d) started at S0
3 clusters: Example 1
3 clusters: invisible columns used as output variables: Example 2 Description: Data Sonogram Sound examples for synthetic datasets and the Iris dataset Duration: about 5 s ##### 8.2 Particle Trajectory Sonification Model This sonification model explores features of a data distribution by computing the trajectories of test particles which are injected into model space and move according to Newton's laws of motion in a potential given by the dataset. * Sound example: page 93, PTSM-Ex-1 Audification of 1 particle in the potential of phi(x). * Sound example: page 93, PTSM-Ex-2 Audification of a sequence of 15 particles in the potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-3 Audification of 25 particles simultaneous in a potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-4 Audification of 25 particles simultaneous in a potential of a dataset with 1 cluster. * Sound example: page 95, PTSM-Ex-5 sigma-step sequence for a mixture of three Gaussian clusters * Sound example: page 95, PTSM-Ex-6 sigma-step sequence for a Gaussian cluster * Sound example: page 96, PTSM-Iris-1 Sonification for the Iris Dataset with 20 particles per step. * Sound example: page 96, PTSM-Iris-2 Sonification for the Iris Dataset with 3 particles per step. * Sound example: page 96, PTSM-Tetra-1 Sonification for a 4d tetrahedron clusters dataset. ##### 8.3 Markov chain Monte Carlo Sonification The McMC Sonification Model defines a exploratory process in the domain of a given density p such that the acoustic representation summarizes features of p, particularly concerning the modes of p by sound. * Sound Example: page 105, MCMC-Ex-1 McMC Sonification, stabilization of amplitudes. * Sound Example: page 106, MCMC-Ex-2 Trajectory Audification for 100 McMC steps in 3 cluster dataset * McMC Sonification for Cluster Analysis, dataset with three clusters, page 107 * Stream 1 MCMC-Ex-3.1 * Stream 2 MCMC-Ex-3.2 * Stream 3 MCMC-Ex-3.3 * Mix MCMC-Ex-3.4 * McMC Sonification for Cluster Analysis, dataset with three clusters, T =0.002s, page 107 * Stream 1 MCMC-Ex-4.1 (stream 1) * Stream 2 MCMC-Ex-4.2 (stream 2) * Stream 3 MCMC-Ex-4.3 (stream 3) * Mix MCMC-Ex-4.4 * McMC Sonification for Cluster Analysis, density with 6 modes, T=0.008s, page 107 * Stream 1 MCMC-Ex-5.1 (stream 1) * Stream 2 MCMC-Ex-5.2 (stream 2) * Stream 3 MCMC-Ex-5.3 (stream 3) * Mix MCMC-Ex-5.4 * McMC Sonification for the Iris dataset, page 108 * MCMC-Ex-6.1 * MCMC-Ex-6.2 * MCMC-Ex-6.3 * MCMC-Ex-6.4 * MCMC-Ex-6.5 * MCMC-Ex-6.6 * MCMC-Ex-6.7 * MCMC-Ex-6.8 ##### 8.4 Principal Curve Sonification Principal Curve Sonification represents data by synthesizing the soundscape while a virtual listener moves along the principal curve of the dataset through the model space. * Noisy Spiral dataset, PCS-Ex-1.1 , page 113 * Noisy Spiral dataset with variance modulation PCS-Ex-1.2 , page 114 * 9d tetrahedron cluster dataset (10 clusters) PCS-Ex-2 , page 114 * Iris dataset, class label used as pitch of auditory grains PCS-Ex-3 , page 114 ##### 8.5 Data Crystallization Sonification Model * Table 8.6, page 122: Sound examples for Crystallization Sonification for 5d Gaussian distribution File: DCS started at center, in tail, from far outside Description: DCS for dataset sampled from N{0, I_5} excited at different locations Duration: 1.4 s * Mixture of 2 Gaussians, page 122 * DCS started at point A DCS-Ex1A * DCS started at point B DCS-Ex1B * Table 8.7, page 124: Sound examples for DCS on variation of the harmonics factor File: h_omega = 1, 2, 3, 4, 5, 6 Description: DCS for a mixture of two Gaussians with varying harmonics factor Duration: 1.4 s * Table 8.8, page 124: Sound examples for DCS on variation of the energy decay time File: tau_(1/2) = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2 Description: DCS for a mixture of two Gaussians varying the energy decay time tau_(1/2) Duration: 1.4 s * Table 8.9, page 125: Sound examples for DCS on variation of the sonification time File: T = 0.2, 0.5, 1, 2, 4, 8 Description: DCS for a mixture of two Gaussians on varying the duration T Duration: 0.2s -- 8s * Table 8.10, page 125: Sound examples for DCS on variation of model space dimension File: selected columns of the dataset: (x0) (x0,x1) (x0,...,x2) (x0,...,x3) (x0,...,x4) (x0,...,x5) Description: DCS for a mixture of two Gaussians varying the dimension Duration: 1.4 s * Table 8.11, page 126: Sound examples for DCS for different excitation locations File: starting point: C0, C1, C2 Description: DCS for a mixture of three Gaussians in 10d space with different rank(S) = {2,4,8} Duration: 1.9 s * Table 8.12, page 126: Sound examples for DCS for the mixture of a 2d distribution and a 5d cluster File: condensation nucleus in (x0,x1)-plane at: (-6,0)=C1, (-3,0)=C2, ( 0,0)=C0 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s * Table 8.13, page 127: Sound examples for DCS for the cancer dataset File: condensation nucleus in (x0,x1)-plane at: benign 1, benign 2
malignant 1, malignant 2 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s ##### 8.6 Growing Neural Gas Sonification * Table 8.14, page 133: Sound examples for GNGS Probing File: Cluster C0 (2d): a, b, c
Cluster C1 (4d): a, b, c
Cluster C2 (8d): a, b, c Description: GNGS for a mixture of 3 Gaussians in 10d space Duration: 1 s * Table 8.15, page 134: Sound examples for GNGS for the noisy spiral dataset File: (a) GNG with 3 neurons 1, 2
(b) GNG with 20 neurons end, middle, inner end
(c) GNG with 45 neurons outer end, middle, close to inner end, at inner end
(d) GNG with 150 neurons outer end, in the middle, inner end
(e) GNG with 20 neurons outer end, in the middle, inner end
(f) GNG with 45 neurons outer end, in the middle, inner end Description: GNG probing sonification for 2d noisy spiral dataset Duration: 1 s * Table 8.16, page 136: Sound examples for GNG Process Monitoring Sonification for different data distributions File: Noisy spiral with 1 rotation: sound
Noisy spiral with 2 rotations: sound
Gaussian in 5d: sound
Mixture of 5d and 2d distributions: sound Description: GNG process sonification examples Duration: 5 s #### Chapter 9: Extensions #### In this chapter, two extensions for Parameter Mapping

Humans commonly engage in a variety of search behaviours, for example when looking for an object, a partner, information, or a solution to a complex problem. The success or failure of a search strategy crucially depends on the structure of the environment and the constraints it imposes on the individuals. Here we focus on environments in which individuals have to explore the solution space gradually and where their reward is determined by one unique solution they choose to exploit. This type of environment has been relatively overlooked in the past despite being relevant to numerous real-life situations, such as spatial search and various problem-solving tasks. By means of a dedicated experimental design, we show that the search behaviour of experimental participants can be well described by a simple heuristic model. Both in rich and poor solution spaces, a take-the-best procedure that ignores all but one cue at a time is capable of reproducing a diversity of observed behavioural patterns. Our approach, therefore, sheds lights on the possible cognitive mechanisms involved in human search.