The Data Analytics Market size was valued at USD 41.05 USD billion in 2023 and is projected to reach USD 222.39 USD billion by 2032, exhibiting a CAGR of 27.3 % during the forecast period. Key drivers for this market are: Rising Demand for Edge Computing Likely to Boost Market Growth. Potential restraints include: Data Security Concerns to Impede the Market Progress . Notable trends are: Metadata-Driven Data Fabric Solutions to Expand Market Growth.

IoT-Town is focused around using IoT sensors on the Helium Network. We will use sensors to mine and analyze data for the purpose of allowing Data Scientists and/or students a way to analyze the data from these sensors in a way that they can tap into the unfounded info that's within.

The data is being collected through two Dragino Temperature and Humidity sensors and two Dragino Door-Status sensors.

Through a decoder function, the data from these IoT devices were extracted and serialized to a Google Sheet integration for user-friendly data analysis.

The data for the temperature and humidity sensors were sporadic and had a handful of misreadings ( outliers) which may make it more challenging to perform analysis on this data.

For backup collection methods, we utilized other integrations in the Helium console such as Datacake, Cayenne, MyDevices, Akenza to check the accuracy of data transmission and proved it checked out, so we continued with phasing out those integrations and relied solely on Google Sheet Integrations because of the UI/UX experience it provides as far as customizing the data visualizations the way we wanted.

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

This dataset provides geospatial location data and scripts used to analyze the relationship between MODIS-derived NDVI and solar and sensor angles in a pinyon-juniper ecosystem in Grand Canyon National Park. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The file GrcaScpnModisCellCenters.csv contains locations (latitude-longitude) of all the 250-m MODIS (MOD09GQ) cell centers associated with the Grand Canyon pinyon-juniper ecosystem that the Southern Colorado Plateau Network (SCPN) is monitoring through its land surface phenology and integrated upland monitoring programs. The file SolarSensorAngles.csv contains MODIS angle measurements for the pixel at the phenocam location plus a random 100 point subset of pixels within the GRCA-PJ ecosystem. The script files (folder: 'Code') consist of 1) a Google Earth Engine (GEE) script used to download MODIS data through the GEE javascript interface, and 2) a script used to calculate derived variables and to test relationships between solar and sensor angles and NDVI using the statistical software package 'R'. The file Fig_8_NdviSolarSensor.JPG shows NDVI dependence on solar and sensor geometry demonstrated for both a single pixel/year and for multiple pixels over time. (Left) MODIS NDVI versus solar-to-sensor angle for the Grand Canyon phenocam location in 2018, the year for which there is corresponding phenocam data. (Right) Modeled r-squared values by year for 100 randomly selected MODIS pixels in the SCPN-monitored Grand Canyon pinyon-juniper ecosystem. The model for forward-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle. The model for back-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle + sensor zenith angle. Boxplots show interquartile ranges; whiskers extend to 10th and 90th percentiles. The horizontal line marking the average median value for forward-scatter r-squared (0.835) is nearly indistinguishable from the back-scatter line (0.833). The dataset folder also includes supplemental R-project and packrat files that allow the user to apply the workflow by opening a project that will use the same package versions used in this study (eg, .folders Rproj.user, and packrat, and files .RData, and PhenocamPR.Rproj). The empty folder GEE_DataAngles is included so that the user can save the data files from the Google Earth Engine scripts to this location, where they can then be incorporated into the r-processing scripts without needing to change folder names. To successfully use the packrat information to replicate the exact processing steps that were used, the user should refer to packrat documentation available at https://cran.r-project.org/web/packages/packrat/index.html and at https://www.rdocumentation.org/packages/packrat/versions/0.5.0. Alternatively, the user may also use the descriptive documentation phenopix package documentation, and description/references provided in the associated journal article to process the data to achieve the same results using newer packages or other software programs.

This dataset contains detailed information about the COVID-19 pandemic. The inspiration behind this dataset is to analyze trends, identify patterns, and understand the global impact of COVID-19 through SQL queries. It is designed for anyone interested in data exploration and real-world analytics.

Religious tourism is a growing sector of the tourism market because of the many social and cultural changes in the 21st century. Pilgrimage centers worldwide are considered important at the levels of religion, heritage, and culture of tourism. Despite the popularity of journeys to pilgrimage centers and their global importance, there is still a lack of knowledge about the dimensionality and impact of socio-demographic factors on visiting these centers. This study aims to (i) establish the motivational dimensions of the pilgrimage to Mecca (ii) identify the relationship between socio-demographic aspects of pilgrims and the motivation (iii) determine the relationship between socio-demographic aspects of pilgrims, satisfaction, and loyalty. The research was carried out on pilgrims who had visited Mecca. The sample consisted of 384 online surveys. Factor analysis and multiple regression method were applied to a analyze data. The results show three motivational dimensions: religious, social, and cultural, and shopping. Additionally, there is evidence of a relationship between age, marital status and average daily expenditure per person with some motivational variables. Similarly, a relationship was found between average daily expenditure per person and other variables such as satisfaction and loyalty. This study helps tourism companies pay attention to pilgrims’ the socio-demographic characteristics of and match them with their motivation, satisfaction, and loyalty during the planning process.

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

A global scale dataset of terrestrial mammal species richness within three trophic groupings. Trophic groups are predators, herbivores, and omnivores. Spatial resolution is 30 x 30 km. Variable descriptions are as follows: FID_1: An identifier variable. y_coord: Latitudinal value. mamm_h_20: The number of herbivore species present in the pixel. mamm_o_20: The number of omnivore species present in the pixel. mamm_p_20: The number of predator species present in the pixel. total_20: The total number of mammals present in the pixel. p_herb_20: The proportion of total species in the pixel that are herbivores. p_omni_20: The proportion of total species in the pixel that are omnivores. p_pred_20: The proportion of total species in the pixel that are predators. GPP: Gross Primary Production of the pixel; extracted from doi 10.1038/sdata.2017.165. mean_temp: The mean annual temperature of the pixel in celsius; extracted from WorldClim v.2. mean_precip: The mean annual precipitation of the pixel in centimeters; extracted from WorldClim v.2. temp_season: The temperature seasonality of the pixel as standard deviation multiplied by 100. precip_season: The precipitation seasonality of the pixel as the coefficient of variation. iso: The isothermality of the pixel as diurnal temperature range divided by annual temperature range. A text file of the code used to analyze data in the R Statistical computing environment is also included.

Background Microarray technologies are emerging as a promising tool for genomic studies. The challenge now is how to analyze the resulting large amounts of data. Clustering techniques have been widely applied in analyzing microarray gene-expression data. However, normal mixture model-based cluster analysis has not been widely used for such data, although it has a solid probabilistic foundation. Here, we introduce and illustrate its use in detecting differentially expressed genes. In particular, we do not cluster gene-expression patterns but a summary statistic, the t-statistic. Results The method is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle-ear infection. Three clusters were found, two of which contain more than 95% genes with almost no altered gene-expression levels, whereas the third one has 30 genes with more or less differential gene-expression levels. Conclusions Our results indicate that model-based clustering of t-statistics (and possibly other summary statistics) can be a useful statistical tool to exploit differential gene expression for microarray data.

Mass Spectrometry Data Analysis AI Market Outlook

According to our latest research, the global mass spectrometry data analysis AI market size reached USD 1.18 billion in 2024, reflecting robust adoption of artificial intelligence technologies in analytical laboratories worldwide. The market is expected to expand at a CAGR of 18.7% from 2025 to 2033, reaching a forecasted value of USD 6.11 billion by 2033. This impressive growth trajectory is primarily driven by the escalating complexity and volume of mass spectrometry data, the increasing demand for high-throughput and precise analytical workflows, and the widespread integration of AI-powered tools to enhance data interpretation and operational efficiency across various sectors.

A key growth factor for the mass spectrometry data analysis AI market is the exponential increase in data complexity generated by advanced mass spectrometry platforms. Modern mass spectrometers, such as high-resolution and tandem mass spectrometry systems, produce vast datasets that are often too intricate for manual analysis. AI-powered solutions are being widely adopted to automate data processing, pattern recognition, and anomaly detection, thereby significantly reducing the time required for data interpretation and minimizing human error. These AI-driven analytical capabilities are particularly valuable in fields like proteomics and metabolomics, where the identification and quantification of thousands of biomolecules require sophisticated computational approaches. As a result, laboratories and research institutions are increasingly investing in AI-enabled mass spectrometry data analysis tools to enhance productivity and scientific discovery.

Another major driver fueling market expansion is the growing emphasis on precision medicine and personalized healthcare. The integration of mass spectrometry with AI is revolutionizing clinical diagnostics by enabling highly sensitive and specific detection of disease biomarkers. AI algorithms can rapidly analyze complex clinical samples, extract meaningful patterns, and provide actionable insights for early disease detection, prognosis, and therapeutic monitoring. Pharmaceutical companies are also leveraging AI-powered mass spectrometry data analysis for drug discovery, pharmacokinetics, and toxicology studies, significantly accelerating the development pipeline. This convergence of AI and mass spectrometry in healthcare and pharmaceutical research is expected to continue propelling market growth over the forecast period.

Furthermore, the adoption of cloud-based deployment models and the proliferation of software-as-a-service (SaaS) solutions are lowering barriers to entry and expanding the accessibility of advanced data analysis tools. Cloud platforms provide scalable computing resources, seamless collaboration, and centralized data management, making it easier for organizations of all sizes to harness the power of AI-driven mass spectrometry analysis. This trend is particularly evident among academic and research institutes, which benefit from flexible and cost-effective access to high-performance analytical capabilities. As cloud infrastructure matures and data security concerns are addressed, the migration towards cloud-based AI solutions is expected to accelerate, further boosting the market.

From a regional perspective, North America currently dominates the mass spectrometry data analysis AI market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The strong presence of leading pharmaceutical and biotechnology companies, well-established research infrastructure, and proactive regulatory support for digital transformation are key factors driving market leadership in these regions. Asia Pacific is witnessing the fastest growth, fueled by increasing investments in life sciences research, expanding healthcare infrastructure, and the rapid adoption of advanced analytical technologies in countries such as China, Japan, and India. As global research collaborations intensify and emerging economies ramp up their R&D activities, regional market dynamics are expected to evolve rapidly over the coming years.

This dataset is designed for skill gap analysis, focusing on evaluating the skill gap between students’ current skills and industry requirements. It provides insights into technical skills, soft skills, career interests, and challenges, helping in skill gap analysis to identify areas for improvement.

By leveraging this dataset, educators, recruiters, and researchers can conduct skill gap analysis to assess students’ job readiness and tailor training programs accordingly. It serves as a valuable resource for identifying skill deficiencies and skill gaps improving career guidance, and enhancing curriculum design through targeted skill gap analysis.

Following is the column descriptors: Name - Student's full name. email_id - Student's email address. Year - The academic year the student is currently in (e.g., 1st Year, 2nd Year, etc.). Current Course - The course the student is currently pursuing (e.g., B.Tech CSE, MBA, etc.). Technical Skills - List of technical skills possessed by the student (e.g., Python, Data Analysis, Cloud Computing). Programming Languages - Programming languages known by the student (e.g., Python, Java, C++). Rating - Self-assessed rating of technical skills on a scale of 1 to 5. Soft Skills - List of soft skills (e.g., Communication, Leadership, Teamwork). Rating - Self-assessed rating of soft skills on a scale of 1 to 5. Projects - Indicates whether the student has worked on any projects (Yes/No). Career Interest - The student's preferred career path (e.g., Data Scientist, Software Engineer). Challenges - Challenges faced while applying for jobs/internships (e.g., Lack of experience, Resume building issues).

In recent years, data science agents powered by Large Language Models (LLMs), known as “data agents,” have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software.

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Streaming Analytics Market Size 2024-2028

The streaming analytics market size is forecast to increase by USD 39.7 at a CAGR of 34.63% between 2023 and 2028.

The market is experiencing significant growth due to the increasing need to improve business efficiency in various industries. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies is a key trend driving market growth. These technologies enable real-time data processing and analysis, leading to faster decision-making and improved operational performance. However, the integration of streaming analytics solutions with legacy systems poses a challenge. IoT platforms play a crucial role In the market, as IoT-driven devices generate vast amounts of data that require real-time analysis. Predictive analytics is another area of focus, as it allows businesses to anticipate future trends and customer behavior, leading to proactive decision-making.Overall, the market is expected to continue growing, driven by the need for real-time data processing and analysis in various sectors.

What will be the Size of the Streaming Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for real-time insights from big data generated by emerging technologies such as IoT and API-driven applications. This market is driven by the strategic shift towards digitization and cloud solutions among large enterprises and small to medium-sized businesses (SMEs) across various industries, including retail. Legacy systems are being replaced with modern streaming analytics platforms to enhance data connectivity and improve production and demand response. The financial impact of real-time analytics is substantial, with applications in fraud detection, predictive maintenance, and operational efficiency. The integration of artificial intelligence (AI) and machine learning algorithms further enhances the market's potential, enabling businesses to gain valuable insights from their data streams.Overall, the market is poised for continued expansion as more organizations recognize the value of real-time data processing and analysis.

How is this Streaming Analytics Industry segmented and which is the largest segment?

The streaming analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. DeploymentCloudOn premisesTypeSoftwareServicesGeographyNorth AmericaCanadaUSAPACChinaJapanEuropeUKMiddle East and AfricaSouth America

By Deployment Insights

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

Cloud-deployed streaming analytics solutions enable businesses to analyze data in real time using remote computing resources, such as the cloud. This deployment model streamlines business intelligence processes by collecting, integrating, and presenting derived insights instantaneously, enhancing decision-making efficiency. The cloud segment's growth is driven by benefits like quick deployment, flexibility, scalability, and real-time data visibility. Service providers offer these capabilities with flexible payment structures, including pay-as-you-go. Advanced solutions integrate AI, API, and event-streaming analytics capabilities, ensuring compliance with regulations, optimizing business processes, and providing valuable data accessibility. Cloud adoption in various sectors, including finance, healthcare, retail, and telecom, is increasing due to the need for real-time predictive modeling and fraud detection.SMEs and startups also benefit from these solutions due to their ease of use and cost-effectiveness. In conclusion, cloud-based streaming analytics solutions offer significant advantages, making them an essential tool for organizations seeking to digitize and modernize their IT infrastructure.

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The Cloud segment was valued at USD 4.40 in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 34% 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 Free Sample

In North America, the region's early adoption of advanced technology and high data generation make it a significant market for streaming analytics. The vast amounts of data produced in this tech-mature region necessitate intelligent analysis to uncover valuable relationships and insights. Advanced software solutions, including AI, virtualization, and cloud computing, are easily adopted to enh

Chronic venous thrombosis is difficult to treat with current approaches, in part due to its extensive collagen network. Histotripsy is a focused ultrasound therapy that uses bubbles to disrupt tissue, and has several features that make its use for chronic thrombosis attractive. This study used a porcine model to test the hypothesis that portions of chronic thrombosis undergoing active remodeling can be disrupted by histotripsy. Vessels occluded for 30 days were found to be composed primarily of disorganized collagen, and cells that promoted disorganized collagen structures. Histotripsy pressure pulses were able to generate bubbles in all cases, resulting in a 59% mean reduction in the thrombus area. Further, disorganized collagen and cells within the samples were damaged to a greater extent than organized collagen. These data indicate there are chronic venous thrombosis components susceptible to mechanical perturbation by histotripsy despite the extensive collagen.These files were used for analysis of data.

Single-Cell Data Analysis Software Market Outlook

According to our latest research, the global Single-Cell Data Analysis Software market size reached USD 498.6 million in 2024, driven by increasing demand for high-resolution cellular analysis in life sciences and healthcare. The market is experiencing robust expansion with a CAGR of 15.2% from 2025 to 2033, and is projected to reach USD 1,522.9 million by 2033. This impressive growth trajectory is primarily attributed to advancements in single-cell sequencing technologies, the proliferation of precision medicine, and the rising adoption of artificial intelligence and machine learning in bioinformatics.

The growth of the Single-Cell Data Analysis Software market is significantly propelled by the rapid evolution of next-generation sequencing (NGS) technologies and the increasing need for comprehensive single-cell analysis in both research and clinical settings. As researchers strive to unravel cellular heterogeneity and gain deeper insights into complex biological systems, the demand for robust data analysis tools has surged. Single-cell data analysis software enables scientists to process, visualize, and interpret large-scale datasets, facilitating the identification of rare cell populations, novel biomarkers, and disease mechanisms. The integration of advanced algorithms and user-friendly interfaces has further enhanced the accessibility and adoption of these solutions across various end-user segments, including academic and research institutes, biotechnology and pharmaceutical companies, and hospitals and clinics.

Another key driver for market growth is the expanding application of single-cell analysis in precision medicine and drug discovery. The ability to analyze gene expression, protein levels, and epigenetic modifications at the single-cell level has revolutionized the understanding of disease pathogenesis and therapeutic response. This has led to a surge in demand for specialized software capable of managing complex, multi-omics datasets and generating actionable insights for personalized treatment strategies. Furthermore, the ongoing trend of integrating artificial intelligence and machine learning in single-cell data analysis is enabling more accurate predictions and faster data processing, thus accelerating the pace of biomedical research and clinical diagnostics.

The increasing collaboration between academia, industry, and government agencies is also contributing to market expansion. Public and private investments in single-cell genomics research are fostering innovation in data analysis software, while strategic partnerships and acquisitions are facilitating the development of comprehensive, end-to-end solutions. Additionally, the growing awareness of the potential of single-cell analysis in oncology, immunology, and regenerative medicine is encouraging the adoption of advanced software platforms worldwide. However, challenges such as data privacy concerns, high implementation costs, and the need for skilled personnel may pose restraints to market growth, particularly in low-resource settings.

From a regional perspective, North America continues to dominate the Single-Cell Data Analysis Software market, owing to its well-established healthcare infrastructure, strong presence of leading biotechnology and pharmaceutical companies, and substantial investments in genomics research. Europe follows closely, supported by robust government funding and a thriving life sciences sector. The Asia Pacific region is emerging as a lucrative market, driven by rising healthcare expenditure, expanding research capabilities, and increasing adoption of advanced technologies in countries such as China, Japan, and India. Latin America and the Middle East & Africa are also witnessing gradual growth, albeit at a slower pace, due to improving healthcare infrastructure and growing awareness of single-cell analysis applications.

Component Analysis

The Single-Cell Data Analysis Software market by component is broadly segmented into software and services, each playing a pivotal role in the overall ecosystem. Software solutions form the backbone of this market, offering a wide array of functionalities such as data preprocessing, quality control, clustering, visualization, and integration of multi-omics data. The increasing complexity and volume of single-cell datasets have driven the development of sophisticated software platforms equipped with advanced analytics, machine learning algorithms, and intuitive user interfaces. These platfo

In-Line Data Analytics DPU Market Outlook

According to our latest research, the global In-Line Data Analytics DPU market size in 2024 stands at USD 1.34 billion, with an impressive compound annual growth rate (CAGR) of 28.2% expected through the forecast period of 2025 to 2033. By the end of 2033, the market is projected to reach USD 13.41 billion. The surge in demand for real-time data processing and advanced analytics in data centers, telecommunications networks, and financial institutions is a primary growth factor driving the expansion of the In-Line Data Analytics DPU market. Organizations across industries are increasingly adopting data processing units (DPUs) to offload and accelerate complex analytics workloads directly within data streams, significantly improving operational efficiency and reducing latency.

The growth trajectory of the In-Line Data Analytics DPU market is fueled by the exponential increase in data volumes generated by digital transformation initiatives, IoT devices, and cloud-native applications. Enterprises are under immense pressure to derive actionable insights from data in real time, necessitating the deployment of advanced analytics solutions that can process and analyze data at the edge or within the network. DPUs, with their specialized hardware and software capabilities, enable organizations to achieve high-throughput, low-latency data analytics, which is critical for applications such as fraud detection, network security, and dynamic resource allocation. The integration of AI and machine learning algorithms within DPUs further enhances their analytical prowess, making them indispensable in modern IT and business environments.

Another significant growth driver is the escalating need for robust security analytics and compliance in highly regulated sectors such as BFSI, healthcare, and government. In-Line Data Analytics DPUs enable real-time monitoring and analysis of network traffic, detecting anomalies and potential threats before they can compromise sensitive data or disrupt operations. This proactive approach to security is increasingly favored over traditional, reactive methods, especially as cyber threats become more sophisticated. Additionally, regulatory mandates around data privacy and real-time reporting are compelling organizations to invest in advanced analytics infrastructure, further boosting the adoption of DPUs.

Technological advancements and the proliferation of cloud computing are also contributing to the rapid expansion of the In-Line Data Analytics DPU market. Cloud service providers are integrating DPU-based analytics solutions to offer scalable, high-performance data processing capabilities to their clients. The shift towards hybrid and multi-cloud environments is creating new opportunities for DPU vendors, as enterprises seek flexible deployment models that can seamlessly support both on-premises and cloud-based analytics workloads. As a result, the market is witnessing a wave of innovation, with vendors introducing DPUs that support a wide array of analytics applications and deployment scenarios.

From a regional perspective, North America continues to dominate the global In-Line Data Analytics DPU market, accounting for the largest share in 2024. The presence of leading technology companies, robust IT infrastructure, and high adoption rates of advanced analytics solutions are key factors underpinning the region's leadership. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid digitization, expanding telecommunications networks, and increasing investments in smart city initiatives. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by rising awareness of the benefits of real-time data analytics and increasing regulatory compliance requirements.

Component Analysis

The In-Line Data Analytics DPU market by component is segmented into hardware, software, and services, each playing a pivotal role in delivering comprehensive analyti

Opinion surveys often employ multiple items to measure the respondent's underlying value, belief, or attitude. To analyze such types of data, researchers have often followed a two-step approach by first constructing a composite measure and then using it in subsequent analysis. This paper presents a class of hierarchical item response models that help integrate measurement and analysis. In this approach, individual responses to multiple items stem from a latent preference, of which both the mean and variance may depend on observed covariates. Compared with the two-step approach, the hierarchical approach reduces bias, increases efficiency, and facilitates direct comparison across surveys covering different sets of items. Moreover, it enables us to investigate not only how preferences differ among groups, vary across regions, and evolve over time, but also levels, patterns, and trends of attitude polarization and ideological constraint. An open-source R package, hIRT, is available for fitting the proposed models.

Case Study: How Does a Bike-Share Navigate Speedy Success?¶

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path. By the end of this lesson, you will have a portfolio-ready case study. Download the packet and reference the details of this case study anytime. Then, when you begin your job hunt, your case study will be a tangible way to demonstrate your knowledge and skills to potential employers.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Characters and teams ● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. ● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. ● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them. ● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

About the company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends

Three questions will guide the future marketing program:

How do annual members and casual riders use Cyclistic bikes differently? Why would casual riders buy Cyclistic annual memberships? How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual rid...