Dataset Description

This dataset contains two files: Large_movies_data.csv and large_movies_clean.csv. The data is taken from the TMDB dataset. Originally, it contained around 900,000 movies, but some movies were dropped for recommendation purposes. Specifically, movies missing an overview were removed since the overview is one of the most important columns for analysis.

Column Description:

Large_movies_data.csv:

• Id: Unique identifier for each movie.
• Title: The title of the movie.
• Overview: A brief description of the movie.
• Genres: The genres associated with the movie.
• Cast: The main actors in the movie.
• Director: The director of the movie.
• Writers: The screenwriters of the movie.
• Production_companies: Companies involved in producing the movie.
• Producers: Producers of the movie.
• Original_language: The original language of the movie.
• Vote_count: Number of votes the movie has received.
• Vote_average: Average rating based on user votes.
• Popularity: Popularity score of the movie.
• Runtime: Duration of the movie in minutes.
• Release_date: The release date of the movie.

Total movies in Large_movies_data.csv: 663,828.

Large_movies_clean.csv:

This file is a cleaned version with unnecessary columns removed, text converted to lowercase, and many symbols removed (though some may still remain). If you find that certain features are missing, you can use the original Large_movies_data.csv.

Columns in large_movies_clean.csv: - Id: Unique identifier for each movie. - Title: The title of the movie. - Tags: Combined information from the overview, genres, and other textual columns. - Original_language: The original language of the movie. - Vote_count: Number of votes the movie has received. - Vote_average: Average rating based on user votes. - Year: Year extracted from the release date. - Month: Month extracted from the release date.

Possible Use Cases:

1. Recommendation System: A robust recommendation system can be built using this large dataset.
2. Analysis: Analyze various aspects, such as identifying actors who starred in the most popular movies, the impact of having the same writer, director, and producer on a movie, and whether independent producers create better movies.
3. Rating Prediction: Predict the average rating of a movie based on factors such as overview, genres, and cast.
4. Other Analysis: Perform other types of analysis to discover patterns in the movie industry.

If you find this dataset useful, please upvote it!

Abstract This project presents a comprehensive analysis of a company's annual sales, using the classic dataset classicmodels as the database. Python is used as the main programming language, along with the Pandas, NumPy and SQLAlchemy libraries for data manipulation and analysis, and PostgreSQL as the database management system.

The main objective of the project is to answer key questions related to the company's sales performance, such as: Which were the most profitable products and customers? Were sales goals met? The results obtained serve as input for strategic decision making in future sales campaigns.

Methodology 1. Data Extraction:

• A connection is established with the PostgreSQL database to extract the relevant data from the orders, orderdetails, customers, products and employees tables.
• A reusable function is created to read each table and load it into a Pandas DataFrame.

2. Data Cleansing and Transformation:

• An exploratory analysis of the data is performed to identify missing values, inconsistencies, and outliers.
• New variables are calculated, such as the total value of each sale, cost, and profit.
• Different DataFrames are joined using primary and foreign keys to obtain a complete view of sales.

3. Exploratory Data Analysis (EDA):

• Key metrics such as total sales, number of unique customers, and average order value are calculated.
• Data is grouped by different dimensions (products, customers, dates) to identify patterns and trends.
• Results are visualized using relevant graphics (histograms, bar charts, etc.).

4. Modeling and Prediction:

• Although the main focus of the project is descriptive, predictive modeling techniques (e.g., time series) could be explored to forecast future sales.

5. Report Generation:

• Detailed reports are created in Pandas DataFrames format that answer specific business questions.
• These reports are stored in new PostgreSQL tables for further analysis and visualization.

Results - Identification of top products and customers: The best-selling products and the customers that generate the most revenue are identified. - Analysis of sales trends: Sales trends over time are analyzed and possible factors that influence sales behavior are identified. - Calculation of key metrics: Metrics such as average profit margin and sales growth rate are calculated.

Conclusions This project demonstrates how Python and PostgreSQL can be effectively used to analyze large data sets and obtain valuable insights for business decision making. The results obtained can serve as a starting point for future research and development in the area of ​​sales analysis.

Technologies Used - Python: Pandas, NumPy, SQLAlchemy, Matplotlib/Seaborn - Database: PostgreSQL - Tools: Jupyter Notebook - Keywords: data analysis, Python, PostgreSQL, Pandas, NumPy, SQLAlchemy, EDA, sales, business intelligence

Product Analytics AI Market Outlook

According to our latest research, the global Product Analytics AI market size reached USD 8.3 billion in 2024, driven by the rapid adoption of artificial intelligence across digital product management and customer experience platforms. The market is expected to expand at a robust CAGR of 19.2% from 2025 to 2033, culminating in a projected value of USD 36.1 billion by 2033. This impressive growth trajectory is primarily fueled by the increasing demand for actionable insights into user behavior, product performance optimization, and the widespread integration of AI-powered analytics into product development lifecycles. As businesses across industries intensify their focus on data-driven decision-making, the Product Analytics AI market is poised for sustained expansion over the coming decade.

One of the principal growth factors driving the Product Analytics AI market is the escalating need for real-time, granular insights into user interactions and product usage patterns. Organizations are increasingly leveraging AI-driven analytics to decode complex behavior datasets, enabling them to tailor product features, enhance user engagement, and reduce churn. The proliferation of digital touchpoints—ranging from mobile applications to web-based platforms—has generated an unprecedented volume of data, which traditional analytics tools struggle to interpret effectively. In contrast, AI-powered product analytics platforms can ingest, process, and analyze massive datasets at scale, delivering actionable intelligence that fuels continuous product improvement. This capability is particularly vital in competitive sectors such as SaaS, e-commerce, and mobile applications, where user expectations and market dynamics evolve rapidly.

Another significant driver is the integration of AI in A/B testing, feature adoption analysis, and retention tracking, which are critical for optimizing product roadmaps and maximizing ROI. Companies are increasingly moving away from intuition-based decisions, instead relying on data-backed insights to prioritize feature releases, streamline user journeys, and validate new product concepts. AI-powered analytics platforms not only automate data collection across multiple sources but also apply advanced machine learning algorithms to uncover hidden patterns and predict future user behaviors. This empowers product managers and growth teams to proactively address user pain points, personalize experiences, and foster long-term customer loyalty. The accelerated digital transformation across industries, further amplified by remote work trends and the proliferation of cloud-native architectures, is expected to sustain the momentum in the Product Analytics AI market.

The evolving regulatory landscape and heightened focus on data privacy are also shaping the Product Analytics AI market. Enterprises are seeking solutions that not only deliver deep analytics but also ensure compliance with global data protection standards such as GDPR and CCPA. This has led to the emergence of privacy-centric AI analytics platforms that combine robust security features with advanced analytical capabilities. Additionally, the democratization of AI through user-friendly interfaces and no-code/low-code platforms is enabling a broader spectrum of stakeholders—from product managers to marketers—to harness the power of product analytics without deep technical expertise. These trends are fostering widespread adoption across both large enterprises and small and medium-sized businesses, further accelerating market growth.

From a regional perspective, North America currently dominates the Product Analytics AI market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology companies, high digital adoption rates, and a mature ecosystem for AI innovation underpin the region’s leadership. However, Asia Pacific is emerging as a high-growth market, propelled by rapid digitalization in countries such as China, India, and Southeast Asia. The region’s expanding e-commerce and SaaS sectors, coupled with increasing investments in AI infrastructure, are expected to drive significant market expansion over the forecast period. Meanwhile, Europe’s stringent data privacy regulations are spurring demand for compliant AI analytics solutions, further contributing to the global market’s diversification.

Component Analysis

The P

In testing the efficiency of ASTRAL-MP, we use several simulated and real datasets (see Table). The datasets range in the number of species (n) between 48 and 1,000 and have between 1,000 and 14,446 gene trees (k).

Name Original publication

Species (n)

Genes (k)

Type

Generations

Contraction threshold

Reps.

SV

Mirarab and Warnow (2015) 100, 200, 500, 1000 1000 Simulated

2×1062×106 Fully resolved 10

Avian

Mirarab et al. (2014a) 48 14 446, 1000 Real Unknown (order: 107) Full, 0, 33, 50, 75% 1, 10

Insects

Sayyari et al. (2017) 144 1478 Real Unknown Fully resolved 1

Note: For SV, some outlier replicates have fewer than 1m000 genes because poorly resolved gene trees are removed. For avian, the full dataset is subsampled randomly to create 10 inputs with 1m000 gene trees. In addi...

About this dataset

Context

The dataset tabulates the Excel population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 45 to 49 years years with a population of 74 (15.64%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.42%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Excel is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Excel total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel Population by Age. You can refer the same here

AI Powered Video Generator Market Outlook

The global AI-powered video generator market size was valued at approximately USD 1.5 billion in 2023 and is forecasted to reach around USD 8.7 billion by 2032, growing at a robust compound annual growth rate (CAGR) of 21.7% during the period. This remarkable growth can be attributed to the increasing demand for automated video content production across various sectors and the continuous advancements in AI technology.

One of the primary growth factors driving the AI-powered video generator market is the burgeoning need for high-quality video content. As businesses across industries increasingly rely on video for marketing, training, and customer engagement, there is a significant demand for tools that can automate video production without compromising on quality. AI-powered video generators provide an efficient and cost-effective solution, enabling companies to produce professional-grade videos quickly and at scale.

Another significant driver is the rapid adoption of artificial intelligence and machine learning technologies across various sectors. With advancements in AI algorithms and the availability of massive datasets, AI-powered video generators can now create highly customized and dynamic content. These tools are capable of understanding context, recognizing patterns, and adapting to specific requirements, making them invaluable for personalized video marketing, virtual training sessions, and other applications.

The growing popularity of video content on social media platforms and the increasing consumption of video on digital channels also contribute to the market's expansion. Platforms like YouTube, TikTok, and Instagram have seen exponential growth in video viewership, prompting brands and influencers to produce more video content. AI-powered video generators help meet this demand by streamlining the content creation process, allowing users to focus more on creativity and strategy rather than the technical aspects of video production.

AI-Powered Video Analytics is emerging as a transformative force within the video content industry, offering enhanced capabilities for understanding and interpreting video data. By leveraging advanced AI algorithms, these analytics tools can automatically detect and analyze patterns, behaviors, and events within video footage. This capability is particularly beneficial for sectors such as security, retail, and sports, where real-time insights from video data can drive decision-making and operational efficiency. As the demand for intelligent video solutions grows, AI-powered video analytics is set to play a crucial role in optimizing content delivery and enhancing viewer experiences.

Regionally, North America is expected to dominate the AI-powered video generator market during the forecast period, driven by the early adoption of advanced technologies and the presence of key market players. The Asia Pacific region is also anticipated to witness significant growth, owing to the increasing digitalization efforts and rising demand for video content in emerging economies like China and India. Europe and Latin America are expected to see steady growth, fueled by technological advancements and the growing importance of video in marketing and communication strategies.

Component Analysis

In the AI-powered video generator market, the component segment is broadly categorized into software, hardware, and services. Each component plays a crucial role in the functionality and performance of AI video generation systems, catering to various needs and preferences of end-users.

The software segment is expected to hold the largest market share, driven by the continuous advancements in AI algorithms and machine learning models. Software solutions for AI video generation encompass a wide range of functionalities, including video editing, motion graphics, special effects, and content personalization. Companies are investing heavily in research and development to enhance the capabilities of their software, making it more intuitive and user-friendly. The integration of cloud-based services also adds to the flexibility and scalability of software solutions, allowing users to access advanced features without significant upfront investments.

The hardware segment, though smaller than software, is critical for the optimal performance of AI video generators. High-performance GPUs, specialized pro

Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires significant insights into expected job runtimes and scaling behavior, resource characteristics, input data distributions, and other factors. Unable to estimate performance accurately, users frequently overprovision resources for their jobs, leading to low resource utilization and high costs. In this paper, we present major building blocks towards a collaborative approach for optimization of data processing cluster configurations based on runtime data and performance models. We believe that runtime data can be shared and used for performance models across different execution contexts, significantly reducing the reliance on the recurrence of individual processing jobs or, else, dedicated job profiling. For this, we describe how the similarity of processing jobs and cluster infrastructures can be employed to combine suitable data points from local and global job executions into accurate performance models. Furthermore, we outline approaches to performance prediction via more context-aware and reusable models. Finally, we lay out how metrics from previous executions can be combined with runtime monitoring to effectively re-configure models and clusters dynamically.

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

Background In microarray data analysis, the comparison of gene-expression profiles with respect to different conditions and the selection of biologically interesting genes are crucial tasks. Multivariate statistical methods have been applied to analyze these large datasets. Less work has been published concerning the assessment of the reliability of gene-selection procedures. Here we describe a method to assess reliability in multivariate microarray data analysis using permutation-validated principal components analysis (PCA). The approach is designed for microarray data with a group structure.

   Results
   We used PCA to detect the major sources of variance underlying the hybridization conditions followed by gene selection based on PCA-derived and permutation-based test statistics. We validated our method by applying it to well characterized yeast cell-cycle data and to two datasets from our laboratory. We could describe the major sources of variance, select informative genes and visualize the relationship of genes and arrays. We observed differences in the level of the explained variance and the interpretability of the selected genes.


   Conclusions
   Combining data visualization and permutation-based gene selection, permutation-validated PCA enables one to illustrate gene-expression variance between several conditions and to select genes by taking into account the relationship of between-group to within-group variance of genes. The method can be used to extract the leading sources of variance from microarray data, to visualize relationships between genes and hybridizations and to select informative genes in a statistically reliable manner. This selection accounts for the level of reproducibility of replicates or group structure as well as gene-specific scatter. Visualization of the data can support a straightforward biological interpretation.

The datatablesview extension for CKAN enhances the display of tabular datasets within CKAN by integrating the DataTables JavaScript library. As a fork of a previous DataTables CKAN plugin, this extension aims to provide improved functionality and maintainability for presenting data in a user-friendly and interactive tabular format. This tool focuses on making data more accessible and easier to explore directly within the CKAN interface. Key Features: Enhanced Data Visualization: Transforms standard CKAN dataset views into interactive tables using the DataTables library, providing a more engaging user experience compared to plain HTML tables. Interactive Table Functionality: Includes features such as sorting, filtering, and pagination within the data table, allowing users to easily navigate and analyze large datasets directly in the browser. Improved Data Accessibility: Makes tabular data more accessible to a wider range of users by providing intuitive tools to explore and understand the information. Presumed Customizable Appearance: Given that it is based on DataTables, users will likely be able to customize the look and feel of the tables through DataTables configuration options (note: this is an assumption based on standard DataTables usage and may require coding). Use Cases (based on typical DataTables applications): Government Data Portals: Display complex government datasets in a format that is easy for citizens to search, filter, and understand, enhancing transparency and promoting data-driven decision-making. For example, presenting financial data, population statistics, or environmental monitoring results. Research Data Repositories: Allow researchers to quickly explore and analyze large scientific datasets directly within the CKAN interface, facilitating data discovery and collaboration. Corporate Data Catalogs: Enable business users to easily access and manipulate tabular data relevant to their roles, improving data literacy and enabling data-informed business strategies. Technical Integration (inferred from CKAN extension structure): The extension likely operates by leveraging CKAN's plugin architecture to override the default dataset view for tabular data. Its implementation likely uses CKAN's templating system to render datasets using DataTables' JavaScript and CSS, enhancing data-viewing experience. Benefits & Impact: By implementing the datatablesview extension, organizations can improve the user experience when accessing and exploring tabular datasets within their CKAN instances. The enhanced interactivity and data exploration features can lead to increased data utilization, improved data literacy, and more effective data-driven decision-making within organizations and communities.

Cloud Analytics Market size was valued at USD 47.5 Billion in 2024 and is projected to reach USD 262.13 Billion by 2032, growing at a CAGR of 23.8% during the forecast period 2026-2032.

Global Cloud Analytics Market Drivers

Digital Transformation and Big Data: Organizations are increasingly digitizing their operations, leading to the generation of vast amounts of data. The need to analyze this data effectively has propelled the demand for cloud analytics solutions.

Cost Efficiency and Scalability: Cloud-based analytics offer scalable resources and cost benefits, allowing businesses to manage large datasets without significant upfront investments in infrastructure.

AI and ML Integration: The integration of AI and ML with cloud analytics enables advanced data processing capabilities, facilitating real-time insights and predictive analytics.

About this dataset

Context

The dataset tabulates the median household income in Big Sandy. It can be utilized to understand the trend in median household income and to analyze the income distribution in Big Sandy by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Big Sandy, TX Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Big Sandy, TX: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Big Sandy, TX
• Big Sandy, TX households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Big Sandy median household income. You can refer the same here

Nonproportional hazards models often arise in biomedical studies, as evidenced by a recent national kidney transplant study. During the follow-up, the effects of baseline risk factors, such as patients’ comorbidity conditions collected at transplantation, may vary over time. To model such dynamic changes of covariate effects, time-varying survival models have emerged as powerful tools. However, traditional methods of fitting time-varying effects survival model rely on an expansion of the original dataset in a repeated measurement format, which, even with a moderate sample size, leads to an extremely large working dataset. Consequently, the computational burden increases quickly as the sample size grows, and analyses of a large dataset such as our motivating example defy any existing statistical methods and software. We propose a novel application of quasi-Newton iteration method to model time-varying effects in survival analysis. We show that the algorithm converges superlinearly and is computationally efficient for large-scale datasets. We apply the proposed methods, via a stratified procedure, to analyze the national kidney transplant data and study the impact of potential risk factors on post-transplant survival. Supplementary materials for this article are available online.

AI in Disease Outbreak Prediction Market Outlook

According to our latest research, the global AI in Disease Outbreak Prediction market size reached USD 2.45 billion in 2024, driven by rapid technological advancements and increasing demand for real-time disease surveillance. The market is projected to grow at a robust CAGR of 28.7% from 2025 to 2033, resulting in a forecasted market size of USD 22.24 billion by 2033. This exponential growth is fueled by the urgent need for predictive analytics in public health, the proliferation of big data, and the increasing integration of artificial intelligence across healthcare infrastructures worldwide.

The primary growth factor for the AI in Disease Outbreak Prediction market is the escalating frequency and severity of infectious disease outbreaks, such as COVID-19, Ebola, and Zika viruses, which have underscored the critical importance of early detection and response systems. Governments and healthcare organizations are increasingly investing in AI-powered predictive tools to enhance their preparedness and response capabilities. These solutions enable the analysis of vast datasets from multiple sources, including electronic health records, social media, and environmental sensors, to identify patterns and predict potential outbreaks before they escalate. Moreover, the integration of AI with traditional epidemiological models significantly improves the accuracy and timeliness of outbreak predictions, minimizing human error and expediting critical interventions.

Another significant driver is the growing adoption of cloud-based platforms and advanced analytics in healthcare. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing organizations of all sizes to leverage sophisticated AI algorithms for disease surveillance and modeling. The emergence of machine learning and deep learning techniques has further enhanced the predictive power of these systems, enabling more nuanced and real-time analysis of complex epidemiological data. The increasing collaboration between technology providers, research institutes, and public health agencies is also fostering innovation and accelerating the development of next-generation AI tools tailored for disease outbreak prediction.

The market is also benefiting from rising awareness and regulatory support for digital health initiatives. Governments across regions are prioritizing investments in health informatics infrastructure, data standardization, and interoperability to ensure seamless data sharing and integration. This regulatory push is facilitating the adoption of AI-driven solutions in both developed and emerging economies, creating new opportunities for market players. Additionally, the proliferation of wearable devices and IoT-enabled health monitoring systems is generating vast amounts of real-time health data, further enriching the datasets available for AI-based outbreak prediction and enhancing the overall efficacy of these systems.

Regionally, North America continues to lead the AI in Disease Outbreak Prediction market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to its advanced healthcare infrastructure, high adoption rate of AI technologies, and significant governmental investments in public health surveillance. Europe is witnessing rapid growth due to increasing collaborations between public health agencies and technology firms, while Asia Pacific is emerging as a key growth engine, driven by large population bases, rising healthcare expenditures, and a growing focus on epidemic preparedness. Latin America and the Middle East & Africa are also experiencing steady growth, supported by international funding and regional health initiatives.

Component Analysis

The Component segment of the AI in Disease Outbreak Prediction market is divided into software, hardware, and services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of disease outbreak prediction, encompassing advanced analytics platforms, machine learning algorithms, and epidemiological modeling tools. These software platforms are designed to ingest, process, and analyze massive datasets from diverse sources, delivering actionable insights to healthcare professionals and policymakers. The rapid evolution of software capabilities, including natural language processing and deep learning, is facilitating more accurate and timely predic

About this dataset

Context

The dataset tabulates the median household income in Big Bend town. It can be utilized to understand the trend in median household income and to analyze the income distribution in Big Bend town by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Big Bend Town, Wisconsin Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Big Bend Town, Wisconsin: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Big Bend Town, Wisconsin
• Big Bend Town, Wisconsin households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Big Bend town median household income. You can refer the same here

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.

About this dataset

Context

The dataset tabulates the median household income in Long Grove. It can be utilized to understand the trend in median household income and to analyze the income distribution in Long Grove by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Long Grove, IL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Long Grove, IL: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Long Grove, IL
• Long Grove, IL households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Long Grove median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Long Lake township. It can be utilized to understand the trend in median household income and to analyze the income distribution in Long Lake township by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Long Lake Township, Michigan Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Long Lake Township, Michigan: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Long Lake Township, Michigan
• Long Lake Township, Michigan households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Long Lake township median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Long Lake. It can be utilized to understand the trend in median household income and to analyze the income distribution in Long Lake by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Long Lake, MN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Long Lake, MN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Long Lake, MN
• Long Lake, MN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Long Lake median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Big Prairie township. It can be utilized to understand the trend in median household income and to analyze the income distribution in Big Prairie township by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Big Prairie Township, Michigan Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Big Prairie Township, Michigan: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Big Prairie Township, Michigan
• Big Prairie Township, Michigan households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Big Prairie township median household income. You can refer the same here