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!

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).

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

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard t test, moderated t test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available.

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Generative AI In Data Analytics Market Size 2025-2029

The generative ai in data analytics market size is valued to increase by USD 4.62 billion, at a CAGR of 35.5% from 2024 to 2029. Democratization of data analytics and increased accessibility will drive the generative ai in data analytics market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2025-2029.
By Deployment - Cloud-based segment was valued at USD 510.60 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 621.84 million 
Market Future Opportunities 2024: USD 4624.00 million
CAGR from 2024 to 2029 : 35.5%

Market Summary

The market is experiencing significant growth as businesses worldwide seek to unlock new insights from their data through advanced technologies. This trend is driven by the democratization of data analytics and increased accessibility of AI models, which are now available in domain-specific and enterprise-tuned versions. Generative AI, a subset of artificial intelligence, uses deep learning algorithms to create new data based on existing data sets. This capability is particularly valuable in data analytics, where it can be used to generate predictions, recommendations, and even new data points. One real-world business scenario where generative AI is making a significant impact is in supply chain optimization. In this context, generative AI models can analyze historical data and generate forecasts for demand, inventory levels, and production schedules. This enables businesses to optimize their supply chain operations, reduce costs, and improve customer satisfaction. However, the adoption of generative AI in data analytics also presents challenges, particularly around data privacy, security, and governance. As businesses continue to generate and analyze increasingly large volumes of data, ensuring that it is protected and used in compliance with regulations is paramount. Despite these challenges, the benefits of generative AI in data analytics are clear, and its use is set to grow as businesses seek to gain a competitive edge through data-driven insights.

What will be the size of the Generative AI In Data Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleGenerative AI, a subset of artificial intelligence, is revolutionizing data analytics by automating data processing and analysis, enabling businesses to derive valuable insights faster and more accurately. Synthetic data generation, a key application of generative AI, allows for the creation of large, realistic datasets, addressing the challenge of insufficient data in analytics. Parallel processing methods and high-performance computing power the rapid analysis of vast datasets. Automated machine learning and hyperparameter optimization streamline model development, while model monitoring systems ensure continuous model performance. Real-time data processing and scalable data solutions facilitate data-driven decision-making, enabling businesses to respond swiftly to market trends. One significant trend in the market is the integration of AI-powered insights into business operations. For instance, probabilistic graphical models and backpropagation techniques are used to predict customer churn and optimize marketing strategies. Ensemble learning methods and transfer learning techniques enhance predictive analytics, leading to improved customer segmentation and targeted marketing. According to recent studies, businesses have achieved a 30% reduction in processing time and a 25% increase in predictive accuracy by implementing generative AI in their data analytics processes. This translates to substantial cost savings and improved operational efficiency. By embracing this technology, businesses can gain a competitive edge, making informed decisions with greater accuracy and agility.

Unpacking the Generative AI In Data Analytics Market Landscape

In the dynamic realm of data analytics, Generative AI algorithms have emerged as a game-changer, revolutionizing data processing and insights generation. Compared to traditional data mining techniques, Generative AI models can create new data points that mirror the original dataset, enabling more comprehensive data exploration and analysis (Source: Gartner). This innovation leads to a 30% increase in identified patterns and trends, resulting in improved ROI and enhanced business decision-making (IDC).

Data security protocols are paramount in this context, with Classification Algorithms and Clustering Algorithms ensuring data privacy and compliance alignment. Machine Learning Pipelines and Deep Learning Frameworks facilitate seamless integration with Predictive Modeling Tools and Automated Report Generation on Cloud

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

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.

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

Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface. The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user’s system or to familiarize oneself with the pipeline. Methods Tissue samples: Tonsil cores were extracted from a larger multi-tumor tissue microarray (TMA), which included a total of 66 unique tissues (51 malignant and semi-malignant tissues, as well as 15 non-malignant tissues). Representative tissue regions were annotated on corresponding hematoxylin and eosin (H&E)-stained sections by a board-certified surgical pathologist (S.Z.). Annotations were used to generate the 66 cores each with cores of 1mm diameter. FFPE tissue blocks were retrieved from the tissue archives of the Institute of Pathology, University Medical Center Mainz, Germany, and the Department of Dermatology, University Medical Center Mainz, Germany. The multi-tumor-TMA block was sectioned at 3µm thickness onto SuperFrost Plus microscopy slides before being processed for CODEX multiplex imaging as previously described. CODEX multiplexed imaging and processing To run the CODEX machine, the slide was taken from the storage buffer and placed in PBS for 10 minutes to equilibrate. After drying the PBS with a tissue, a flow cell was sealed onto the tissue slide. The assembled slide and flow cell were then placed in a PhenoCycler Buffer made from 10X PhenoCycler Buffer & Additive for at least 10 minutes before starting the experiment. A 96-well reporter plate was prepared with each reporter corresponding to the correct barcoded antibody for each cycle, with up to 3 reporters per cycle per well. The fluorescence reporters were mixed with 1X PhenoCycler Buffer, Additive, nuclear-staining reagent, and assay reagent according to the manufacturer's instructions. With the reporter plate and assembled slide and flow cell placed into the CODEX machine, the automated multiplexed imaging experiment was initiated. Each imaging cycle included steps for reporter binding, imaging of three fluorescent channels, and reporter stripping to prepare for the next cycle and set of markers. This was repeated until all markers were imaged. After the experiment, a .qptiff image file containing individual antibody channels and the DAPI channel was obtained. Image stitching, drift compensation, deconvolution, and cycle concatenation are performed within the Akoya PhenoCycler software. The raw imaging data output (tiff, 377.442nm per pixel for 20x CODEX) is first examined with QuPath software (https://qupath.github.io/) for inspection of staining quality. Any markers that produce unexpected patterns or low signal-to-noise ratios should be excluded from the ensuing analysis. The qptiff files must be converted into tiff files for input into SPACEc. Data preprocessing includes image stitching, drift compensation, deconvolution, and cycle concatenation performed using the Akoya Phenocycler software. The raw imaging data (qptiff, 377.442 nm/pixel for 20x CODEX) files from the Akoya PhenoCycler technology were first examined with QuPath software (https://qupath.github.io/) to inspect staining qualities. Markers with untenable patterns or low signal-to-noise ratios were excluded from further analysis. A custom CODEX analysis pipeline was used to process all acquired CODEX data (scripts available upon request). The qptiff files were converted into tiff files for tissue detection (watershed algorithm) and cell segmentation.

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.

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 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

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

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

About this dataset

Context

The dataset tabulates the median household income in Brevard County. It can be utilized to understand the trend in median household income and to analyze the income distribution in Brevard County 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).

• Brevard County, FL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Brevard County, FL: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Brevard County, FL
• Brevard County, FL 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 Brevard County median household income. You can refer the same here

Description: - This dataset includes all 22 built-in datasets from the Seaborn library, a widely used Python data visualization tool. Seaborn's built-in datasets are essential resources for anyone interested in practicing data analysis, visualization, and machine learning. They span a wide range of topics, from classic datasets like the Iris flower classification to real-world data such as Titanic survival records and diamond characteristics.

• Included Datasets:
  • Anagrams: Analysis of word anagram patterns.
  • Anscombe: Anscombe's quartet demonstrating the importance of data visualization.
  • Attention: Data on attention span variations in different scenarios.
  • Brain Networks: Connectivity data within brain networks.
  • Car Crashes: US car crash statistics.
  • Diamonds: Data on diamond properties including price, cut, and clarity.
  • Dots: Randomly generated data for scatter plot visualization.
  • Dow Jones: Historical records of the Dow Jones Industrial Average.
  • Exercise: The relationship between exercise and health metrics.
  • Flights: Monthly passenger numbers on flights.
  • FMRI: Functional MRI data capturing brain activity.
  • Geyser: Eruption times of the Old Faithful geyser.
  • Glue: Strength of glue under different conditions.
  • Health Expenditure: Health expenditure statistics across countries.
  • Iris: Famous dataset for classifying Iris species.
  • MPG: Miles per gallon for various vehicles.
  • Penguins: Data on penguin species and their features.
  • Planets: Characteristics of discovered exoplanets.
  • Sea Ice: Measurements of sea ice extent.
  • Taxis: Taxi trips data in a city.
  • Tips: Tipping data collected from a restaurant.
  • Titanic: Survival data from the Titanic disaster.

This complete collection serves as an excellent starting point for anyone looking to improve their data science skills, offering a wide array of datasets suitable for both beginners and advanced users.

About this dataset

Context

The dataset tabulates the median household income in Billings. It can be utilized to understand the trend in median household income and to analyze the income distribution in Billings 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).

• Billings, OK Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Billings, OK: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Billings, OK
• Billings, OK 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 Billings median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the Missouri household income by age. The dataset can be utilized to understand the age-based income distribution of Missouri income.

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).

• Missouri annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Missouri household incomes: Comparative analysis across 16 income brackets

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 Missouri income distribution by age. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Woodland Park. It can be utilized to understand the trend in median household income and to analyze the income distribution in Woodland Park 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).

• Woodland Park, CO Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Woodland Park, CO: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Woodland Park, CO
• Woodland Park, CO 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 Woodland Park median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Franklin County. It can be utilized to understand the trend in median household income and to analyze the income distribution in Franklin County 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).

• Franklin County, ID Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Franklin County, ID: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Franklin County, ID
• Franklin County, ID 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 Franklin County median household income. You can refer the same here