The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-12-02 to 2025-12-01 about stock market, average, industry, and USA.

Context

The script used to acquire all of the following data can be found in this GitHub repository. This repository also contains the modeling codes and will be updated continually, so welcome starring or watching!

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here provided a dataset with historical stock prices (last 12 years) for 29 of 30 DJIA companies (excluding 'V' because it does not have the whole 12 years data).

   ['MMM', 'AXP', 'AAPL', 'BA', 'CAT', 'CVX', 'CSCO', 'KO', 'DIS', 'XOM', 'GE',

   'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'MCD', 'MRK', 'MSFT', 'NKE', 'PFE',

   'PG', 'TRV', 'UTX', 'UNH', 'VZ', 'WMT', 'GOOGL', 'AMZN', 'AABA']

In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 13 years of stock data (in the all_stocks_2006-01-01_to_2018-01-01.csv and corresponding folder) and a smaller version of the dataset (all_stocks_2017-01-01_to_2018-01-01.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_2006-01-01_to_2018-01-01 contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_2006-01-01_to_2018-01-01.csv and all_stocks_2017-01-01_to_2018-01-01.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Acknowledgement

This Data description is adapted from the dataset named 'S&P 500 Stock data'. This data is scrapped from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and the Market.

Overview This dataset contains combined news headlines and stock market data, specifically the Dow Jones Industrial Average (DJIA). It is designed to facilitate the study of the relationship between news sentiment and stock market movements.

Dataset Description The dataset includes the following features:

Date: The date corresponding to the news headlines and DJIA data. Label: A binary label indicating whether the DJIA increased (1) or decreased (0) on that particular date. Top 1 to Top 25: The top 25 news headlines for each date. These columns contain the news headlines that were published on that date. Usage This dataset can be utilized for various analytical and modeling purposes, including but not limited to:

Sentiment Analysis: Develop models to analyze the sentiment of news headlines and correlate them with stock market movements. Stock Market Prediction: Build predictive models to forecast stock market trends based on news headlines. Text Preprocessing Techniques: Implement and evaluate text preprocessing methods such as tokenization, stemming, and lemmatization. Natural Language Processing: Apply NLP techniques to extract meaningful insights from news headlines. Potential Applications Enhancing trading strategies by incorporating news sentiment analysis. Building robust machine learning pipelines for financial forecasting. Studying the impact of news on stock market volatility. Experimenting with various NLP and machine learning techniques for financial data. File information The dataset is provided in CSV format and contains 1990 records and 27 columns.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Dow City. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Dow City. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Dow City, householders within the 25 to 44 years age group have the highest median household income at $96,250, followed by those in the 45 to 64 years age group with an income of $64,167. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $46,458. Notably, householders within the under 25 years age group, had the lowest median household income at $33,750.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Dow City median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Dow City 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 Dow City. The dataset can be utilized to understand the population distribution of Dow City by age. For example, using this dataset, we can identify the largest age group in Dow City.

Key observations

The largest age group in Dow City, IA was for the group of age 20 to 24 years years with a population of 63 (16.41%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Dow City, IA was the 85 years and over years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Dow City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Dow City 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 Dow City Population by Age. You can refer the same here

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Experimental studies in the area of Psychology and Behavioral Economics have suggested that people change their search pattern in response to positive and negative events. Using Internet search data provided by Google, we investigated the relationship between stock-specific events and related Google searches. We studied daily data from 13 stocks from the Dow-Jones and NASDAQ100 indices, over a period of 4 trading years. Focusing on periods in which stocks were extensively searched (Intensive Search Periods), we found a correlation between the magnitude of stock returns at the beginning of the period and the volume, peak, and duration of search generated during the period. This relation between magnitudes of stock returns and subsequent searches was considerably magnified in periods following negative stock returns. Yet, we did not find that intensive search periods following losses were associated with more Google searches than periods following gains. Thus, rather than increasing search, losses improved the fit between people’s search behavior and the extent of real-world events triggering the search. The findings demonstrate the robustness of the attentional effect of losses.

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is google trends of stock (Dow, S&P500 index, Nasdaq index to update later) from pytrends (It is not official). Contains value of trend's result normalized as date of about 1 year (2020-06-14, 2021-06-06).

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want to compare stock's recent price, you should check this data set and refer to the Notebook.

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In Trend_sp500.json It is presented that trend of google to be normalized by index of S&P500

• In Trend_dow.json. It is presented that trend of google to be normalized by index of Dow

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

It is funning model comparing trend of google if it has correlation or not.

This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code,Please see another data sets like S&P500 price and financial statements, Dow price and financial statements

Russia's main stock market index, the MOEX, fell to 2681 points on December 2, 2025, losing 0.20% from the previous session. Over the past month, the index has climbed 4.30% and is up 5.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on December of 2025.

The 3D Hydrography Program (3DHP) data is an integrated, National, 3D-enabled hydrologic dataset derived from the USGS 3D Elevation Program (3DEP) data. For areas where Elevation-derived Hydrography (EDH) has not yet been collected, 3DHP data is supplemented by hydrologic vector data from the National Hydrography Dataset (NHD). As further EDH data is collected, it will replace the NHD data in those areas. 3DHP data ingested from EDH sources includes ‘value added’ catchments and flowline network derivative attributes. All the data is open and non-proprietary. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. This dataset is not intended to be used for site-specific regulatory determinations. 3DHP datasets include a three-dimensional (3D) hydrography network generated from, and integrated with, elevation data from the 3DEP to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. This product is new in federal fiscal year 2025 (FY25), and consists only of vector data in a series of feature classes. The product represents the 3DHP dataset and the schema in which it is contained as of September 30, 2024 Future Annual Staged Product releases will reflect the schema at the time the product is generated and include more EDH-sourced data holdings.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Dow City: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 32(15.02%) households where the householder is under 25 years old, 41(19.25%) households with a householder aged between 25 and 44 years, 93(43.66%) households with a householder aged between 45 and 64 years, and 47(22.07%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Dow City, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Dow City median household income by age. You can refer the same here

Note: Please use this link to leave the data view and to see the full description: https://data.ct.gov/Environment-and-Natural-Resources/Spill-Incidents/wr2a-rnsg Description of Dataset: This data set represents information reported between July 1, 1996 and June 30, 2022 to the Department of Energy and Environmental Protection (CT DEEP), generally to the CT DEEP Dispatch Center, regarding releases of substances to the environment, generally through accidental spills. For information related to releases reported to CT DEEP from July 1, 2022 to the present, go to Incident Reports for Releases Reported to CT DEEP July 1, 2022 to present at: https://connecticut.hazconnect.com/listincidentpublic.aspx For a dataset related to releases reported to CT DEEP from July 1, 2022 to recent refer to the CT Open Data dataset: https://data.ct.gov/Environment-and-Natural-Resources/Spill-Incidents-from-July-1-2022-to-Recent-for-Dow/ffju-s5c5 Connecticut General Statutes Section 22a-450 requires anyone who causes any discharge, spillage, uncontrolled loss, seepage or filtration of oil or petroleum or chemical liquids or solid, liquid or gaseous products, or hazardous wastes which poses a potential threat to human health or the environment to report that release to the CT DEEP. Reports of releases from other persons are also included in this dataset. Examples of what may be included in a spill incident record includes: Administrative information (unique spill case number). Spill date/time. Location. Spill source and cause. Material(s) and material type spilled. Quantity spilled. Measurement units. Surface water bodies affected. Data limitations and factors to consider when using this data: This data is limited to information about a spill incident as it was known at the time it was reported to CT DEEP. Although some data reflects updated information after the time of the initial notification, CT DEEP is unable to field check and verify all reported information. Therefore, information later determined to be incomplete or inaccurate may exist in this data set. There may also be spelling errors or other unintentionally inaccurate data that was transcribed in the spill incident report. This dataset is a subset of records and information that may be available about releases that have occurred at specific locations. This dataset does not replace a full review of files publicly available either on-line and/or at CT DEEP’s Records Center. For a complete review of agency records for this or other agency programs, you can perform your own search in our DEEP public file room located at 79 Elm Street, Hartford CT or at our DEEP Online Search Portal at: https://filings.deep.ct.gov/DEEPDocumentSearchPortal/Home . If errors are found or there are questions about the data, please contact the program unit using the following email address: DEEP.SpillsDocs@ct.gov

This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

1. Portfolio

This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

• Columns:
  • Ticker: The stock symbol (e.g., AAPL, TSLA)
  • Quantity: The number of shares in the portfolio
  • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
  • Close: The closing price of the stock
  • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

2. Portfolio Prices

This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol
  • Open: The opening price of the stock on that day
  • High: The highest price reached on that day
  • Low: The lowest price reached on that day
  • Close: The closing price of the stock
  • Adjusted: The adjusted closing price after stock splits and dividends
  • Returns: Daily percentage return based on close prices
  • Volume: The volume of shares traded that day

3. NASDAQ

This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

4. S&P 500

This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

5. Dow Jones

This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

Personal Portfolio Data

This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

About this dataset

Context

The dataset tabulates the data for the Dow City, IA population pyramid, which represents the Dow City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Dow City, IA, is 24.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Dow City, IA, is 21.7.
• Total dependency ratio for Dow City, IA is 46.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Dow City, IA is 4.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 for the Dow City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Dow City for the selected age group is shown in the following column.
• Population (Female): The female population in the Dow City for the selected age group is shown in the following column.
• Total Population: The total population of the Dow City for the selected age group is shown in the following column.

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 Dow City Population by Age. You can refer the same here

Context

Stock time series are a favourite among data scientists because they are easily understood and widely available - in this extensive data set you will find long-time time-series with open/close/high/min/adjusted features, as well as data regarding stock splits, trading volume and dividends.

Content

This data set includes Dow Jones member stock prices (status 01.0.1.2021) with all their historic stock performances from 01.01.2020 to 31.12.2020.

• 30 Dow Jones stocks
• 21 years of data (depending on company age)
• 1 entry per day
• 150503 data points

Please also check the corresponding Jupyter Notebook to get some basic ideas how to use this data set: https://www.kaggle.com/deeplytics/dow-jones-historic-stock-data-2000-2020

Stock Names

In the data set, all companies use their stock ticker names. If you are unfamiliar with them, please check this overview: https://www.cnbc.com/dow-30/

Inspiration

Today's free APIs and coding libraries make it relatively easy for the average user to get an understanding of stock price movements. More advanced users may even be able to find patterns, that can be incorporated into investment decisions.

Acknowledgements

Photo by Dmitry Demidko on Unsplash: https://unsplash.com/photos/eBWzFKahEaU?utm_source=unsplash&utm_medium=referral&utm_content=creditShareLink

Current 30 components of the Dow Jones Industrial Average, and their Open, High, Low, Close, Adjusted Close, Volume data since 1980, for stocks that were listed then. Updates daily using the Yahoo Finance API.

Description:

Stock data: Dow Jones Industrial Average (DJIA) is used to "prove the concept". (Range: 2008-08-08 to 2016-07-01)

1. Stock Headlines.csv: I provide this combined dataset with 27 columns. The first column is "Date", the second is "Label", and the following ones are news headlines ranging from "Top1" to "Top25".

2. DJIA_table.csv: Downloaded 5 years data directly from Yahoo Finance: check out the web page for more info.

If you find this dataset useful, please consider upvoting ❤️

About this dataset

Context

The dataset tabulates the Dow City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Dow City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Dow City was 470, a 1.26% decrease year-by-year from 2021. Previously, in 2021, Dow City population was 476, a decline of 1.45% compared to a population of 483 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Dow City decreased by 38. In this period, the peak population was 510 in the year 2003. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Dow City is shown in this column.
• Year on Year Change: This column displays the change in Dow City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Dow City Population by Year. You can refer the same here