In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

Dataset

This dataset was created by Arshan Khan

Contents

It contains the following files:

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

Household Saving Rate in the United States decreased to 4.50 percent in May from 4.90 percent in April of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is designed for analyzing various product categories within the Japanese market. It provides information about each product category's size, growth rate, market share, competitor market shares, average price, customer demographics, online presence, and market saturation. Here's a breakdown of each column:

Product Category: The type of products or services being analyzed within the Japanese market.

Total Market Size (in USD): The estimated total market size in terms of US dollars for each product category. This figure reflects the overall revenue potential for that category.

Market Growth Rate (%): The projected annual growth rate of each product category's market. This percentage indicates how much the market is expected to expand or contract over time.

Market Share (%): The percentage of the total market size that each product category holds. This reflects the relative importance of each category within the overall market.

Competitor 1 Market Share (%): The market share percentage of the first major competitor within each product category. This helps to understand the competitive landscape.

Competitor 2 Market Share (%): The market share percentage of the second major competitor within each product category. Similar to the previous column, this provides insight into the competitive environment.

Average Price (in USD): The average price of products or services within each product category. This information helps understand the pricing dynamics of the category.

Customer Demographics: The primary target audience or customer segments for each product category. Understanding the demographics helps in tailoring marketing efforts.

Online Presence (%): The percentage of businesses within each product category that have an online presence. This includes websites, social media, and other digital platforms.

Market Saturation (%): An estimate of how much of the potential market demand has already been captured by existing products or services within each category. A higher percentage indicates a more saturated market.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample excludes the Federal Dependencies because of remoteness and difficulty of access, as well as some additional areas because of security concerns.The excluded areas represent about 5% of the population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

• Name: Characterization of investments profiles on the energy transition for european citizens
• Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description.
• License: cc-BY-SA
• Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.
• Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.
• Collection Date: 22/07/2022
• Publication Date: 15/10/2023
• DOI: 10.5281/zenodo.4455198
• Other repositories:
• Author: University of Deusto
• Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.
• Description: The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset
  • Section 1 - Scenarios for energy transition.
    • ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible.
    • ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario
    • ID191, ID192.. Amount of money people would spend based on an assumed case.
    • ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.
    • [ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.
    • [ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.
    • [ID141, ID5, ID147]. Used for statements that best represent survey responder

      </li>
      <li><strong>Section 2 - Determinants (factors).</strong> Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible.
      <ul>
        <li><strong>ID100</strong> Risk profile</li>
        <li><strong>ID101</strong> Added value</li>
        <li><strong>ID102</strong> Self-Satisfaction</li>
        <li><strong>ID103</strong> Technical Fit</li>
        <li><strong>ID104</strong> Own competence</li>
        <li><strong>ID105</strong> Knowledge</li>
        <li><strong>ID106</strong> Cost-Efficiency</li>
        <li><strong>ID107</strong> Safety</li>
        <li><strong>ID108</strong> Trust</li>
        <li><strong>ID109</strong> Autarky</li>
        <li><strong>ID110</strong> Legal</li>
        <li><strong>ID111</strong> Climate Protection</li>
        <li><strong>ID112</strong> Wellbeing</li>
        <li><strong>ID113</strong> Coziness</li>
        <li><strong>ID114</strong> Rights and Duties</li>
        <li><strong>ID115</strong> Peer-Pressure</li>
        <li><strong>ID116</strong> Socialising</li>
        <li><strong>ID117</strong> Support</li>
        <li><strong>ID118</strong> Agreement</li>
        <li><strong>ID119</strong> Brag</li>
        <li><strong>ID120</strong> Fun</li>
        <li><strong>ID121</strong> Novelty</li>
        <li><strong>ID122</strong> Trends</li>
        <li><strong>ID123</strong> Authority</li>
        <li><strong>ID124</strong> Own Significance</li>
        <li><strong>ID125</strong> Poseur</li>
        <li><strong>ID2</strong> Frugality</li>
        <li><strong>ID3</strong> Environmental concerns</li>
        <li><strong>ID31</strong> Adherence</li>
        <li><strong>ID52</strong> Commitment</li>
        <li><strong>ID97</strong> Profits</li>
        <li><strong>ID99</strong> Credit Score</li>
      </ul>
      </li>
      <li><strong>Section 3 - “Socio-economic” description. </strong>Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked
      <ul>
        <li><strong>ID164</strong> Understanding of questions</li>
        <li><strong>ID300</strong> Country of residence</li>
        <li><strong>ID137</strong> Age</li>
        <li><strong>ID178</strong> Highest level of education</li>
        <li><strong>ID136</strong> Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li>
      </ul>
      </li>
      <li><strong>Section 4 - Investment decision</strong>. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle)
      <ul>
        <li>Appliances
        <ul>
          <li><strong>ID42</strong> Affordable cost of a Regular refrigerator</li>
          <li><strong>ID45</strong> Energy efficient refrigerator costs</li>
          <li><strong>ID50</strong> Willingness to purchase an energy efficient refrigerator
          <ul>
            <li><strong>ID65</strong> Why no</li>
            <li><strong>ID66</strong> affordable cost of an energy efficient option</li>
            <li><strong>ID67</strong> Years to amortize an efficient option</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Insulation
        <ul>
          <li><strong>ID47</strong> Affordable cost of updating to a state of the art insulation on the facade</li>
          <li><strong>ID56</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID74</strong> Why no?</li>
            <li><strong>ID20</strong> affordable cost of an energy efficient option</li>
            <li><strong>ID34</strong> Years to amortize an energy efficient option</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Energy Generation
        <ul>
          <li><strong>ID68</strong> Affordable cost of a solar photovoltaic system</li>
          <li><strong>ID76</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID84</strong> Why no?</li>
            <li><strong>ID132</strong> Affordable cost of a photovoltaic system</li>
            <li><strong>ID138</strong> Years that amortize a photovoltaic system</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Energy Storage
        <ul>
          <li><strong>ID142</strong> Affordable cost of an energy storage system</li>
          <li><strong>ID146</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID181</strong> Why no? </li>
            <li><strong>ID182</strong> Affordable cost of an energy storage system </li>
            <li><strong>ID183</strong> Years that amortize an energy storage systems</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Heating
        <ul>
          <li><strong>ID140</strong> Affordable cost of a gas boiler</li>
          <li><strong>ID209</strong> Affordable cost of an energy efficient heating system</li>
          <li><strong>ID217</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID238</strong> Why no?</li>
            <li><strong>ID239</strong> Affordable cost of a energy efficient option</li>
            <li><strong>ID241</strong> Years that amortize a heat pumps</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Mobility
        <ul>
          <li><strong>ID41</strong> Average kilometers traveled a typical day</li>
          <li><strong>ID51</strong> Usual travel option</li>
          <li><strong>ID264</strong> Affordable cost of a diesel or gasoline mid-range brand new car</li>
          <li><strong>ID265</strong> Affordable cost of a mid-range brand new electric car</li>
          <li><strong>ID281</strong> Willingness to buy an electric car
          <ul>
            <li><strong>ID289</strong> Why no?</li>
            <li><strong>ID290</strong> Affordable price of an electric car</li>
            <li><strong>ID291</strong> Years that amortize an electric car</li>
          </ul>
          </li>
        </ul>
        </li>
      </ul>
      </li>
      <li><strong>Section 5 - Household characterization</strong>
      <ul>
        <li><strong>ID127</strong> Selecting an asked value</li>
        <li><strong>ID189</strong> Type of living area</li>
        <li><strong>ID202</strong> Gender
    

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.

Census tracts designated as Opportunity Zones. Qualified census tracts are those that have a poverty rate of at least 20 percent of a median income that does not exceed 80 percent of the area median income. The opportunity fund model encourages investors to pool their resources in opportunity zones, increasing the scale of investments going to underserved areas. The program provides a federal tax incentive for investors to re-invest unrealized capital gains into opportunity zones through opportunity funds. Under the terms of the program, the governor of each state must submit a plan to the federal government designating up to 25 percent of the qualified census tracts in their state as opportunity zones, which is then subject to approval of the Secretary of the Treasury.

This dataset provides insights into the social media reaction following a significant hack of a popular cryptocurrency exchange platform in September 2020. It allows for the analysis of how major events in the crypto world propagate through social media, offering a valuable resource for understanding investor behaviour and the broader market's response to critical situations. The dataset can be used to identify key developments within the crypto landscape by analysing public discourse.

Columns

• username: This column contains the username of the individual who authored the tweet.
• tweet_content: This column holds the actual content or body of the tweet.
• time: This column records the timestamp, formatted according to ISO standards, indicating when the tweet was published.

Distribution

The dataset is typically provided as a data file, commonly in CSV format. It comprises 3553 total records, with each record detailing the username, tweet content, and publishing timestamp. While the exact file size is not specified, the number of individual tweets is available.

Usage

This dataset is particularly well-suited for several applications: * Text classification: Organising and categorising tweets based on their content. * Analysis of investor's behaviour: Studying how investors react and communicate during high-stakes situations. * Predicting exchange rates: When combined with cryptocurrency rates, the dataset can help forecast future exchange rates by observing the collective reaction of investors.

Coverage

The data covers tweets specifically related to the crypto hack that occurred in September 2020. Its regional scope is global. Specific details regarding demographic coverage or data availability for particular groups or years beyond the specified time frame are not provided.

License

CCO

Who Can Use It

This dataset is intended for a range of users, including those in data science and analytics. Ideal users and their potential applications include: * Data scientists: For developing and refining text classification models and algorithms. * Financial analysts: To gain an understanding of investor sentiment and its impact on the cryptocurrency market. * Researchers: For studying social media propagation of news, crisis communication, and market psychology. * Developers: For building applications that leverage Natural Language Processing (NLP) and text mining techniques related to financial topics.

Dataset Name Suggestions

• Tweets About Crypto Hack 2020
• Social Media Crypto Event Data
• Cryptocurrency Breach Tweets
• Investor Reaction Crypto Dataset

Attributes

Original Data Source: Tweets About Big Crypto Hack

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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For more datasets, click here.

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to May 2025 about savings, personal, rate, and USA.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children, we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of

Operational Database Management Market Outlook

The global operational database management market size was valued at approximately USD 39.1 billion in 2023 and is projected to reach around USD 82.6 billion by 2032, growing at a CAGR of 8.7% during the forecast period. This market is driven by the increasing need for real-time data analytics, enhanced data security, and the rising adoption of cloud-based solutions. As businesses continue to digitize their operations, the demand for robust database management systems that can handle large volumes of data in real time has surged, positioning this market for significant growth.

One of the primary growth factors for this market is the proliferation of data across various industries. With the advent of IoT, social media, and other digital platforms, organizations are generating an unprecedented amount of data that needs to be managed efficiently. This has led to the adoption of advanced database management systems that can handle diverse data types and provide real-time insights. Additionally, advancements in AI and machine learning have further fueled the demand for operational databases that can support predictive analytics and automated decision-making processes.

Another major driver is the increasing necessity for enhanced data security and compliance. As data breaches and cyber threats become more sophisticated, organizations are under immense pressure to ensure the security and integrity of their data. Modern operational database management systems offer advanced security features such as encryption, access controls, and regular audits, which help organizations comply with stringent regulatory requirements and protect their sensitive information from unauthorized access and attacks.

The growing adoption of cloud-based solutions is also a significant contributor to market growth. Cloud-based operational databases offer numerous advantages, including reduced infrastructure costs, scalability, and accessibility from anywhere with an internet connection. This has made them particularly appealing to small and medium enterprises (SMEs) that may lack the resources to invest in on-premises solutions. Moreover, the integration of cloud services with AI and machine learning capabilities allows organizations to leverage their data for more strategic decision-making, further driving the demand for cloud-based database management systems.

The rise of Open Source Database solutions has been a game-changer in the operational database management market. These databases offer a cost-effective alternative to traditional proprietary systems, making them particularly attractive to small and medium enterprises (SMEs) and startups. Open source databases are not only budget-friendly but also provide the flexibility to customize and adapt the software to meet specific business needs. The robust community support and continuous innovation associated with open-source projects ensure that these databases remain at the forefront of technological advancements. As a result, many organizations are increasingly adopting open-source databases to leverage their scalability, reliability, and comprehensive feature sets, which are comparable to those of their proprietary counterparts.

From a regional perspective, North America remains a dominant player in the operational database management market, thanks to its advanced IT infrastructure and the presence of major technology companies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation, increasing investments in IT infrastructure, and the rising adoption of cloud services in countries like China and India. Europe and Latin America are also anticipated to experience steady growth due to the increasing focus on data security and compliance with regulations such as GDPR.

Component Analysis

The operational database management market can be segmented into software and services. The software segment is anticipated to hold the larger market share during the forecast period. This is primarily due to the continuous advancements in database technologies that offer enhanced performance, scalability, and security. Companies are increasingly investing in sophisticated database management software that can support their growing data requirements and provide real-time analytics. Moreover, the integration of AI and machine learning capabilities into database software is enabling predictive analytic

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 66 companies listed on the Nairobi Securities Exchange (XNAI) in Kenya. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Kenya:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Kenya:

Nairobi Securities Exchange All Share Index (NASI): The main index that tracks the performance of all companies listed on the Nairobi Securities Exchange (NSE). NASI provides insights into the overall market performance in Kenya.

Nairobi Securities Exchange 20 Share Index (NSE 20): An index that tracks the performance of the top 20 companies by market capitalization listed on the NSE. NSE 20 is an important benchmark for the Kenyan stock market.

Safaricom PLC: A leading telecommunications company in Kenya, offering mobile and internet services. Safaricom is one of the largest and most actively traded companies on the NSE.

Equity Group Holdings PLC: A prominent financial institution in Kenya, providing banking and financial services. Equity Group is a significant player in the Kenyan financial sector and is listed on the NSE.

KCB Group PLC: Another major financial institution in Kenya, offering banking and financial services. KCB Group is also listed on the NSE and is among the key players in the country's banking industry.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Kenya, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Kenya ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Kenya?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Kenya exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and se...

The Gross Domestic Product (GDP) in the United States contracted 0.50 percent in the first quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Unemployment Rate in India decreased to 7.90 percent in February from 8.20 percent in January of 2025. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

This data was extracted from the open database of quotations of currencies and precious metals located on the site of the Bank of Russia. The link https://www.cbr.ru/Eng/statistics/?PrtId=finr is available for all internet users, the website is in Russian and in English.

Content

It consists of 1128 observations of 23 variables. Variables that indicating exchange rates are measured in rubles, the prices of precious metals are denoted in rubles per gram, foreign exchange.

The special variable dual currency basket is calculated according to the formula: 0.55 USD + 0.45 EUR.

The variables k_CNY, k_JPY are coefficients for the currencies values.

Foreign exchange reserves and monetary gold reserves consist of official data points for every month about the state reserves in Russia.

Acknowledgments

From publicly available data the files in 'xlsx' and 'csv' formats have been generated and downloaded. They are absolutely free for usage.

Usage

A set of financial indicators is suitable for training in the field of data visualization and learning simple regression algorithms.

The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains two datasets:

1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

   1. class = 1 if the price of the stock increases during 2019
   2. class = 0 if the price of the stock decreases during 2019

In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?