Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

The 2017 National Financial Well-Being in America Survey, conducted for the CFPB Offices of Financial Education and Financial Protection for Older Americans, was an online survey conducted to measure the financial well-being of adults in the United States. These data were created as a foundation for internal and external research into financial well-being and are relevant to work being done by researchers in the Office of Research who have access to the (deidentified) data.

Kinyarwanda Automatic Speech Recognition Dataset

  Dataset Description

This dataset contains a sample from the 500 hours of Kinyarwanda speech data covering Health, Government, Finance, Education, and Agriculture domains, converted from the Kaggle Kinyarwanda ASR Track A competition.

  Dataset Details

Language: Kinyarwanda (rw) Task: Automatic Speech Recognition Size: ~500 hours of transcribed speech Domains: Health, Government, Financial Services, Education… See the full description on the dataset page: https://huggingface.co/datasets/badrex/kinyarwanda-speech-sample.

Kinyarwanda Automatic Speech Recognition Dataset

  Dataset Description

This dataset contains ~1000 hours of transcribed Kinyarwanda speech data covering Health, Government, Finance, Education, and Agriculture domains, converted from the Kaggle Kinyarwanda ASR Track B competition.

  Dataset Details

Language: Kinyarwanda (rw) Task: Automatic Speech Recognition Size: ~1000 hours of transcribed speech Domains: Health, Government, Financial Services, Education… See the full description on the dataset page: https://huggingface.co/datasets/badrex/kinyarwanda-speech-1000h.

Data explanation

High-quality financial data is expensive to acquire and is therefore rarely shared for free. The data set includes about 2750 US listed firm on NASDAQ and NYSE stock market. These all firms have December year end month. The firms names have been replaced with company number randomly. The data set can be analyzed from various perspectives. You will find out the performance of different industry in US in 2020, a pandemic situation. It is a very good and genuine dataset for people having Finance knowledge. It has taken from a financial database and amended for the Kaggle users.

Content

company_number: Just a random number primary_industry: secondary_industry: sub_secondary_industry
dividend_payer: companies that pay dividend has given dummy variable '1', non payer is '0'..
ebit_fy2019: Earnings before interest and tax for 2019
ebit_fy2020: Earnings before interest and tax for 2020
marketcap_decemb2019: Market capitalisation MTBV_Dec2019: Market to book value
totalreturn_percent_ytd_dec2020: stock returns for 2020 dps_fy2020: dividend paid per share in 2020 dps_fy2019: dividend paid per share in 2019 day_close_price_dollars_december: share closing price
change_in_earnings_by_marketcap total_equity_fy2019

Inspiration

Which industry has performed well and has the highest returns after COVID-19 which was still there in 2020 , either the companies in them are majority dividend payer or non payers. Industry wise profitability performance? What is the impact of size of the firms on their earnings and stock returns? The above questions are just a sample of questions, you can analyze the data as you want. Good luck!

Context

Pennsylvania requires all school districts, career and technology centers, and charter schools to report an "Annual Financial Report". This dataset aims to consolidate and analyze this data.

Content

This data is all open and available on the PA Dept of Education website. I have preserved the original files in this dataset and work directly with those files.

Acknowledgements

Thank you, Commonwealth of PA, for making this information easy for a normal person to find and use.

Inspiration

I want people to be able to see how their own school district is doing in relation to other districts in the Commonwealth.

It's no secret that US university students often graduate with debt repayment obligations that far outstrip their employment and income prospects. While it's understood that students from elite colleges tend to earn more than graduates from less prestigious universities, the finer relationships between future income and university attendance are quite murky. In an effort to make educational investments less speculative, the US Department of Education has matched information from the student financial aid system with federal tax returns to create the College Scorecard dataset.

Kaggle is hosting the College Scorecard dataset in order to facilitate shared learning and collaboration. Insights from this dataset can help make the returns on higher education more transparent and, in turn, more fair.

Data Description

Here's a script showing an exploratory overview of some of the data.

college-scorecard-release-*.zip contains a compressed version of the same data available through Kaggle Scripts.

It consists of three components:

• All the raw data files released in version 1.40 of the college scorecard data
• Scorecard.csv, a single CSV file with all the years data combined. In it, we've converted categorical variables represented by integer keys in the original data to their labels and added a Year column
• database.sqlite, a SQLite database containing a single Scorecard table that contains the same information as Scorecard.csv

New to data exploration in R? Take the free, interactive DataCamp course, "Data Exploration With Kaggle Scripts," to learn the basics of visualizing data with ggplot. You'll also create your first Kaggle Scripts along the way.

Techsalerator's Corporate Actions Dataset in Austria offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 50 companies traded on the Vienna Stock Exchange* (XWBO).

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Top 5 used data fields in the Corporate Actions Dataset for Austria:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Austria:

Mergers and Acquisitions: Corporate actions related to mergers and acquisitions have been observed in Austria, involving both domestic and international companies. These actions could lead to changes in ownership structures, business operations, and market dynamics.

Investments in Technology and Innovation: Austria has a strong emphasis on technology and innovation. Corporate actions often involve investments in research and development, technology startups, and initiatives to foster innovation across various sectors.

Sustainable and Renewable Energy Initiatives: Corporate actions have been taken to promote sustainable energy solutions and reduce carbon emissions. This includes investments in renewable energy projects, energy-efficient technologies, and the development of clean energy infrastructure.

Financial Services Expansion: Companies in Austria's financial sector have been taking corporate actions to expand their services and reach both domestically and internationally. This includes new product offerings, partnerships, and digital transformation initiatives.

Real Estate Development: Corporate actions in the real estate sector involve property development, urban planning, and infrastructure projects. These actions contribute to the growth of Austria's real estate market and urban landscape.

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Top 5 financial instruments with corporate action Data in Austria

Vienna Stock Exchange (VSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Vienna Stock Exchange. This index would provide insights into the performance of the Austrian stock market.

Vienna Stock Exchange (VSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Vienna Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in Austria.

AustroGrocer: An Austria-based supermarket chain with operations in multiple regions. AustroGrocer focuses on providing quality products and enhancing the grocery shopping experience for consumers.

FinanceAustria: A financial services provider in Austria with a focus on offering inclusive financial solutions and promoting financial literacy among various segments of the population.

CropTech Austria: A company dedicated to advancing agricultural technology in Austria, focusing on sustainable farming practices, innovative crop management, and contributing to food security.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Austria, 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:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Austria?

The cost of the Corporate Actions 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 ...

CONTEXT

Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment,​ the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.

The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.

The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.

DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).

Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html

Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE

COLUMN NOTES

All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.

FEDERAL FINANCE DATA DEFINITIONS

t_fed_rev: Total federal revenue through the state to each school district.

C14- Federal revenue through the state- Title 1 (no child left behind act).

C25- Federal revenue through the state- Child Nutrition Act.

Title 1 is a program implemented in schools to help raise academic achievement ​for all students. The program is available to schools where at least 40% of the students come from low inccom​​e families.

Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income​ families.

MATH SCORES DATA DEFINITIONS

Note: Mathematics, Grade 8, 2017, All Students (Total)

average_scale_score - The state's average score for eighth graders taking the NAEP math exam.

Context

This dataset marks the beginning of my adventure in the world of data science. And for me, it represents the merging of two areas of knowledge: computational chemistry and data science.

Content

Time evolution of molecular systems by molecular dynamics technique. To get the simulation, the Nosé-Hoover or Berendsen thermostat-barostat was used for controlling the temperature and/or pressure. After the minimization of the energy, the time-scale was performed multiplying the number of steps by the timestep. The use must be attempt to the units of measurement, which are atmosphere, Kelvin, kcal/mol, angstrom, and picosecond. The canonical variables of each atom have been getting by HICOLM software.

This dataset is automatically linked with the GitHub repository of datasets.

Acknowledgements

I acknowledge the Pró Reitoria de Pesquisa, Extensão e Inovação (PROEPPI-IFPR) for the financial support.