Dataset

This dataset was created by Master Sniffer

Released under MIT

Contents

Question Paper Solutions of chapter Descriptive Statistics of Basic Data Science, 3rd Semester , Master of Computer Applications (2 Years)

This dataset was an inspiration to me to analytically find the best value Master's programs in data science given the statistics and rankings of each respective university. I acquired a majority of this data through Forbes. Though this data doesn't entirely go through every university from last year's ranking system, I went through each schools webpages through the top 250 universities to find the best value programs and if they offered a Data Science MS. I hope you use this data to make the best decision for yourself and make a respectable upgrade in your career as a Data Scientist.

NOTE: Some of the metrics are skewed for my usage i.e. I am a citizen in New York State and the cost of public universities in NY will be lesser than if you did not come from New York.

I also set a standard of 3.0 as a minimum GPA to be admitted to programs if a university did not provide a minimum GPA to be admitted.

Attribute Information:

1) School Name: Name of Given University 2) State: US State Abbreviation 3) City: US City University is located in 4) Ranking: 2021 Forbes ranking of University 5) Online: 0 -> in-person program, 1 -> online 6) Total_Tuition_Cost: Cost of Tuition in USD 7) Program_Years_Full_Time: Number of years to finish program 8) Min_Quant_GRE_Score: Quant GRE score needed to be accepted (blank if not found) 9) Min_Undergraduate_GPA: GPA needed to be accepted into program 10) Median_Salary_10yr: 10 year Median salary of former graduates (Not Exclusive to DS Majors) 11) Need_GRE: 0-> Do not need to take GRE, 1-> must take GRE 12) Institution Type: Either 'Private' or 'Public'

Main Source: (2022 Version)

https://www.forbes.com/top-colleges/

Question Paper Solutions of chapter Inferential Statistics of Basic Data Science, 3rd Semester , Master of Computer Applications (2 Years)

This dataset was generated as part of Practical Exercise 1 of the Data Typology and Lifecycle course, within the UOC's Master's in Data Science.

The objective of the project is to demonstrate the operation of an automated scraper developed with Python and Selenium to extract historical statistics of Premier League players from the 2007/08 season to 2023/24.

This file contains simulated data.
To avoid potential conflicts with intellectual property or privacy rights, the original personal and sports data has been replaced with automatically generated fictitious values. Although masked, private use is preferred. The structure, format, and statistical consistency have been maintained for educational and demonstration purposes.

The original scraper dynamically accessed the official Premier League website (https://www.premierleague.com/stats) to extract information such as:

• Player name
• Position
• Nationality
• Date of birth
• Height
• Season
• Club

Seasonal statistics:

• Goals
• Goal assist
• Clean sheet
• Appearances
• Mins played
• Yellow cards
• Red cards
• Total pass

This simulated dataset retains that structure but does not contain any real data.
It can be used as a basis for testing, data analysis training, or documentation of the scraping process.

This information was complied from the Australian Bureau of Statistics in Partial fullfilment of Coursework for the Master of Data Science taught at UNSW

Household income and wealth Australia, Building Activity Australia, Affordable Housing Database, National and Regional House Price Indices, Population Projections, Lending Indicators

Household income and wealth Australia ->https://www.abs.gov.au/statistics/economy/finance/household-income-and-wealth-australia/latest-release, Affordable Housing Database ->http://www.oecd.org/social/affordable-housing-database.htm, National and Regional House Price Indices ->https://stats.oecd.org/Index.aspx?DataSetCode=RHPI_TARGET, Population Projections ->https://stats.oecd.org/Index.aspx?DataSetCode=POPPROJ, Lending Indicators ->https://www.abs.gov.au/statistics/economy/finance/lending-indicators/apr-2021

This dataset contains the results of the experiments that I ran for my master thesis. The full code (and more) can be found at https://github.com/dimitris93/msc-thesis

Ahoy, data enthusiasts! Join us for a hands-on workshop where you will hoist your sails and navigate through the Statistics Canada website, uncovering hidden treasures in the form of data tables. With the wind at your back, you’ll master the art of downloading these invaluable Stats Can datasets while braving the occasional squall of data cleaning challenges using Excel with your trusty captains Vivek and Lucia at the helm.

Informatica Inc. develops an artificial intelligence-powered platform that connects, manages, and unifies data across multi-cloud, hybrid systems at enterprise scale in the United States. The company's platform includes a suite of interoperable data management products, including data integration products to ingest, transform, and integrate data; API and application integration products that enable users to create and manage APIs and integration processes for app-to-app synchronization, business process orchestration, B2B partner management, application development, and API management; data quality products to profile, cleanse, standardize, and enrich data to deliver accurate, complete, and consistent data sets for analytics, data science, governance, and other initiatives; and master data management products to create an authoritative single source of truth of business-critical data to reduce data related errors and remove redundancies. Its platform also includes customer and business 360 products to create, visualize, and browse comprehensive 360-degree views of business-critical data; data catalog products that enables customers to quickly find, access, and understand enterprise data using a simple Google-like search experience; and governance and privacy products that help users govern data, enable compliance with regulatory and corporate policies, and drive broader data consumption. The company also offers maintenance and professional services. Informatica Inc. was founded in 1993 and is headquartered in Redwood City, California.

In 2024, it was projected that people in the United States with a Master’s degree in Computer Science would have the highest average starting salary, at 85,403 U.S. dollars. People who held a Master’s degree in Engineering were projected to have the second-highest starting salary, at 83,628 U.S. dollars. An abundance of Masters As higher education in the United States has become more common, and even expected, the number of Master’s degrees awarded has increased. During the 1949-50 academic year, about 58,180 Master’s degrees were awarded to students, with the vast majority being earned by male students. In the 2018-19 academic year, this figure increased to about 833,710 Master’s degrees awarded, with the majority being earned by female students. The right career While Engineering might have the highest starting pay for Master’s degree holders, those with a Master’s degree as a Physician Assistant had the highest mid-career median pay in 2021. Engineering continues to be one of the most popular fields for those seeking their Master’s degree, and STEM fields continue to dominate the field in number of Master’s degrees awarded.

The Pre-1990 HMDA Aggregation Data were prepared annually during this period by the FFIEC on behalf of institutions reporting HMDA data. The Aggregation Data consists of home purchase and home improvement loans that a depository institution originated or purchased during each calendar year. The collected HMDA data were individually aggregated up to the tract level by the reporting depository institution and submitted accordingly to the FFIEC. Individual records are the summary of loan activity for the specified respondent for the indicated census tract except when the census tract numbers were either 888888 or 999999. The 888888 tract records are the sum of all loan activity by the reporter outside of the MSA being reported, but not appearing in any other MSA report. The 999999 tract records are the consolidated county summary data for loans made in untracted counties or counties with 1980 total population less than 30,000. The 1988 and 1989 Aggregation Data files include aggregated data from nondepository institutions, specifically mortgage banking subsidiaries of bank holding companies.

This data collection contains energy commodity production statistics for approximately 200 United Nations reporting countries for the years 1970-1979. In this file, each record refers to an individual reporting country and the quantity of its various transactions (e.g., production, imports, exports, bunkers, additions to stocks, and capacity) for a given energy commodity in a given year. Only annual data are included. The 70 types of commodities reported include solid fuels (e.g., coal, peat, and charcoal), liquid fuels (e.g., crude petroleum, gasoline, and kerosene), gases, uranium, and both industrial and public types of geothermal, hydro, and nuclear generated electricity. Information is also included on the population (in thousands) of the reporting country, the quantity of the commodity per transaction, and the date of the transaction. Supplementary data not contained in this data collection are in the introduction and footnotes of the individual tables published in the YEARBOOK OF WORLD ENERGY STATISTICS, 1979.

The FFRDC Research and Development Survey is the primary source of information on R&D expenditures at federally funded research and development centers (FFRDCs) in the United States. Conducted annually for university-administered FFRDCs since FY 1953 and all FFRDCs since FY 2001, the survey collects information on R&D expenditures by source of funds and types of research and expenses. The survey is an annual census of the full population of eligible FFRDCs. See https://www.nsf.gov/statistics/ffrdclist/ for the Master Government List of FFRDCs maintained by the National Science Foundation.

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first cycle of the NPHS is both longitudinal and cross-sectional. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons will be interviewed every two years. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

Download reports and data on tuberculosis incidences in Massachusetts, including a 5-year summary and demographic breakouts.

Graph and download economic data for All Employees: Professional, Scientific, and Technical Services in Jackson, MS (MSA) (SMU28271406054000001A) from 2001 to 2024 about Jackson, science, MS, professional, services, employment, and USA.

Graph and download economic data for All Employees: Professional, Scientific, and Technical Services in Memphis, TN-MS-AR (MSA) (SMU47328206054000001A) from 1990 to 2024 about Memphis, science, MS, AR, professional, TN, services, employment, and USA.

Employment income (in 2019 and 2020) by detailed major field of study and highest certificate, diploma or degree, including work activity (full time full year, part time full year, or part year).

This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A25,3A26

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.

This is the GPM-like formatted TRMM Precipitation Radar (PR) monthly gridded data, first released with the "V8" TRMM reprocessing. The TRMM radar Level 3 grids are now consistent with the GPM Dual-frequency Precipitation Radar (DPR). The closest ancestor of this dataset was the monthly radar statistics 3A25.

This product consists of monthly statistics of the PR measurements at 0.25x0.25 degrees, and monthly histograms and statistics at 5x5 degrees, horizontal resolution.

The objective of the algorithm is to calculate various daily statistics from the level 2 PR output products. Four types of statistics are calculated: 1. Probabilities of occurrence (count values) 2. Means and standard deviations 3. Histograms 4. Correlation coefficients In all cases, the statistics are conditioned on the presence of rain or some other quantity such as the presence of stratiform rain or the presence of a bright-band. For example, to compute the unconditioned mean rain rate, the conditional mean must be multiplied by the probability of rain which, in turn is calculated from the ratio of rain counts to the total number of observations in the box of interest.

The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. The low resolution 5x5 deg grid covers 70S to 70N. Areas beyond the ±40 degrees of latitudes are padded with empty grid cells.