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/

Dataset

This dataset was created by Master Sniffer

Released under MIT

Contents

Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in scientific research. Ten years after the publication of "Computing in the Statistics Curriculum,'' the nature of statistics continues to change, and computing skills are more necessary than ever for modern scientific researchers. In this paper, we describe research on the design and implementation of a suite of data science workshops for environmental science graduate students, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data using reproducible tools. These workshops help to bridge the gap between the computing skills necessary for scientific research and the computing skills with which students leave their statistics service courses. Moreover, though targeted to environmental science graduate students, these workshops are open to the larger academic community. As such, they promote the continued learning of the computational tools necessary for working with data, and provide resources for incorporating data science into the classroom.

Methods Surveys from Carpentries style workshops the results of which are presented in the accompanying manuscript.

Pre- and post-workshop surveys for each workshop (Introduction to R, Intermediate R, Data Wrangling in R, Data Visualization in R) were collected via Google Form.

The surveys administered for the fall 2018, spring 2019 academic year are included as pre_workshop_survey and post_workshop_assessment PDF files. 
The raw versions of these data are included in the Excel files ending in survey_raw or assessment_raw.

  The data files whose name includes survey contain raw data from pre-workshop surveys and the data files whose name includes assessment contain raw data from the post-workshop assessment survey.


The annotated RMarkdown files used to clean the pre-workshop surveys and post-workshop assessments are included as workshop_survey_cleaning and workshop_assessment_cleaning, respectively. 
The cleaned pre- and post-workshop survey data are included in the Excel files ending in clean. 
The summaries and visualizations presented in the manuscript are included in the analysis annotated RMarkdown file.

The Graduate Students and Postdoctorates in Science and Engineering survey is an annual census of all U.S. academic institutions granting research-based master's degrees or doctorates in science, engineering, and selected health fields as of fall of the survey year. The survey, sponsored by the National Center for Science and Engineering Statistics within the National Science Foundation and by the National Institutes of Health, collects the total number of master's and doctoral students, postdoctoral appointees, and doctorate-level nonfaculty researchers by demographic and other characteristics such as source of financial support. Results are used to assess shifts in graduate enrollment and postdoc appointments and trends in financial support.

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

Context

I am studying for a master's degree in CS at Unversity of Illinois - Urbana Champaign and was curious what's the number of students enrolling and graduating the CS undergraduate and graduate school.

Fortunately, there is a page from UIUC that has the latest years of data for undergraduate and graduate students.

https://cs.illinois.edu/about-us/statistics

Content

The data includes how many students are enrolled in CS undergraduate and graduate school, how many of them are actually graduated, and what major that students took with CS, how many of them are Ph.D. awarded, etc.

Acknowledgements

Thank you UIUC for providing statistics on https://cs.illinois.edu/about-us/statistics. All the numbers and data are from the website as of 3/10/2020.

Inspiration

It would be fun to find out any trend in UIUC CS, e.g, what major is getting famous for years from students, if the number of PhD/M/Master degree enrollment is increasing or decresing, etc.

The Graduate Students and Postdoctorates in Science and Engineering survey is an annual census of all U.S. academic institutions granting research-based master's degrees or doctorates in science, engineering, and selected health fields as of fall of the survey year. The survey, sponsored by the National Center for Science and Engineering Statistics within the National Science Foundation and by the National Institutes of Health, collects the total number of master's and doctoral students, postdoctoral appointees, and doctorate-level nonfaculty researchers by demographic and other characteristics such as source of financial support. Results are used to assess shifts in graduate enrollment and postdoc appointments and trends in financial support. This dataset includes GSS assets for 2022.

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

Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this article, we present a one-week course module for students in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an R shiny app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with R code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.

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.

Weighted average tuition fees by field of study for full-time Canadian graduate students. Data are collected from all publicly funded Canadian degree-granting institutions.

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

Frequency of reported types of studies and use of descriptive and inferential statistics (n = 216).

The International STEM Graduate Student Survey assesses why international students are coming to the United States for their graduate studies, the challenges they have faced while studying in the US, their future career plans, and whether they wish to stay or leave the US upon graduation. According to the Survey of Earned Doctorates by the National Science Foundation and the National Center for Science and Engineering Statistics, international students accounted for over 40% of all US doctoral graduates in STEM in 2013. The factors that influence international students' decisions to study in the US and whether they will stay or leave are important to US economic competitiveness. We contacted graduate students (both domestic and international) in STEM disciplines from the top 10 universities ranked by the total number of enrolled international students. We estimate that we contacted approximately 15,990 students. Individuals were asked to taken an online survey regarding their background, reasons for studying in the US, and whether they plan to stay or leave the US upon graduation. We received a total of 2,322 completed surveys, giving us a response rate of 14.5%. 1,535 of the completed were from domestic students and 787 of which were from international students. Raw survey data are presented here.Survey participants were contacted via Qualtrics to participate in this survey. The Universe of this survey data set pertains to all graduate students (Master's and PhD) in STEM disciplines from the following universities: Columbia University, University of Illinois-Urbana Champaign, Michigan State University, Northeastern University, Purdue University, University of Southern California, Arizona State University, University of California at Los Angeles, New York University, University of Washington at Seattle. Data are broken into 2 subsets: one for international STEM graduate students and one for domestic STEM graduate students, please see respective files.

A little paragraph from one real dataset, with a few little changes to protect students' private information. Permissions are given.

Goals

You are going to help teachers with only the data: 1. Prediction: To tell what makes a brilliant student who can apply for a graduate school, whether abroad or not. 2. Application: To help those who fails to apply for a graduate school with advice in job searching.

Tips

1. Educational data may have subtle structures, hierarchies and heterogeneity are probably involved. Simple regressions can hardly make any difference. Also, you should keep an eye on the collinearity in some indicators collected by teachers who have already forgot statistics.
2. Not all students are free to choose to apply for a graduate school, but some were born with privileges.
3. Some of the students are trying (or planning to try) to apply for a graduate school for years, you should be responsible to give advice accurately under their circumstances

About the Data

Some of the original structure are deleted or censored. For those are left: Basic data like: - ID - class: categorical, initially students were divided into 2 classes, yet teachers suspect that of different classes students may performance significant differently. - gender - race: categorical and censored - GPA: real numbers, float

Some teachers assume that scores of math curriculums can represent one's likelihood perfectly: - Algebra: real numbers, Advanced Algebra - ......

Some assume that background of students can affect their choices and likelihood significantly, which are all censored as: - from1: students' home locations - from2: a probably bad indicator for preference on mathematics - from 3: how did students apply for this university (undergraduate) - from4: a probably bad indicator for family background. 0 with more wealth, 4 with more poverty

The final indicator y: - 0, one fails to apply for the graduate school, who may apply again or search jobs in the future - 1, success, inland - 2, success, abroad

The joint UNESCO-OECD-Eurostat (UOE) data collection on formal education systems provides annual data on student participation and completion of educational programmes as well as data on personnel, cost and type of resources devoted to education. The reference period for non-monetary education data is the school year and for monetary data it is the calendar year. The International Statistics of Education and Training Systems ÔÇô UNESCO-UIS/OECD/Eurostat (UOE) Questionnaire aims to provide the data required by international bodies, in addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of Education and Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different levels of education in order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. A selection of tables with data derived from this statistic is provided below, together with a presentation summary note:

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.

Context

This dataset is originally from Dhaka Stock Exchange Ltd. The objective of the dataset is to assign analytical report writing tasks to Summer 2020 students enrolled in ASDS18: Data Mining course in proceedings of the partial fulfillment of the requirements for the Professional Masters in Applied Statistics and Data Science (PMASDS) degree. This data set was collected using the Dhaka Stock Exchange API.

Content

The datasets consist of several stock company predictor (independent) variables and one target (dependent) variable, Outcome. Independent variables include the last price, net asset value (NAV) of the stock, Earnings Per Share (EPS), price-to-earnings (P/E) ratio of the stock, paid-up capital per share, and so on.

It contains information on 374 listed companies from Dhaka Stock Exchange - DSE, Bangladesh. The outcome tested was Category, 258 tested positive and 500 tested negative. Therefore, there is one target (dependent) variable and 8 attributes.

Acknowledgements

Dr. Md. Rezaul Karim, Associate Professor, Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh (2021) provided us with this dataset. Using the Dhaka Stock Exchange API this data set was collected to assign analytical report writing tasks to Summer 2020 students in proceedings of the partial fulfillment of the requirements for the Professional Masters in Applied Statistics and Data Science (PMASDS) degree.

DSE Listed Companies Database

Inspiration

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.