The total number of new computer science (CS) graduates in the United States of America and in Canada has consistently grown over the past decade, jumping from nearly ** thousand in 2010 to over ** thousand new graduates in 2022. Bachelor's graduates represent the vast majority of the total figure, where the most significant increase happened. CS graduates still majority males The share of male computer science new graduates is drastically higher than female and nonbinary graduates, both for bachelor's and master's degrees. The percentage of graduates identifying themselves as female was, nevertheless, higher for master's degree than bachelor's across all observed years. This suggests that females are more willing, proportionately, to stick with the education path and go into the academic side of CS. Europe follows a similar trend Females in Europe were, in general, a higher ratio of CS students compared to the United States and Canada. Estonia, Romania, Bulgaria, and Turkey were particularly strong in females taking part in CS education, accounting for over **% students.

The share of male computer science (CS) new graduates is drastically higher than female and nonbinary graduates, both for bachelor's and master's degrees. The percentage of graduates identifying themselves as female was, nevertheless, higher for master's degree than bachelor's across all observed years.

The majority of new informatics, computer science (CS), computer engineering (CE), and information technology (IT) in Europe is largely concentrated in the United Kingdom. Germany and Turkey follow, with roughly ** thousand and ** thousand new bachelor's graduates in 2022.

Women and Men in Spain: Rate of graduates in science, mathematics, computer science, engineering, industry and construction in the EU by period (1,000 persons aged 20 to 29). Annual. National.

This statistic displays the results of a survey across India about the number of students who graduated in computer science engineering stream in fiscal year 2016, based on gender. There were around **** thousand male students who graduated from computer science engineering discipline, while there were around **** thousand female computer science engineering students who graduated during the measured time period.

The number of postsecondary graduates, by Classification of Instructional Programs, Primary groupings (CIP_PG), International Standard Classification of Education (ISCED), age group and gender.

Context

Pakistan has a large number of public and private universities offering degrees in multiple disciplines. There are 162 universities out of which 64 are in private sector and 98 are public sector/government universities recognized by the Higher Education Commission of Pakistan (HEC).

According to HEC, Pakistani universities are producing over half a million graduates per year, which include over more than 10,000 Computer Science/IT graduates.

From year 2001 to 2015 there is a mass increase in number of enrollment in universities. The recent statistics shows that in 2015, 1,298,600 students enrolled in different levels of degree, 869,378 in Bachelors (16 years), 63,412 in Bachelors (17 years), 219,280 in Masters (16 years), 124,107 in M.Phil/MS, 14,373 in Ph.D, and 8,319 in P.G.D. However, in 2014 the number of doctoral degree awarded were 1,351 only.

Moreover, according to HEC report, in 2014-2015 there are over 10,125 fulltime Ph.D. faculty teaching in Pakistan in all disciplines. Computer Science and related disciplines are widely taught in Pakistan with over 90 universities offering this discipline with qualified faculty. According to our dataset, there are 504 PhD faculty members in Computer Science in Pakistan for 10,000 students. So we have a PhD faculty member for every 20 students on average in computer science program.

Current Student to PhD Professor Ratio in Pakistan is 130:1 (while India is going towards 10:1 in Post-Graduate and 25:1 in Undergrad education).

Here is world's Top 100 universities with Student to Staff Ratio.

Content

Dataset: The dataset contains list of computer science/IT professors from 89 different universities of Pakistan.

Variables: The dataset contains Serial No, Teacher’s Name, University Currently Teaching, Department, Province University Located, Designation, Terminal Degree, Graduated from (university for professor), Country of graduation, Year, Area of Specialization/Research Interests, and some Other Information

Acknowledgements

Data has been collected from respective university websites. Some of the universities did not mention about their faculty profiles or were unavailable (hence the limitation of this dataset). The statistics mentioned above are gathered by Higher Education Commission of Pakistan (HEC) website and other web resources.

Inspiration

Here is what I like you to do:

1. Which area of interest/expertise is in abundance in Pakistan and where we need more people?
2. How many professors we have in Data Sciences, Artificial Intelligence, or Machine Learning?
3. Which country and university hosted majority of our teachers?
4. Which research areas were most common in Pakistan?
5. How does Pakistan Student to PhD Professor Ratio compare against rest of the world, especially with USA, India and China?
6. Any visualization and patterns you can generate from this data

Let me know how I can improve this dataset and best of luck with your work

In 2023, there were nearly ** university graduates per 10,000 inhabitants in Brazil who completed undergraduate studies in business, administration and Law. In that year, *** out of 10,000 had concluded a degree in computer science. Brazil's population amounted to more than *** million people in 2023.

Local Law 177 enacted in 2016 requires the Department of Education of the New York City School District to submit to the Council an annual report concerning computer science education for the prior school year. This report provides information about the number of computer science programs offered and the number of students enrolled in those programs as defined in Local Law 177 as reported through the 2017-2018 STARS database. It is important to note that schools self-report their computer science course information in STARS. This report also includes information regarding the number and ratio of certified STEM instructors, the department's STEM Institute, the nature of the district's computer science initiatives and the total available bandwidth in each school.

Historical Dataset of Academy Of Computer Science And Engineering is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2009-2023),Total Classroom Teachers Trends Over Years (2009-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2009-2023),Asian Student Percentage Comparison Over Years (2012-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (2009-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2009-2023),Free Lunch Eligibility Comparison Over Years (2009-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2009-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2011-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2023),Graduation Rate Comparison Over Years (2014-2023)

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This dataset examined integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom (i.e., primary discipline, grade level, programming paradigm, programming language, minimum amount of time the lesson takes, source of the lesson plan), the teacher (i.e., years teaching, current role (classroom teacher, tech specialist, STEM specialist, etc.), grade levels taught, disciplines taught, degrees and certifications, institutional support received for integrated computing, gender, race, self-efficacy), and the school (e.g., socioeconomic status of students, racial composition, number of CS courses offered, number of CS teachers, years CS courses have been taught, number of students, school location (urban, suburban, rural)). Methods Procedure Data about integrated computing lessons in non-CS classrooms were collected from in-service K-12 teachers in the United States via an online survey, and 262 surveys were completed. Participants were recruited first through teacher networks and districts to include diverse populations and then through LinkedIn. Teachers received a $100 gift card upon completion of the survey, which took approximately 30 minutes. Due to the incentive, submissions were screened during data collection to ensure eligibility (i.e., having a valid school district email) and quality (described below).

Instrument The survey asked about the programming and CT concepts taught in the activities and 20 factors related to classroom, teacher, and school context. The programming concepts included were based on a framework developed by Margulieux et al., 2023. A full list of concepts and contextual factors can be found below. Due to the large sample size, the survey was designed to be primarily quantitative but included a few qualitative questions (e.g., "Please describe in 1-2 sentences the computing learning objective of this activity") and requested teachers to submit their lesson plans. The research team used these qualitative elements to verify data quality, such as by ensuring the lesson included computing and comparing elements of the lesson plans to the quantitative data provided by the teachers. Overall, we found, and excluded, very few instances of low-quality data.

Survey Questions and Descriptive Statistics Qualitative Questions: Title of lesson plan One sentence describing the activity topic (e.g., In this activity, students apply their computational thinking skills to explore the life cycle of a butterfly.) One sentence describing the disciplinary learning objective (e.g., The primary learning goal is to model the life cycle of a butterfly.) One sentence describing the computing learning objective (e.g., Students will conditionals to match body features to life stages.) 1-3 sentences describing the instructional paradigm (e.g., Students will discuss butterflies and life cycles with their partners. Then they will modify the program and use conditionals to create the model.)

Quantitative Question Topic: Response Options (descriptive statistics in parentheses)

Programming and CT Concepts Programming paradigm: Select one: No Programming (80), Unplugged (87), Block-based (69), Text-based (26) Programming language: Open-ended Programming concepts: Select all that apply: Operator-arithmetic, Operator-Boolean, Operator-relational, Conditional-if-else, Conditional-if-then, Loop-for loop, Loop-while loop, Loop-loop index variable, Function-define/call, Function-parameter, Variable, Data types (string, integer, etc.), List, Multimedia component (sprite, sound, button, etc.), Multimedia properties (color, location, etc.), Multimedia movement (forward, back, turn), Output-string, Output-variable, User input, Event (M = 3.2, SD = 2.7) CT concepts: Select all that apply: Algorithms–sequences (158), Algorithms–parallelism (10), Pattern recognition (142), Abstraction (84), Decomposition (89), Debugging (40), Automation (40) (M = 2.1, SD = 1.1)

Classroom Context Integrated discipline: Select one: Art (5), Language arts (37), Foreign language (2), Math (67), Music (3), Science (61), Social Studies (13) Grades taught in lesson: Select all that apply: Kindergarten through 12th grade (activities that spanned K-5 = 107, 6-8 = 53, 9-12 = 93, K-12 = 9) Minimum amount of time the lesson takes: Select one: < 1 hour (90), 1-3 hours (126), 3-8 hours (32), 8+ hours (14) Source of the lesson plan: Select all that apply: Colleague (16), Online search (18), Professional development (20), Professional organization (23), Created based on an external source by myself or with colleagues (28), Modified from an external source (33), Created by myself or with colleagues (124)

Teacher Information Number of years teaching: Open-ended, M = 14.11, SD = 7.6 Current role: Select one: Teacher (220), STEM/Tech specialist (24), Librarian (9), Computer lab director (1), Other (8) Grade levels taught: Select all that apply: K-2, 3-5, 6-8, 9-10, 11-12 (grade levels that spanned K-5 = 79, 6-8 = 45, 9-12 = 93, K-12 = 45) Disciplines taught: Select all that apply: Art (13), Language arts (71), Foreign language (5), Math (134), Music (4), Science (100), Social Studies (54), Computer science (80), Technology (78), Other (8) Degrees, Certs, endorsements, etc. attained: Select all that apply: Teaching certificate in primary discipline(s) (164), Teaching certificate in CS (17), Bachelor’s degree in primary discipline education (129), Bachelor’s degree in CS or CS education (4), Master’s degree in primary discipline education (163), Master’s degree in CS or CS education (0), Endorsement in computer science education (47), EdD or PhD in education (17), Other (86) Support for integrated CS/CT development and implementation: Select all that apply: Professional development through my school/district/LEA/RESA (157), Professional development through external organizations (117), Peer/colleague/department collaboration in my school/district/LEA/RESA (130), Peer/colleague collaboration in external organizations (73), Funding for software licensing, hardware, or curricula (69) Self-efficacy: Views of CT and self-efficacy scale from Yadav, Caeli, Ocak, and Macann, 2022 (M = 4.23 out of 5, SD = 0.60) Gender: Select one: Man (60), Woman (198), Non-binary/third gender (2), Prefer not to say (2) Race: Select one: African American or Black (31), American Indian or Indigenous (1), Asian (13), Caucasian or White (193), Latino/a/x or Hispanic (10), Middle Eastern (0), Pacific Islander (0), Other (14)

School Context Number of students: Open-ended (M = 1179, SD = 741) Number of CS teachers: Open-ended (M = 1.6, SD = 1.4) Number of CS courses: Open-ended (M = 2.1, SD = 2.0) Number of years CS courses taught: Open-ended (M = 3.0, SD = 2.1) Racial composition: Give % of each race: American Indian or Native American (M = 1.8%), Asian (M = 4.5%), Black or African American (M = 23.3%), Hispanic or Latino (M = 17.2%), White or Caucasian (M = 47.5%), Other (M = 2.4%) % of students eligible for free or reduced lunch: Open-ended (M = 56%, SD = 34%) Type of area: Select one: Rural (90), Suburban (122), Urban (50)

This statistic shows the activity of first-degree, full-time computer science graduates who graduated at the end of academic year ******* in the United Kingdom (UK). ** percent of these graduates were in full time employment in the UK, compared with ** percent who were unemployed.

This dataset tracks annual total students amount from 2009 to 2023 for Academy Of Computer Science And Engineering

This dataset tracks annual total students amount from 2016 to 2020 for Nashville Academy Of Computer Science

In 2021, there were a total of 391,375 Bachelor's degrees earned in business studies across the United States, making it the most common field of study. In that same year, a further 126,944 Bachelor's degrees were earned in the field of psychology.

Explore the progression of average salaries for graduates in Computer Science And Statistics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Computer Science And Statistics relative to other fields. This data is essential for students assessing the return on investment of their education in Computer Science And Statistics, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Computer Science Statistics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Computer Science Statistics relative to other fields. This data is essential for students assessing the return on investment of their education in Computer Science Statistics, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Statistics Computer Science from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Statistics Computer Science relative to other fields. This data is essential for students assessing the return on investment of their education in Statistics Computer Science, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Statistics And Computer Science from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Statistics And Computer Science relative to other fields. This data is essential for students assessing the return on investment of their education in Statistics And Computer Science, providing a clear picture of financial prospects post-graduation.

Brief Description of Dataset

The dataset contains information about students in a 5-year Bachelor of Technology Degree in Computer Science from a North Eastern Nigerian University of Technology. The year of enrolment of the students ranges from 2005 to 2015. In the dataset, “NA” means that the student did not attempt the course.

Data Cleaning

First steps: the student marks that are less than 40 are excluded, as the course has to be retaken to be passed with a minimum of 50 marks. In addition, courses that are taken outside of graduation audit by students are eliminated.

There were 430 students screened for enrolment in the study with 95 being excluded because they did not take the course within the period of degree program for their early exemption. The exact ages of the participants are unknown other than all students enrolled were aged above 18 years of age.