CS LinkedIn Data by School: Tech & Finance /u/Ok_Experience_5151 CSCQ protests reddit

CS LinkedIn Data: Tech & Finance

This is a second attempt at an exercise I did a couple years ago. I picked ~200 research universities, ~75 liberal arts colleges and a few notable regional and special-focus schools and checked the # of U.S. based CS alumni (on LinkedIn) at various A+ employers in both tech and finance, then divided by the total number of U.S. based CS alumni. These percentages largely correlate with selectivity. But not exactly!

Included are all schools ranked by US News for CS, all AAU members, all schools with D1 power football, the top N national universities, the top N LACs, the highest-ranked public school (or pair of schools) in each U.S. state, and a few others chosen on the basis of having high enrollment.

Shown are each school’s US News rank (within category), the year it joined the AAU if applicable, whether or not it competes in a D1 “power” conference in football, and its US News CS rank (if ranked). Also admit rate (from US News) and SAT splits (also from US News). For schools where US News did not have SAT splits I went to IPEDS (past years) and/or Google. Those are shaded in yellow.

The list of tech employers: Google, Microsoft, Amazon, Apple, Meta, Amazon Web Services, NVIDIA, Facebook, Netflix.

The list of finance employers: Two Sigma, Citadel, Jane Street, Optiver, Citadel Securities, IMC Trading, Jump Trading Group, Hudson River Trading, Virtu Financial, Millennium, Susquehanna International Group, The D.E. Shaw Group.

In the comments I list the top cohort of schools for tech and finance separately within various “bands” of selectivity by admit rate and median SAT. This is interesting since it highlights less selective schools that nevertheless have a disproportionately high % of grads at these employers. Note that schools are grouped by overall admit rate and median SAT not the figures for their CS programs specifically. This results in schools where the CS program is much more selective than the school as a whole (e.g. UIUC) looking better than they might otherwise. I also excluded some schools whose percentages were only high only by virtue of having very small denominator, as well as one school (Drexel) whose high finance % seems to be due to a special internship arrangement with SIG.

Some caveats about these percentages:

• They count both undergrad and graduate alumni. Consequently, the percentages for schools with many CS graduate students will be inflated, whereas those for schools with few (or no) graduate students will not.
• They are highly sensitive to location. Students tend to attend schools near to where they group up and often choose to live/work in the same area. This is especially true of public schools. You see the effects of this in the percentages for California schools and those located near Redmond (since I included Microsoft in the list of tech employers).
• The percentages correlate with selectivity. Stronger inputs lead to stronger outcomes. The fact that a given school’s percentages are higher does not necessarily imply that any given student will have better odds of landing a job at these employers if he or she attends that school. Imagine a college that admits students on the basis of height, athletic ability and basketball skill. That college will send a disproportionately high % of its alumni to the NBA. If you’re 5’0″ and not athletic, though, then attending that college will not meaningfully improve your odds of playing in the NBA.
• Certain LACs that only enroll women punch way about their weight class in terms of tech employment; I would guess tech employers heavily recruit these schools in an effort to increase the % of their technical staff who are women.

CS LinkedIn Data: Tech & Finance

This is a second attempt at an exercise I did a couple years ago. I picked ~200 research universities, ~75 liberal arts colleges and a few notable regional and special-focus schools and checked the # of U.S. based CS alumni (on LinkedIn) at various A+ employers in both tech and finance, then divided by the total number of U.S. based CS alumni. These percentages largely correlate with selectivity. But not exactly!

Included are all schools ranked by US News for CS, all AAU members, all schools with D1 power football, the top N national universities, the top N LACs, the highest-ranked public school (or pair of schools) in each U.S. state, and a few others chosen on the basis of having high enrollment.

Shown are each school’s US News rank (within category), the year it joined the AAU if applicable, whether or not it competes in a D1 “power” conference in football, and its US News CS rank (if ranked). Also admit rate (from US News) and SAT splits (also from US News). For schools where US News did not have SAT splits I went to IPEDS (past years) and/or Google. Those are shaded in yellow.

The list of tech employers: Google, Microsoft, Amazon, Apple, Meta, Amazon Web Services, NVIDIA, Facebook, Netflix.

The list of finance employers: Two Sigma, Citadel, Jane Street, Optiver, Citadel Securities, IMC Trading, Jump Trading Group, Hudson River Trading, Virtu Financial, Millennium, Susquehanna International Group, The D.E. Shaw Group.

In the comments I list the top cohort of schools for tech and finance separately within various “bands” of selectivity by admit rate and median SAT. This is interesting since it highlights less selective schools that nevertheless have a disproportionately high % of grads at these employers. Note that schools are grouped by overall admit rate and median SAT not the figures for their CS programs specifically. This results in schools where the CS program is much more selective than the school as a whole (e.g. UIUC) looking better than they might otherwise. I also excluded some schools whose percentages were only high only by virtue of having very small denominator, as well as one school (Drexel) whose high finance % seems to be due to a special internship arrangement with SIG.

Some caveats about these percentages:

• They count both undergrad and graduate alumni. Consequently, the percentages for schools with many CS graduate students will be inflated, whereas those for schools with few (or no) graduate students will not.
• They are highly sensitive to location. Students tend to attend schools near to where they group up and often choose to live/work in the same area. This is especially true of public schools. You see the effects of this in the percentages for California schools and those located near Redmond (since I included Microsoft in the list of tech employers).
• The percentages correlate with selectivity. Stronger inputs lead to stronger outcomes. The fact that a given school’s percentages are higher does not necessarily imply that any given student will have better odds of landing a job at these employers if he or she attends that school. Imagine a college that admits students on the basis of height, athletic ability and basketball skill. That college will send a disproportionately high % of its alumni to the NBA. If you’re 5’0″ and not athletic, though, then attending that college will not meaningfully improve your odds of playing in the NBA.
• Certain LACs that only enroll women punch way about their weight class in terms of tech employment; I would guess tech employers heavily recruit these schools in an effort to increase the % of their technical staff who are women.