generated from mwc/project_argument
This project focused on observing trends in data
on women in STEM, and the correlation between median income, major, and percentage of women in said major. The data itself was not surprising, but what was surprising was how helpful I found this analysis to be. I can easily see a number of potential uses for this in the classroom to collect information, and I will be exploring them at a later point in time.
This commit is contained in:
Justin Toombs
parent
7fee8584d3
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@ -0,0 +1,78 @@
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Rank,Major_code,Major,Major_category,Total,Men,Women,ShareWomen,Median
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1,2419,PETROLEUM ENGINEERING,Engineering,2339,2057,282,0.120564344,110000
|
||||
2,2416,MINING AND MINERAL ENGINEERING,Engineering,756,679,77,0.101851852,75000
|
||||
3,2415,METALLURGICAL ENGINEERING,Engineering,856,725,131,0.153037383,73000
|
||||
4,2417,NAVAL ARCHITECTURE AND MARINE ENGINEERING,Engineering,1258,1123,135,0.107313196,70000
|
||||
5,2418,NUCLEAR ENGINEERING,Engineering,2573,2200,373,0.144966965,65000
|
||||
6,2405,CHEMICAL ENGINEERING,Engineering,32260,21239,11021,0.341630502,65000
|
||||
7,5001,ASTRONOMY AND ASTROPHYSICS,Physical Sciences,1792,832,960,0.535714286,62000
|
||||
8,2414,MECHANICAL ENGINEERING,Engineering,91227,80320,10907,0.119558903,60000
|
||||
9,2401,AEROSPACE ENGINEERING,Engineering,15058,12953,2105,0.139792801,60000
|
||||
10,2408,ELECTRICAL ENGINEERING,Engineering,81527,65511,16016,0.196450256,60000
|
||||
11,2407,COMPUTER ENGINEERING,Engineering,41542,33258,8284,0.199412643,60000
|
||||
12,5008,MATERIALS SCIENCE,Engineering,4279,2949,1330,0.310820285,60000
|
||||
13,2404,BIOMEDICAL ENGINEERING,Engineering,14955,8407,6548,0.437846874,60000
|
||||
14,2409,ENGINEERING MECHANICS PHYSICS AND SCIENCE,Engineering,4321,3526,795,0.183985189,58000
|
||||
15,2402,BIOLOGICAL ENGINEERING,Engineering,8925,6062,2863,0.320784314,57100
|
||||
16,2412,INDUSTRIAL AND MANUFACTURING ENGINEERING,Engineering,18968,12453,6515,0.343473218,57000
|
||||
17,2400,GENERAL ENGINEERING,Engineering,61152,45683,15469,0.252959838,56000
|
||||
18,2403,ARCHITECTURAL ENGINEERING,Engineering,2825,1835,990,0.350442478,54000
|
||||
19,2102,COMPUTER SCIENCE,Computers & Mathematics,128319,99743,28576,0.222695002,53000
|
||||
20,2502,ELECTRICAL ENGINEERING TECHNOLOGY,Engineering,11565,8181,3384,0.292607004,52000
|
||||
21,2413,MATERIALS ENGINEERING AND MATERIALS SCIENCE,Engineering,2993,2020,973,0.325091881,52000
|
||||
22,2499,MISCELLANEOUS ENGINEERING,Engineering,9133,7398,1735,0.189970437,50000
|
||||
23,2406,CIVIL ENGINEERING,Engineering,53153,41081,12072,0.227117943,50000
|
||||
24,2500,ENGINEERING TECHNOLOGIES,Engineering,3600,2695,905,0.251388889,50000
|
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25,2411,GEOLOGICAL AND GEOPHYSICAL ENGINEERING,Engineering,720,488,232,0.322222222,50000
|
||||
26,2410,ENVIRONMENTAL ENGINEERING,Engineering,4047,2662,1385,0.342228811,50000
|
||||
27,6107,NURSING,Health,209394,21773,187621,0.896018988,48000
|
||||
28,2503,INDUSTRIAL PRODUCTION TECHNOLOGIES,Engineering,4631,3477,1154,0.24919024,46000
|
||||
29,5102,"NUCLEAR, INDUSTRIAL RADIOLOGY, AND BIOLOGICAL TECHNOLOGIES",Physical Sciences,2116,528,1588,0.75047259,46000
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||||
30,2105,INFORMATION SCIENCES,Computers & Mathematics,11913,9005,2908,0.244103081,45000
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||||
31,2100,COMPUTER AND INFORMATION SYSTEMS,Computers & Mathematics,36698,27392,9306,0.253583302,45000
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32,5007,PHYSICS,Physical Sciences,32142,23080,9062,0.281936407,45000
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33,3701,APPLIED MATHEMATICS,Computers & Mathematics,4939,2794,2145,0.434298441,45000
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34,3700,MATHEMATICS,Computers & Mathematics,72397,39956,32441,0.448098678,45000
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35,3702,STATISTICS AND DECISION SCIENCE,Computers & Mathematics,6251,2960,3291,0.526475764,45000
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36,3607,PHARMACOLOGY,Biology & Life Science,1762,515,1247,0.707718502,45000
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37,6105,MEDICAL TECHNOLOGIES TECHNICIANS,Health,15914,3916,11998,0.75392736,45000
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38,5006,OCEANOGRAPHY,Physical Sciences,2418,752,1666,0.688999173,44700
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39,2501,ENGINEERING AND INDUSTRIAL MANAGEMENT,Engineering,2906,2400,506,0.174122505,44000
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40,4005,MATHEMATICS AND COMPUTER SCIENCE,Computers & Mathematics,609,500,109,0.178981938,42000
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41,6104,MEDICAL ASSISTING SERVICES,Health,11123,803,10320,0.927807246,42000
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42,2101,COMPUTER PROGRAMMING AND DATA PROCESSING,Computers & Mathematics,4168,3046,1122,0.269193858,41300
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43,4006,COGNITIVE SCIENCE AND BIOPSYCHOLOGY,Biology & Life Science,3831,1667,2164,0.56486557,41000
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44,2504,MECHANICAL ENGINEERING RELATED TECHNOLOGIES,Engineering,4790,4419,371,0.077453027,40000
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45,2599,MISCELLANEOUS ENGINEERING TECHNOLOGIES,Engineering,8804,7043,1761,0.200022717,40000
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46,5000,PHYSICAL SCIENCES,Physical Sciences,1436,894,542,0.377437326,40000
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47,1401,ARCHITECTURE,Engineering,46420,25463,20957,0.451464886,40000
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48,3605,GENETICS,Biology & Life Science,3635,1761,1874,0.515543329,40000
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49,3603,MOLECULAR BIOLOGY,Biology & Life Science,18300,7426,10874,0.59420765,40000
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50,6108,PHARMACY PHARMACEUTICAL SCIENCES AND ADMINISTRATION,Health,23551,8697,14854,0.630716318,40000
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51,5003,CHEMISTRY,Physical Sciences,66530,32923,33607,0.505140538,39000
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52,3606,MICROBIOLOGY,Biology & Life Science,15232,6383,8849,0.580948004,38000
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53,2106,COMPUTER ADMINISTRATION MANAGEMENT AND SECURITY,Computers & Mathematics,8066,6607,1459,0.180882718,37500
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54,3601,BIOCHEMICAL SCIENCES,Biology & Life Science,39107,18951,20156,0.515406449,37400
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55,3602,BOTANY,Biology & Life Science,1329,626,703,0.52896915,37000
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56,2107,COMPUTER NETWORKING AND TELECOMMUNICATIONS,Computers & Mathematics,7613,5291,2322,0.305004597,36400
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57,5004,GEOLOGY AND EARTH SCIENCE,Physical Sciences,10972,5813,5159,0.470196865,36200
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58,5005,GEOSCIENCES,Physical Sciences,1978,809,1169,0.591001011,36000
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59,6199,MISCELLANEOUS HEALTH MEDICAL PROFESSIONS,Health,13386,1589,11797,0.881293889,36000
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60,1301,ENVIRONMENTAL SCIENCE,Biology & Life Science,25965,10787,15178,0.584556133,35600
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61,5002,ATMOSPHERIC SCIENCES AND METEOROLOGY,Physical Sciences,4043,2744,1299,0.321296067,35000
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62,2001,COMMUNICATION TECHNOLOGIES,Computers & Mathematics,18035,11431,6604,0.366176878,35000
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63,5098,MULTI-DISCIPLINARY OR GENERAL SCIENCE,Physical Sciences,62052,27015,35037,0.564639335,35000
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64,3608,PHYSIOLOGY,Biology & Life Science,22060,8422,13638,0.618223028,35000
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65,3611,NEUROSCIENCE,Biology & Life Science,13663,4944,8719,0.63814682,35000
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66,6103,HEALTH AND MEDICAL ADMINISTRATIVE SERVICES,Health,18109,4266,13843,0.764426528,35000
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67,4002,NUTRITION SCIENCES,Health,18909,2563,16346,0.864456079,35000
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68,6110,COMMUNITY AND PUBLIC HEALTH,Health,19735,4103,15632,0.792095262,34000
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69,3699,MISCELLANEOUS BIOLOGY,Biology & Life Science,10706,4747,5959,0.556603774,33500
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70,6106,HEALTH AND MEDICAL PREPARATORY PROGRAMS,Health,12740,5521,7219,0.566640502,33500
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71,3600,BIOLOGY,Biology & Life Science,280709,111762,168947,0.601858152,33400
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72,3604,ECOLOGY,Biology & Life Science,9154,3878,5276,0.576360061,33000
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73,6109,TREATMENT THERAPY PROFESSIONS,Health,48491,13487,35004,0.721865913,33000
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74,6100,GENERAL MEDICAL AND HEALTH SERVICES,Health,33599,7574,26025,0.774576624,32400
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75,6102,COMMUNICATION DISORDERS SCIENCES AND SERVICES,Health,38279,1225,37054,0.967998119,28000
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76,3609,ZOOLOGY,Biology & Life Science,8409,3050,5359,0.637293376,26000
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@ -12,6 +12,7 @@ files = [
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]
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[package.dependencies]
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exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
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idna = ">=2.8"
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sniffio = ">=1.1"
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@ -33,22 +34,23 @@ files = [
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[[package]]
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name = "argon2-cffi"
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version = "21.3.0"
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description = "The secure Argon2 password hashing algorithm."
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version = "23.1.0"
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description = "Argon2 for Python"
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optional = false
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python-versions = ">=3.6"
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python-versions = ">=3.7"
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files = [
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{file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"},
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{file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"},
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{file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
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{file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
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]
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[package.dependencies]
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argon2-cffi-bindings = "*"
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[package.extras]
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dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"]
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docs = ["furo", "sphinx", "sphinx-notfound-page"]
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tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
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dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
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docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
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tests = ["hypothesis", "pytest"]
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typing = ["mypy"]
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[[package]]
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name = "argon2-cffi-bindings"
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{file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
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]
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[package.dependencies]
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typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
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[[package]]
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name = "attrs"
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version = "23.1.0"
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@ -524,6 +529,20 @@ files = [
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{file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
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]
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[[package]]
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name = "exceptiongroup"
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version = "1.1.3"
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description = "Backport of PEP 654 (exception groups)"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"},
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||||
{file = "exceptiongroup-1.1.3.tar.gz", hash = "sha256:097acd85d473d75af5bb98e41b61ff7fe35efe6675e4f9370ec6ec5126d160e9"},
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]
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[package.extras]
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test = ["pytest (>=6)"]
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[[package]]
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name = "executing"
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version = "1.2.0"
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
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files = [
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{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
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{file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
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]
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[[package]]
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[[package]]
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name = "jupyter-server"
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version = "2.7.0"
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version = "2.7.1"
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description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "jupyter_server-2.7.0-py3-none-any.whl", hash = "sha256:6a77912aff643e53fa14bdb2634884b52b784a4be77ce8e93f7283faed0f0849"},
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||||
{file = "jupyter_server-2.7.0.tar.gz", hash = "sha256:36da0a266d31a41ac335a366c88933c17dfa5bb817a48f5c02c16d303bc9477f"},
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||||
{file = "jupyter_server-2.7.1-py3-none-any.whl", hash = "sha256:59838bf20759354e2b4222fed702948a934cb21a503e21cd5b03706a456391d9"},
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{file = "jupyter_server-2.7.1.tar.gz", hash = "sha256:76b4ae0b568c331acc9aa904fc4a1194ef9bdefa69f232deb3f3cae802528c05"},
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]
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[package.dependencies]
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@ -1002,7 +1022,7 @@ packaging = "*"
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prometheus-client = "*"
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pywinpty = {version = "*", markers = "os_name == \"nt\""}
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pyzmq = ">=24"
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send2trash = "*"
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send2trash = ">=1.8.2"
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terminado = ">=0.8.3"
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tornado = ">=6.2.0"
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traitlets = ">=5.6.0"
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@ -1033,13 +1053,13 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov",
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[[package]]
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name = "jupyterlab"
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version = "4.0.4"
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version = "4.0.5"
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description = "JupyterLab computational environment"
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||||
optional = false
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||||
python-versions = ">=3.8"
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files = [
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||||
{file = "jupyterlab-4.0.4-py3-none-any.whl", hash = "sha256:23eef35d22be8f2ad9b873ec41ceb2e8c3b0dc8ae740c0f973e2de09e587530f"},
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{file = "jupyterlab-4.0.4.tar.gz", hash = "sha256:049449a56d93202ed204e0e86f96f5a3447a08cfc09fb012fd239e178651cb34"},
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{file = "jupyterlab-4.0.5-py3-none-any.whl", hash = "sha256:13b3a326e7b95d72746fe20dbe80ee1e71165d6905e01ceaf1320eb809cb1b47"},
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{file = "jupyterlab-4.0.5.tar.gz", hash = "sha256:de49deb75f9b9aec478ed04754cbefe9c5d22fd796a5783cdc65e212983d3611"},
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]
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[package.dependencies]
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@ -1052,6 +1072,7 @@ jupyter-server = ">=2.4.0,<3"
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jupyterlab-server = ">=2.19.0,<3"
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notebook-shim = ">=0.2"
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packaging = "*"
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tomli = {version = "*", markers = "python_version < \"3.11\""}
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tornado = ">=6.2.0"
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traitlets = "*"
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@ -1960,88 +1981,104 @@ files = [
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[[package]]
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name = "pyzmq"
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||||
version = "25.1.0"
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version = "25.1.1"
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description = "Python bindings for 0MQ"
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||||
optional = false
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python-versions = ">=3.6"
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files = [
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||||
{file = "pyzmq-25.1.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:1a6169e69034eaa06823da6a93a7739ff38716142b3596c180363dee729d713d"},
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||||
{file = "pyzmq-25.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:19d0383b1f18411d137d891cab567de9afa609b214de68b86e20173dc624c101"},
|
||||
{file = "pyzmq-25.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f1e931d9a92f628858a50f5bdffdfcf839aebe388b82f9d2ccd5d22a38a789dc"},
|
||||
{file = "pyzmq-25.1.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:97d984b1b2f574bc1bb58296d3c0b64b10e95e7026f8716ed6c0b86d4679843f"},
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||||
{file = "pyzmq-25.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:154bddda2a351161474b36dba03bf1463377ec226a13458725183e508840df89"},
|
||||
{file = "pyzmq-25.1.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:cb6d161ae94fb35bb518b74bb06b7293299c15ba3bc099dccd6a5b7ae589aee3"},
|
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{file = "pyzmq-25.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:90146ab578931e0e2826ee39d0c948d0ea72734378f1898939d18bc9c823fcf9"},
|
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{file = "pyzmq-25.1.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:831ba20b660b39e39e5ac8603e8193f8fce1ee03a42c84ade89c36a251449d80"},
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[package.dependencies]
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|
||||
{file = "tornado-6.3.2.tar.gz", hash = "sha256:4b927c4f19b71e627b13f3db2324e4ae660527143f9e1f2e2fb404f3a187e2ba"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:805d507b1f588320c26f7f097108eb4023bbaa984d63176d1652e184ba24270a"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1bd19ca6c16882e4d37368e0152f99c099bad93e0950ce55e71daed74045908f"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ac51f42808cca9b3613f51ffe2a965c8525cb1b00b7b2d56828b8045354f76a"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71a8db65160a3c55d61839b7302a9a400074c9c753040455494e2af74e2501f2"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:ceb917a50cd35882b57600709dd5421a418c29ddc852da8bcdab1f0db33406b0"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:7d01abc57ea0dbb51ddfed477dfe22719d376119844e33c661d873bf9c0e4a16"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:9dc4444c0defcd3929d5c1eb5706cbe1b116e762ff3e0deca8b715d14bf6ec17"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-win32.whl", hash = "sha256:65ceca9500383fbdf33a98c0087cb975b2ef3bfb874cb35b8de8740cf7f41bd3"},
|
||||
{file = "tornado-6.3.3-cp38-abi3-win_amd64.whl", hash = "sha256:22d3c2fa10b5793da13c807e6fc38ff49a4f6e1e3868b0a6f4164768bb8e20f5"},
|
||||
{file = "tornado-6.3.3.tar.gz", hash = "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2419,6 +2467,17 @@ files = [
|
|||
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
|
||||
test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.7.1"
|
||||
description = "Backported and Experimental Type Hints for Python 3.7+"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
|
||||
{file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2023.3"
|
||||
|
|
@ -2527,5 +2586,5 @@ files = [
|
|||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "d73b8b7f9822e735a730bf9c4c1b9a8a3dd0e81bc83691fc31af48059157e553"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "7e415dbe7c926e97929278155c278e89c7a8dcbfa75e6df1a77c2beb7b6a01ea"
|
||||
|
|
|
|||
28
proposal.md
28
proposal.md
|
|
@ -7,33 +7,39 @@ argument.
|
|||
|
||||
### What central question are you interested in exploring? Why are you interested in exploring this question?
|
||||
|
||||
*This should be the big picture question that you ask; use at least 5
|
||||
sentences to describe why you are interested in it.*
|
||||
What factors account for women taking a career in STEM?
|
||||
|
||||
STEM has increasingly become an important sector in the United States as demand for careers outweighs the number of students graduating with a degree in a STEM-related field. While demand increases, there is also a trend present with demographics entering STEM degree programs, with men making up the majority of applicants and graduates with only a small margin of women doing the same.
|
||||
|
||||
Diversity is one of the most important factors in innovation. Rooms of "yes men" that think similarly to one another will only limit the capabilities of future technological breakthroughs. There needs to be a variety of methods developed to get women into STEM degree programs and careers, but in order for these methods to be effective, specific data needs to be taken into account.
|
||||
|
||||
This study will explore trends regarding women in STEM programs and careers, how they got to this point, and what can be done to increase numbers.
|
||||
|
||||
### What specific research questions will you investigate?
|
||||
|
||||
*List 2-4 specific research questions. Each should be answerable
|
||||
using your data set.*
|
||||
1. Which STEM majors contain the highest concentration of women? (main question)
|
||||
2. Which college majors contain the highest number of women?
|
||||
3. How many women enter STEM careers?
|
||||
4. How can the number of women increase in these areas?
|
||||
|
||||
## Data source
|
||||
|
||||
### What data set will you use to answer your overarching question?
|
||||
|
||||
*Give the title of your data set and provide a link to your data.*
|
||||
College Majors
|
||||
|
||||
https://github.com/fivethirtyeight/data/tree/master/college-majors
|
||||
|
||||
### Where is this data from?
|
||||
|
||||
*Describe the source of the data set--not just where you downloaded it, but
|
||||
the person or organization who gathered the data. Explain why you trust them.*
|
||||
The data collected is from the American Community Survey 2010-2012 Public Use Microdata Series, which was compiled as a direct result of the United States Census. Since this was collected as part of the US Census, there is a high level of credibility as it was collected using a government agency under specific rules and regulations. This data was then used to publish an article titled "The Economic Guide To Picking A College Major" by Ben Casselman and published on FiveThirtyEight, an affiliate of ABC News.
|
||||
|
||||
### What is this data about?
|
||||
|
||||
*Describe the nature of the data in the dataset, including the number of rows
|
||||
and some of the columns which will be important to you.*
|
||||
This dataset utilizes the Census data of all college graduates who completed it along with a list of college majors, and as such there is not information immediately provided for the number of recent grads and grad students (this can be confirmed using the sample_size row). For these recent grads and grad students, the main focus will be on women-stem, which provides a subset of recent-grads and focuses on the spread of women in all STEM related fields of study. In this subset, ShareWomen will be the main area of focus.
|
||||
|
||||
## Methods
|
||||
|
||||
### How will you use your data set to answer your quantitative questions?
|
||||
|
||||
*For each research question, explain what you will do with the data set
|
||||
to answer the question, and how you will present your answer (e.g. a chart or a table).*
|
||||
For the main question, there is one graph I would like to make. One will include the 77 college majors with the percentage of women in each of those majors. The graph will also place the majors in order of their median salary to determine if there is a correlation between salary and percentage of women in that specific field. This could also be presented in two graphs, with one focusing on the percentage of women in each STEM field in order of frequency, and separate the earnings median and place into another graph.
|
||||
|
|
|
|||
|
|
@ -4,10 +4,9 @@ version = "0.1.0"
|
|||
description = ""
|
||||
authors = ["Chris Proctor <chris@chrisproctor.net>"]
|
||||
readme = "README.md"
|
||||
packages = [{include = "project_argument"}]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.11"
|
||||
python = "^3.10"
|
||||
jupyter = "^1.0.0"
|
||||
seaborn = "^0.12.2"
|
||||
pandas = "^2.0.3"
|
||||
|
|
|
|||
Loading…
Reference in New Issue