explore.ipynb

{"cells":[{"source":"# Change directory to VSCode workspace root so that relative path loads work correctly. Turn this addition off with the DataScience.changeDirOnImportExport setting\r\n# ms-python.python added\r\nimport os\r\ntry:\r\n\tos.chdir(os.path.join(os.getcwd(), '..'))\r\n\tprint(os.getcwd())\r\nexcept:\r\n\tpass\r\n","cell_type":"code","outputs":[],"metadata":{},"execution_count":0},{"cell_type":"markdown","metadata":{},"source":[" # Exploratory Analysis\n"," We'll be exploring the CIS enrollment data to find patterns and see what\n"," information we can resolve. Danny wrote a handy data structure for the data\n"," that we're going to try here."]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from test_extract import Data\n","import re\n","import seaborn as sns\n","\n","FILENAME = \"data/CIS_enrollment.csv\"\n","ENCODING = \"iso-8859-1\"\n","\n","data = Data(FILENAME)\n","raw  = pd.read_csv(FILENAME, encoding=ENCODING)\n",""]},{"cell_type":"markdown","metadata":{},"source":[" # Pre-processing\n"," One challenge we're going to run into is that the course data makes no\n"," distinction among lectures and discussion blocks. We can see three cases:\n","\n"," * Lecture with discussion - we should remove the discussion\n"," * Lecture without discussion - this is fine\n"," * Discussion with no lecture - we need to retain these records\n","\n"," This presents a pretty significant challenge to a filtering procedure that\n"," we'd like to develop."]},{"cell_type":"markdown","metadata":{},"source":[" It would be really useful to be able to identify core classes for a major. One\n"," way to do that would be to identify what percentage of students who declare a\n"," major have taken that class. Let's see how that works out."]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"72.79% of Computer Science majors take CS1110\n"}],"source":["def get_students_in_major(major):\n","    return dict(filter(lambda s: s[1].major == major, data.students.items()))\n","\n","def get_prop(course, major=\"\"):\n","    if major == \"\":\n","        students = data.students\n","    else:\n","        students = filter(lambda s: s[1].major == major, data.students.items())\n","\n","    return len(data.courses[course].students) / len(dict(students))\n","\n","course = \"CS1110\"\n","major = \"Computer Science\"\n","p = get_prop(course, major)\n","print(f\"{p:.2%} of {major} majors take {course}\")\n",""]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/plain":"<BarContainer object of 12 artists>"},"execution_count":3,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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is."]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x1f74bf1e400>"},"execution_count":4,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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Axes>"},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["pattern = re.compile(r\"^CS\\d{4}$\")\n","cs_courses = list(filter(lambda c: re.match(pattern, c), data.courses.keys()))\n","\n","y = [get_prop(course, major) for course in cs_courses]\n","sns.kdeplot(y, shade=True)\n",""]},{"cell_type":"markdown","metadata":{},"source":[" Let's see what some of those top classes are."]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":"c:\\Users\\Sam\\Documents\\cornell\\CDS\\Pathways-exploration\\env\\lib\\site-packages\\ipykernel_launcher.py:15: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n  from ipykernel import kernelapp as app\n"},{"data":{"image/png":"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= plt.subplots(1,1, figsize=(6,6))\n","ax.barh(sub_df.Course, sub_df.Proportion, color=sub_df.Core)\n","ax.invert_yaxis()\n","plt.title(\"Proportion of CS Majors who take CS Classes\")\n","fig.show()\n",""]},{"cell_type":"markdown","metadata":{},"source":[" We can immediately see that the 5 core courses plus the two introductory\n"," courses have the highest attendance from CS majors. But we can also see some\n"," other looming challenges.\n","\n"," ## Honors Classes\n"," A number of important CS courses have honors options, including 1110, 2110,\n"," and now 2802, which are some of the most popular courses. If we could reliably\n"," aggregate these together, it would probably move those courses further up.\n"," More importantly though, we need to think about how to visualize pathways\n"," where an honors class might be an equally viable option.\n","\n"," ## Complements\n"," This is less of an issue, as it's essentially a specification of the central\n"," problem we're trying to solve with this project. CS 3420 (embedded systems)\n"," can substitute for 3410 in the CS core, but it's a lot less popular and\n"," doesn't seem to have the trappings of a \"core class\". This is only really an\n"," issue to the extent that we want to literally show users what the core major\n"," courses based on the data we have access to."]},{"cell_type":"markdown","metadata":{},"source":[" I'm going to take a quick detour here to demo the\n","\n"," ## Courses of Study API\n"," This is a bit rocky, since virtually the only useful endpoint the API provides\n"," is the `search/classes` endpoint, which requires you to enter all the\n"," parameters of your course (except the number of the course, which isn't an\n"," allowed search term) and then find the course you want in the results. This is\n"," really annoying but if you abstract it away it's useful enough that I can get\n"," over it. Here's a demo."]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"{\n  \"strm\": 2741,\n  \"crseId\": 358592,\n  \"crseOfferNbr\": 1,\n  \"subject\": \"CS\",\n  \"catalogNbr\": \"4780\",\n  \"titleShort\": \"Machine Learning Intell Sys\",\n  \"titleLong\": \"Machine Learning for Intelligent Systems\",\n  \"enrollGroups\": [\n    {\n      \"classSections\": [\n        {\n          \"ssrComponent\": \"LEC\",\n          \"ssrComponentLong\": \"Lecture\",\n          \"section\": \"001\",\n          \"classNbr\": 12632,\n          \"meetings\": [\n            {\n              \"classMtgNbr\": 1,\n              \"timeStart\": \"02:55PM\",\n              \"timeEnd\": \"04:10PM\",\n              \"startDt\": \"08/29/2019\",\n              \"endDt\": \"12/10/2019\",\n              \"instructors\": [\n                {\n                  \"instrAssignSeq\": 4,\n                  \"netid\": \"nh386\",\n                  \"firstName\": \"Nika\",\n                  \"middleName\": \"\",\n                  \"lastName\": \"Haghtalab\"\n                },\n                {\n                  \"instrAssignSeq\": 1,\n                  \"netid\": \"tj36\",\n                  \"firstName\": \"Thorsten\",\n                  \"middleName\": \"\",\n                  \"lastName\": \"Joachims\"\n                }\n              ],\n              \"pattern\": \"TR\",\n              \"facilityDescr\": \"Statler Hall 185-Aud\",\n              \"bldgDescr\": \"Statler Hall\",\n              \"facilityDescrshort\": \"STL185\",\n              \"meetingTopicDescription\": \"\"\n            }\n          ],\n          \"notes\": [\n            {\n              \"classNotesSeq\": 1,\n              \"descrlong\": \"Enrollment limited to CIS students only. All others may add themselves to the waitlist during add/drop. Please go to http://www.cs.cornell.edu/courseinfo/enrollment for updates. It is expected that undergraduate students enroll in the 4000-level section of this class and graduate students enroll in the 5000-level section of this class.\"\n            }\n          ],\n          \"campus\": \"MAIN\",\n          \"campusDescr\": \"Ithaca\",\n          \"location\": \"ITH\",\n          \"locationDescr\": \"Ithaca, NY (Main Campus)\",\n          \"startDt\": \"08/29/2019\",\n          \"endDt\": \"12/10/2019\",\n          \"addConsent\": \"N\",\n          \"addConsentDescr\": \"No Special Consent Required\",\n          \"isComponentGraded\": true,\n          \"instructionMode\": \"P\",\n          \"instrModeDescrshort\": \"In Person\",\n          \"instrModeDescr\": \"In Person\",\n          \"topicDescription\": \"\"\n        }\n      ],\n      \"unitsMinimum\": 4,\n      \"unitsMaximum\": 4,\n      \"componentsOptional\": [],\n      \"componentsRequired\": [\n        \"LEC\"\n      ],\n      \"gradingBasis\": \"OPT\",\n      \"gradingBasisShort\": \"Stdnt Opt\",\n      \"gradingBasisLong\": \"Letter or S/U grades\",\n      \"simpleCombinations\": [\n        {\n          \"subject\": \"CS\",\n          \"catalogNbr\": \"5780\",\n          \"type\": \"W\"\n        }\n      ],\n      \"sessionCode\": \"1\",\n      \"sessionBeginDt\": \"08/29/2019\",\n      \"sessionEndDt\": \"12/10/2019\",\n      \"sessionLong\": \"Regular Academic Session\"\n    }\n  ],\n  \"description\": \"An introduction to machine learning for intelligent systems (e.g. search engines, recommender systems, ad placement). Topics include supervised learning, statistical learning theory, and online learning.\",\n  \"catalogBreadth\": \"\",\n  \"catalogDistr\": \"\",\n  \"catalogLang\": \"\",\n  \"catalogForbiddenOverlaps\": \"\",\n  \"catalogAttribute\": \"\",\n  \"catalogWhenOffered\": \"Fall.\",\n  \"catalogComments\": \"This course can be taken independently or in any order with CS 4786/CS 5786.\",\n  \"catalogPrereqCoreq\": \"Prerequisite: probability theory (BTRY 3080, ECON 3130, MATH 4710, or strong performance in ENGRD 2700\\u00a0or equivalent); linear algebra (MATH 1920\\u00a0or equivalent); CS 2110\\u00a0or equivalent programming proficiency.\",\n  \"catalogFee\": \"\",\n  \"catalogSatisfiesReq\": \"\",\n  \"catalogPermission\": \"\",\n  \"catalogCourseSubfield\": \"\",\n  \"catalogOutcomes\": null,\n  \"acadCareer\": \"UG\",\n  \"acadGroup\": \"EN\"\n}\n"}],"source":["import urllib.request\n","import json\n","recent_semesters = ['FA19']#, 'SP19']\n","\n","def get_info(course):\n","    dept = course[:-4]\n","    num  = course[-4:]\n","    for semester in recent_semesters:\n","        url = f\"https://classes.cornell.edu/api/2.0/search/classes.json?roster={semester}&subject={dept}\"\n","        response = urllib.request.urlopen(url)\n","        data = json.load(response)\n","\n","        if data['status'] != 'success':\n","            continue\n","\n","        for course in data['data']['classes']:\n","            if course['catalogNbr'] == num:\n","                return course\n","\n","    else:\n","        raise ValueError(\"Course number does not exist\")\n","\n","    #return class_dat\n","\n","d = get_info(\"CS4780\")\n","print(json.dumps(d, indent=2))\n",""]},{"cell_type":"markdown","metadata":{},"source":[" Nice! This gives us way more information than we could ever possibly want.\n"," Some useful keys to this json file:\n","\n"," - `titleLong`: The full name of the course\n"," - `titleShort`: An abbreviation of the course title\n"," - `description`: The full text course description\n"," - `catalogPrereqCoreq`: Text describing prerequisites. Still in a string\n","   that's hard to parse but nevertheless present.\n"," - `catalogWhenOffered`: Pretty useful if you're trying to segment a pathway\n","   into specific semesters.\n"," - `enrollGroups/n/unitsMinimum`: The number of credits the course counts for\n","\n"," There's not really any actual documentation, much less models for this API so\n"," most of this is inferred. In any case, it's a good way to get some basic\n"," course info so we don't have to send anyone away from the site. I don't know\n"," what response times look like (i.e. whether we can or should load this\n"," dynamically) but I can see this being useful in a hover or a preview box."]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/markdown":"# INFO1300: Introductory Design and Programming for the Web (3)\n\nThe World Wide Web is both a technology and a pervasive and powerful resource in our society and culture. To build functional and effective web sites, students need technical and design skills as well as analytical skills for understanding who is using the web, in what ways they are using it, and for what purposes. In this course, students develop skills in all three of these areas through the use of technologies such as XHTML, Cascading Stylesheets, and PHP. Students study how web sites are deployed and used, usability issues on the web, user-centered design, and methods for visual layout and information architecture. Through the web, this course provides an introduction to the interdisciplinary field of information science.\n\n**Offered in the** Fall.","text/plain":"<IPython.core.display.Markdown object>"},"metadata":{},"output_type":"display_data"}],"source":["from IPython.display import display, Markdown\n","\n","course = \"INFO1300\"\n","d = get_info(course)\n","\n","text = [\n","    f\"# {course}: {d['titleLong']} ({d['enrollGroups'][0]['unitsMinimum']})\\n\\n\",\n","    f\"{d['description']}\\n\\n\"\n","    f\"**Offered in the** {d['catalogWhenOffered']}\"\n","]\n","\n","display(Markdown(\"\".join(text)))\n",""]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"name":"python","codemirror_mode":{"name":"ipython","version":3}},"orig_nbformat":2,"file_extension":".py","mimetype":"text/x-python","name":"python","npconvert_exporter":"python","pygments_lexer":"ipython3","version":3}}