{ "cells": [ { "cell_type": "markdown", "id": "a9215f5c-525b-4508-8b0d-700f55cbd252", "metadata": {}, "source": [ "## Introduction to Data Science\n", "\n", "#### University of Redlands - DATA 101\n", "#### Prof: Joanna Bieri [joanna_bieri@redlands.edu](mailto:joanna_bieri@redlands.edu)\n", "#### [Class Website: data101.joannabieri.com](https://data101.joannabieri.com)\n", "\n", "---------------------------------------\n", "# Practice Exam 1\n", "---------------------------------------\n", "\n", "This practice exam is **not graded**, but please give it a real try! We will go over it together on **Review Day (Thursday 10/1)**, so bring your work with you. That is what we will work from in class.\n", "\n", "1. Download Day7-PracticeExam.ipynb from the class website and move it into your `Day7` folder in your repo.\n", "2. Work on it a little at a time between now and Review Day.\n", "3. **Commit** and **Push** what you have so I can see how you are doing.\n", "\n", "NOTE:\n", "The practice exam is intended to give you an idea of what an exam in this class might feel like. Remember you will be expected to work on the exam **independently** and submit your work before we get together to work on the exam in class. **15\\% of your Exam grade will come from what you can do independently** so start practicing now and get help if you need it!\n", "\n", "Some things you will notice:\n", "\n", "1. I do not give code in the practice exam, but you can copy and paste the code from your other assignments or the lecture notes.\n", "2. The exam will be open notes, open book, open basic internet search - but you should not use AI to generate your answer. It is usually very easy to see when code has been generated by AI - it will have commands we have not learned or a style of programming that is clearly AI. YOU MUST UNDERSTAND ALL THE CODE YOU SUBMIT!\n", "3. The questions get progressively more involved. Just do one at a time.\n", "4. I expect you to explain your results. I should not have to interpret what your numbers or code outputs mean. Add Markdown cells to describe what YOU see in the results." ] }, { "cell_type": "code", "execution_count": 1, "id": "049c24b6-a6c1-4ccc-b2de-5ea0366d09f8", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:34.075195Z", "iopub.status.busy": "2026-09-16T22:25:34.074571Z", "iopub.status.idle": "2026-09-16T22:25:35.738967Z", "shell.execute_reply": "2026-09-16T22:25:35.738356Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "import plotly.io as pio\n", "pio.renderers.default = 'notebook_connected'" ] }, { "cell_type": "markdown", "id": "0b3b6bfe-df90-4521-9d96-404916911e2b", "metadata": {}, "source": [ "# Practice Exam - EDA - College Majors\n", "\n", "Your solutions should include code written to answer the questions AND words describing the results. For each problem:\n", "\n", "1. Write code to get the answers. It is okay to copy and paste from our lecture notes or your homework assignments.\n", "2. Describe what the code output is telling you.\n", "3. Explain any interesting aspects of your results.\n", "\n", "------------------------\n", "-------------------------" ] }, { "cell_type": "markdown", "id": "1c634098-c93e-41c2-bc4a-523f4c27a333", "metadata": {}, "source": [ "The first step in the process of turning information into knowledge is to summarize and describe the raw information - the data.\n", "In this assignment we explore data on college majors and earnings, specifically the data behind the FiveThirtyEight story [\"The Economic Guide To Picking A College Major\"](https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/).\n", "\n", "These data originally come from the American Community Survey (ACS) 2010-2012 Public Use Microdata Series.\n", "While this is outside the scope of this assignment, if you are curious about how raw data from the ACS were cleaned and prepared, see [the code](https://github.com/fivethirtyeight/data/blob/master/college-majors/college-majors-rscript.R) FiveThirtyEight authors used.\n", "\n", "We should also note that there are many considerations that go into picking a major.\n", "Earnings potential and employment prospects are two of them, and they are important, but they don't tell the whole story.\n", "Keep this in mind as you analyze the data." ] }, { "cell_type": "code", "execution_count": 2, "id": "20e25679-7c2a-4244-be1b-dc01e35f8126", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:35.740823Z", "iopub.status.busy": "2026-09-16T22:25:35.740656Z", "iopub.status.idle": "2026-09-16T22:25:35.984269Z", "shell.execute_reply": "2026-09-16T22:25:35.983766Z" } }, "outputs": [], "source": [ "file_location = 'https://joannabieri.com/introdatascience/data/recent-grads.csv'\n", "DF_raw = pd.read_csv(file_location)" ] }, { "cell_type": "code", "execution_count": 3, "id": "763b02f4-00ce-4b9a-ac1d-792c794109f1", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:35.986156Z", "iopub.status.busy": "2026-09-16T22:25:35.985970Z", "iopub.status.idle": "2026-09-16T22:25:36.003021Z", "shell.execute_reply": "2026-09-16T22:25:36.002651Z" } }, "outputs": [ { "data": { "text/html": [ "
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RankMajor_codeMajorTotalMenWomenMajor_categoryShareWomenSample_sizeEmployed...Part_timeFull_time_year_roundUnemployedUnemployment_rateMedianP25thP75thCollege_jobsNon_college_jobsLow_wage_jobs
012419PETROLEUM ENGINEERING2339.02057.0282.0Engineering0.120564361976...2701207370.018381110000950001250001534364193
122416MINING AND MINERAL ENGINEERING756.0679.077.0Engineering0.1018527640...170388850.11724175000550009000035025750
232415METALLURGICAL ENGINEERING856.0725.0131.0Engineering0.1530373648...133340160.02409673000500001050004561760
342417NAVAL ARCHITECTURE AND MARINE ENGINEERING1258.01123.0135.0Engineering0.10731316758...150692400.0501257000043000800005291020
452405CHEMICAL ENGINEERING32260.021239.011021.0Engineering0.34163128925694...51801669716720.061098650005000075000183144440972
..................................................................
1681693609ZOOLOGY8409.03050.05359.0Biology & Life Science0.637293476259...219036023040.04632026000200003900027712947743
1691705201EDUCATIONAL PSYCHOLOGY2854.0522.02332.0Psychology & Social Work0.81709972125...57212111480.065112250002400034000148861582
1701715202CLINICAL PSYCHOLOGY2838.0568.02270.0Psychology & Social Work0.799859132101...64812933680.149048250002500040000986870622
1711725203COUNSELING PSYCHOLOGY4626.0931.03695.0Psychology & Social Work0.798746213777...96527382140.05362123400192002600024031245308
1721733501LIBRARY SCIENCE1098.0134.0964.0Education0.8779602742...237410870.104946220002000022000288338192
\n", "

173 rows × 21 columns

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" ], "text/plain": [ " Rank Major_code Major Total \\\n", "0 1 2419 PETROLEUM ENGINEERING 2339.0 \n", "1 2 2416 MINING AND MINERAL ENGINEERING 756.0 \n", "2 3 2415 METALLURGICAL ENGINEERING 856.0 \n", "3 4 2417 NAVAL ARCHITECTURE AND MARINE ENGINEERING 1258.0 \n", "4 5 2405 CHEMICAL ENGINEERING 32260.0 \n", ".. ... ... ... ... \n", "168 169 3609 ZOOLOGY 8409.0 \n", "169 170 5201 EDUCATIONAL PSYCHOLOGY 2854.0 \n", "170 171 5202 CLINICAL PSYCHOLOGY 2838.0 \n", "171 172 5203 COUNSELING PSYCHOLOGY 4626.0 \n", "172 173 3501 LIBRARY SCIENCE 1098.0 \n", "\n", " Men Women Major_category ShareWomen Sample_size \\\n", "0 2057.0 282.0 Engineering 0.120564 36 \n", "1 679.0 77.0 Engineering 0.101852 7 \n", "2 725.0 131.0 Engineering 0.153037 3 \n", "3 1123.0 135.0 Engineering 0.107313 16 \n", "4 21239.0 11021.0 Engineering 0.341631 289 \n", ".. ... ... ... ... ... \n", "168 3050.0 5359.0 Biology & Life Science 0.637293 47 \n", "169 522.0 2332.0 Psychology & Social Work 0.817099 7 \n", "170 568.0 2270.0 Psychology & Social Work 0.799859 13 \n", "171 931.0 3695.0 Psychology & Social Work 0.798746 21 \n", "172 134.0 964.0 Education 0.877960 2 \n", "\n", " Employed ... Part_time Full_time_year_round Unemployed \\\n", "0 1976 ... 270 1207 37 \n", "1 640 ... 170 388 85 \n", "2 648 ... 133 340 16 \n", "3 758 ... 150 692 40 \n", "4 25694 ... 5180 16697 1672 \n", ".. ... ... ... ... ... \n", "168 6259 ... 2190 3602 304 \n", "169 2125 ... 572 1211 148 \n", "170 2101 ... 648 1293 368 \n", "171 3777 ... 965 2738 214 \n", "172 742 ... 237 410 87 \n", "\n", " Unemployment_rate Median P25th P75th College_jobs Non_college_jobs \\\n", "0 0.018381 110000 95000 125000 1534 364 \n", "1 0.117241 75000 55000 90000 350 257 \n", "2 0.024096 73000 50000 105000 456 176 \n", "3 0.050125 70000 43000 80000 529 102 \n", "4 0.061098 65000 50000 75000 18314 4440 \n", ".. ... ... ... ... ... ... \n", "168 0.046320 26000 20000 39000 2771 2947 \n", "169 0.065112 25000 24000 34000 1488 615 \n", "170 0.149048 25000 25000 40000 986 870 \n", "171 0.053621 23400 19200 26000 2403 1245 \n", "172 0.104946 22000 20000 22000 288 338 \n", "\n", " Low_wage_jobs \n", "0 193 \n", "1 50 \n", "2 0 \n", "3 0 \n", "4 972 \n", ".. ... \n", "168 743 \n", "169 82 \n", "170 622 \n", "171 308 \n", "172 192 \n", "\n", "[173 rows x 21 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF_raw" ] }, { "cell_type": "markdown", "id": "d2c98753-1b02-4889-81b3-d97627d70824", "metadata": {}, "source": [ "### 1. Describe the data that you see here.\n", "\n", "* How many variables?\n", "* How many observations?\n", "* Are the variables categorical or numerical?\n", "* Are there any variables that you don't know what they mean? If so [CLICK HERE - to see the Git Repo for the data where the variables are defined](https://github.com/fivethirtyeight/data/tree/master/college-majors)\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "30f16d98-040a-41f9-984e-330617d70fe1", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.004671Z", "iopub.status.busy": "2026-09-16T22:25:36.004511Z", "iopub.status.idle": "2026-09-16T22:25:36.006349Z", "shell.execute_reply": "2026-09-16T22:25:36.006018Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 5, "id": "b09fe27b-bcac-46c2-af97-6ca873f5e81d", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.007808Z", "iopub.status.busy": "2026-09-16T22:25:36.007648Z", "iopub.status.idle": "2026-09-16T22:25:36.009830Z", "shell.execute_reply": "2026-09-16T22:25:36.009343Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 6, "id": "0b9165e9-a915-4cd4-898f-06d81ccf22fa", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.011258Z", "iopub.status.busy": "2026-09-16T22:25:36.011104Z", "iopub.status.idle": "2026-09-16T22:25:36.013043Z", "shell.execute_reply": "2026-09-16T22:25:36.012633Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "markdown", "id": "13005d11-f03f-40dd-a53c-9ff76f5dbfe1", "metadata": {}, "source": [ "**(Click Here to Explain your Results)**" ] }, { "cell_type": "markdown", "id": "e6081090-f75b-43f2-aaee-e9e90afbb058", "metadata": {}, "source": [ "### 2. Answer some questions about the data\n", "\n", "* Which major has the lowest unemployment rate? Show a data frame that answers this question, but only show columns that are useful to the answer.\n", "* Which major has the highest percentage of women? Show a data frame that answers this question, but only show columns that are useful to the answer.\n", "* How do the distributions of median income compare across major categories?\n", " * First get a value count on what major categories are in the data. What categories are most or least represented?\n", " * Do some summary statistics using the .groupby() command to see the statistics for each major category.\n", " * Make a histogram that explores median income for each of the major categories (facet_col). How did you choose your number of bins?\n", " * Why do we often choose the median, rather than the mean, to describe the typical income of a group of people? Look this up online if you don't know the answer!\n", " * Answer the question: Which major category has the highest typical (you'll need to decide what this means) median income?" ] }, { "cell_type": "code", "execution_count": 7, "id": "1274bfec-dd4a-4e48-95b7-1952e18c0955", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.014590Z", "iopub.status.busy": "2026-09-16T22:25:36.014431Z", "iopub.status.idle": "2026-09-16T22:25:36.016221Z", "shell.execute_reply": "2026-09-16T22:25:36.015897Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 8, "id": "65955115-72c3-445f-be32-af352ececa93", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.017678Z", "iopub.status.busy": "2026-09-16T22:25:36.017523Z", "iopub.status.idle": "2026-09-16T22:25:36.019302Z", "shell.execute_reply": "2026-09-16T22:25:36.018969Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 9, "id": "8b12bd53-36e0-47b4-9ef5-3cbf6fc065a4", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.020779Z", "iopub.status.busy": "2026-09-16T22:25:36.020624Z", "iopub.status.idle": "2026-09-16T22:25:36.022410Z", "shell.execute_reply": "2026-09-16T22:25:36.022067Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 10, "id": "a595e3c3-8b12-4917-905a-486a305b6baa", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.023850Z", "iopub.status.busy": "2026-09-16T22:25:36.023697Z", "iopub.status.idle": "2026-09-16T22:25:36.025501Z", "shell.execute_reply": "2026-09-16T22:25:36.025170Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 11, "id": "a0fbbb69-ed86-4445-b2b0-6013bf406361", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.026902Z", "iopub.status.busy": "2026-09-16T22:25:36.026750Z", "iopub.status.idle": "2026-09-16T22:25:36.028527Z", "shell.execute_reply": "2026-09-16T22:25:36.028202Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "markdown", "id": "04306ea4-b398-411c-b91e-86a68d24a8d6", "metadata": {}, "source": [ "**(Click Here to Explain your Results)**" ] }, { "cell_type": "markdown", "id": "39d4118e-26d8-4f81-8d65-569c4b6d97d1", "metadata": {}, "source": [ "### 3. More complicated questions\n", "\n", "* Are all STEM fields the same in terms job opportunities?\n", " * Assume the following are STEM major categories\n", " \n", " ``` [\"Biology & Life Science\", \"Computers & Mathematics\", \"Engineering\",\"Physical Sciences\"]```\n", " \n", " * Create a DataFrame (using a mask) so you only look at STEM majors.\n", " * Which STEM majors have median salaries equal to or less than the median for all majors' median earnings?\n", "* What types of majors do women tend to major in?\n", " * Create a scatterplot of median income vs. proportion of women in that major, coloured by whether the major is in a STEM field or not." ] }, { "cell_type": "code", "execution_count": 12, "id": "1639bd96-4f56-4ccb-b28c-51b09b961ae4", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.030047Z", "iopub.status.busy": "2026-09-16T22:25:36.029896Z", "iopub.status.idle": "2026-09-16T22:25:36.031635Z", "shell.execute_reply": "2026-09-16T22:25:36.031313Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 13, "id": "4e0a96d0-07ee-4693-8972-829ab3518125", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.033015Z", "iopub.status.busy": "2026-09-16T22:25:36.032862Z", "iopub.status.idle": "2026-09-16T22:25:36.034607Z", "shell.execute_reply": "2026-09-16T22:25:36.034284Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "code", "execution_count": 14, "id": "be654ff2-e684-4eda-8843-4ac9f07a74ae", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.035989Z", "iopub.status.busy": "2026-09-16T22:25:36.035841Z", "iopub.status.idle": "2026-09-16T22:25:36.037596Z", "shell.execute_reply": "2026-09-16T22:25:36.037270Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "markdown", "id": "b9f65c22-2c17-495a-be1e-66e1dd2e183a", "metadata": {}, "source": [ "**(Click Here to Explain your Results)**" ] }, { "cell_type": "markdown", "id": "b3df8412-b946-4812-ba3b-873fb0eb8d7c", "metadata": {}, "source": [ "### 4. Ask a question of interest to you." ] }, { "cell_type": "code", "execution_count": 15, "id": "25a08e99-946a-4003-a477-1dc941e4357e", "metadata": { "execution": { "iopub.execute_input": "2026-09-16T22:25:36.039117Z", "iopub.status.busy": "2026-09-16T22:25:36.038968Z", "iopub.status.idle": "2026-09-16T22:25:36.040721Z", "shell.execute_reply": "2026-09-16T22:25:36.040396Z" } }, "outputs": [], "source": [ "#" ] }, { "cell_type": "markdown", "id": "0940ef30-6ee7-42c8-8747-80f4b2bb8e3f", "metadata": {}, "source": [ "**(Click Here to Explain your Results)**" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }