{ "cells": [ { "cell_type": "markdown", "id": "worldwide-blood", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "id": "understanding-numbers", "metadata": {}, "source": [ "*✏️ Write 2-3 sentences describing your research.*" ] }, { "cell_type": "markdown", "id": "greater-circular", "metadata": {}, "source": [ "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]" ] }, { "cell_type": "markdown", "id": "appreciated-testimony", "metadata": {}, "source": [ "*✏️ Write 2-3 sentences explaining why this question.*" ] }, { "cell_type": "markdown", "id": "permanent-pollution", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 4, "id": "technical-evans", "metadata": { "tags": [] }, "outputs": [], "source": [ "#Include any import statements you will need\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 5, "id": "overhead-sigma", "metadata": { "tags": [] }, "outputs": [], "source": [ "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n", "\n", "file_name = \"US_births_2000-2014_SSA.csv\"\n", "dataset_path = \"data/\" + file_name\n", "\n", "df = pd.read_csv(dataset_path)" ] }, { "cell_type": "code", "execution_count": 6, "id": "heated-blade", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " year month date_of_month day_of_week births\n", "0 2000 1 1 6 9083\n", "1 2000 1 2 7 8006\n", "2 2000 1 3 1 11363\n", "3 2000 1 4 2 13032\n", "4 2000 1 5 3 12558" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "continental-franklin", "metadata": {}, "source": [ "**Data Overview**\n", "\n", "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*" ] }, { "cell_type": "markdown", "id": "infinite-instrument", "metadata": {}, "source": [ "# Methods and Results" ] }, { "cell_type": "code", "execution_count": null, "id": "basic-canadian", "metadata": {}, "outputs": [], "source": [ "#Import any helper files you need here" ] }, { "cell_type": "markdown", "id": "recognized-positive", "metadata": {}, "source": [ "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" ] }, { "cell_type": "markdown", "id": "graduate-palmer", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "endless-variation", "metadata": {}, "source": [ "*Explain how you will approach this research question below. Consider the following:* \n", " - *Which aspects of the dataset will you use?* \n", " - *How will you reorganize/store the data?* \n", " - *What data science tools/functions will you use and why?* \n", " \n", "✏️ *Write your answer below:*\n", "\n" ] }, { "cell_type": "markdown", "id": "portuguese-japan", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 17, "id": "negative-highlight", "metadata": {}, "outputs": [], "source": [ "#######################################################################\n", "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n", "### \n", "### Your data analysis may include a statistic and/or a data visualization\n", "#######################################################################" ] }, { "cell_type": "code", "execution_count": 16, "id": "victorian-burning", "metadata": {}, "outputs": [], "source": [ "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON " ] }, { "cell_type": "markdown", "id": "collectible-puppy", "metadata": {}, "source": [ "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" ] }, { "cell_type": "markdown", "id": "demographic-future", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "incorporate-roller", "metadata": {}, "source": [ "*Explain how you will approach this research question below. Consider the following:* \n", " - *Which aspects of the dataset will you use?* \n", " - *How will you reorganize/store the data?* \n", " - *What data science tools/functions will you use and why?* \n", "\n", "✏️ *Write your answer below:*\n" ] }, { "cell_type": "markdown", "id": "juvenile-creation", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 14, "id": "pursuant-surrey", "metadata": {}, "outputs": [], "source": [ "#######################################################################\n", "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n", "###\n", "### Your data analysis may include a statistic and/or a data visualization\n", "#######################################################################" ] }, { "cell_type": "code", "execution_count": 15, "id": "located-night", "metadata": {}, "outputs": [], "source": [ "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON " ] }, { "cell_type": "markdown", "id": "infectious-symbol", "metadata": {}, "source": [ "# Discussion" ] }, { "cell_type": "markdown", "id": "furnished-camping", "metadata": { "code_folding": [] }, "source": [ "## Considerations" ] }, { "cell_type": "markdown", "id": "bearing-stadium", "metadata": {}, "source": [ "*It's important to recognize the limitations of our research.\n", "Consider the following:*\n", "\n", "- *Do the results give an accurate depiction of your research question? Why or why not?*\n", "- *What were limitations of your datset?*\n", "- *Are there any known biases in the data?*\n", "\n", "✏️ *Write your answer below:*" ] }, { "cell_type": "markdown", "id": "beneficial-invasion", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", "id": "about-raise", "metadata": {}, "source": [ "*Summarize what you discovered through the research. Consider the following:*\n", "\n", "- *What did you learn about your media consumption/digital habits?*\n", "- *Did the results make sense?*\n", "- *What was most surprising?*\n", "- *How will this project impact you going forward?*\n", "\n", "✏️ *Write your answer below:*" ] } ], "metadata": { "jupytext": { "cell_metadata_json": true, "text_representation": { "extension": ".Rmd", "format_name": "rmarkdown", "format_version": "1.2", "jupytext_version": "1.9.1" } }, "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": "3.10.12" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }