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Chris Proctor
2023-08-09 18:52:11 -04:00
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# -----------------------------------------------------------------
# Write your entire commit message above this line.
#
# The first line should be a quick description of your latest progress.
# Then leave a blank line.
# Then, taking as many lines as you want, reflect on the current state
# of your data science project. Write about whatever you want; here are
# a few suggestions:
#
# - Which recent successes are you proud of?
# - What are you currently stuck on? Are there parts of this project
# that you're worried about, or which you don't know how to do?
# - Has your work sparked any new ideas or interests?
# - Have you learned any new skills?

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{
"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": null,
"id": "technical-evans",
"metadata": {},
"outputs": [],
"source": [
"#Include any import statements you will need\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "overhead-sigma",
"metadata": {},
"outputs": [],
"source": [
"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
"\n",
"file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
"dataset_path = \"data/\" + file_name\n",
"\n",
"df = pd.read_csv(dataset_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "heated-blade",
"metadata": {},
"outputs": [],
"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": "3.9.7"
},
"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
}

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# Project proposal
This planning document will also form the introduction of your
argument.
## Overarching Question
### 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 specific research questions will you investigate?
*List 2-4 specific research questions. Each should be answerable
using your data set.*
## 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.*
### 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.*
### 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.*
## 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).*

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pyproject.toml
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[tool.poetry.dependencies] [tool.poetry.dependencies]
python = "^3.11" python = "^3.11"
jupyter = "^1.0.0"
seaborn = "^0.12.2"
pandas = "^2.0.3"
[build-system] [build-system]