generated from mwc/project_argument
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17
.commit_template
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17
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# -----------------------------------------------------------------
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# Write your entire commit message above this line.
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||||||
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#
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||||||
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# The first line should be a quick description of your latest progress.
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||||||
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# Then leave a blank line.
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||||||
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# Then, taking as many lines as you want, reflect on the current state
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||||||
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# of your data science project. Write about whatever you want; here are
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# a few suggestions:
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#
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# - Which recent successes are you proud of?
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# - What are you currently stuck on? Are there parts of this project
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# that you're worried about, or which you don't know how to do?
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# - Has your work sparked any new ideas or interests?
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# - Have you learned any new skills?
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||||||
360
argument.ipynb
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360
argument.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "worldwide-blood",
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"metadata": {},
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"source": [
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"# Introduction"
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]
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},
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||||||
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{
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||||||
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"cell_type": "markdown",
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"id": "understanding-numbers",
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"metadata": {},
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"source": [
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"*✏️ Write 2-3 sentences describing your research.*"
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]
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},
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{
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||||||
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"cell_type": "markdown",
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"id": "greater-circular",
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||||||
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"metadata": {},
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||||||
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"source": [
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"## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "appreciated-testimony",
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"metadata": {},
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"source": [
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"*✏️ Write 2-3 sentences explaining why this question.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "permanent-pollution",
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"metadata": {},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "technical-evans",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Include any import statements you will need\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "overhead-sigma",
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"metadata": {},
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"outputs": [],
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"source": [
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"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
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"\n",
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"file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
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"dataset_path = \"data/\" + file_name\n",
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"\n",
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"df = pd.read_csv(dataset_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "heated-blade",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "continental-franklin",
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"metadata": {},
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"source": [
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"**Data Overview**\n",
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"\n",
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"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "infinite-instrument",
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"metadata": {},
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"source": [
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"# Methods and Results"
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|
]
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|
},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "basic-canadian",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import any helper files you need here"
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]
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},
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{
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"cell_type": "markdown",
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"id": "recognized-positive",
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"metadata": {},
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"source": [
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"## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "graduate-palmer",
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"metadata": {},
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"source": [
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"### Methods"
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|
]
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},
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{
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"cell_type": "markdown",
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"id": "endless-variation",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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|
" - *Which aspects of the dataset will you use?* \n",
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||||||
|
" - *How will you reorganize/store the data?* \n",
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|
" - *What data science tools/functions will you use and why?* \n",
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" \n",
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||||||
|
"✏️ *Write your answer below:*\n",
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|
"\n"
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|
]
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||||||
|
},
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||||||
|
{
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|
"cell_type": "markdown",
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|
"id": "portuguese-japan",
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"metadata": {},
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"source": [
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"### Results "
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|
]
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|
},
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{
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|
"cell_type": "code",
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|
"execution_count": 17,
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|
"id": "negative-highlight",
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||||||
|
"metadata": {},
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||||||
|
"outputs": [],
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||||||
|
"source": [
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|
"#######################################################################\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",
|
||||||
|
"#######################################################################"
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||||||
|
]
|
||||||
|
},
|
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|
{
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|
"cell_type": "code",
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||||||
|
"execution_count": 16,
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|
"id": "victorian-burning",
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"metadata": {},
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"outputs": [],
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||||||
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"source": [
|
||||||
|
"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
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|
]
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|
},
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||||||
|
{
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||||||
|
"cell_type": "markdown",
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||||||
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"id": "collectible-puppy",
|
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|
"metadata": {},
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||||||
|
"source": [
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||||||
|
"## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
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||||||
|
"id": "demographic-future",
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||||||
|
"metadata": {},
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||||||
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"source": [
|
||||||
|
"### Methods"
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||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"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",
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||||||
|
"execution_count": 14,
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||||||
|
"id": "pursuant-surrey",
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||||||
|
"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",
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||||||
|
"execution_count": 15,
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||||||
|
"id": "located-night",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "infectious-symbol",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
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||||||
|
"# Discussion"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
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||||||
|
"id": "furnished-camping",
|
||||||
|
"metadata": {
|
||||||
|
"code_folding": []
|
||||||
|
},
|
||||||
|
"source": [
|
||||||
|
"## Considerations"
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||||||
|
]
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||||||
|
},
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||||||
|
{
|
||||||
|
"cell_type": "markdown",
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||||||
|
"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
|
||||||
|
}
|
||||||
0
data/.hiddenfile
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0
data/.hiddenfile
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2531
poetry.lock
generated
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2531
poetry.lock
generated
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Load Diff
39
proposal.md
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39
proposal.md
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@@ -0,0 +1,39 @@
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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).*
|
||||||
18
pyproject.toml
Normal file
18
pyproject.toml
Normal file
@@ -0,0 +1,18 @@
|
|||||||
|
[tool.poetry]
|
||||||
|
name = "project-argument"
|
||||||
|
version = "0.1.0"
|
||||||
|
description = ""
|
||||||
|
authors = ["Chris Proctor <chris@chrisproctor.net>"]
|
||||||
|
readme = "README.md"
|
||||||
|
packages = [{include = "project_argument"}]
|
||||||
|
|
||||||
|
[tool.poetry.dependencies]
|
||||||
|
python = "^3.11"
|
||||||
|
jupyter = "^1.0.0"
|
||||||
|
seaborn = "^0.12.2"
|
||||||
|
pandas = "^2.0.3"
|
||||||
|
|
||||||
|
|
||||||
|
[build-system]
|
||||||
|
requires = ["poetry-core"]
|
||||||
|
build-backend = "poetry.core.masonry.api"
|
||||||
Reference in New Issue
Block a user