unit-project/1/notebooks/unit-project-1-starter-code.ipynb

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   "source": [
    "# DS-SF-30 | Unit Project 1: Research Design"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first unit project you will create a framework to scope out data science projects.  This framework will provide you with a guide to develop a well-articulated problem statement and analysis plan that will be robust and reproducible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part A.  Evaluate the following problem statement:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> \"Determine which free-tier customers will covert to paying customers, using demographic data collected at signup (age, gender, location, and profession) and customer useage data (days since last log in, and `activity score 1 = active user`, `0 = inactive user`) based on Hooli data from January-April 2015.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 1.  What is the outcome?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: If we make conversion as a scale from 1- 5 with 1 as less likely and 5 as highly likely . We can create a bar chart of # of customers falling under each of the numbers on the scale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 2.  What are the predictors/covariates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: predictors: age, gender, demographics Covariates: customer usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 3.  What timeframe is this data relevent for?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:3- 6 months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 4.  What is the hypothesis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: \n",
    "\n",
    "1Users with higher activity score and highly probable to convert to premium customers.\n",
    "the younger age group/profession of college students & IT industry may have higher usage\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part B.  Let's start exploring our UCLA dataset and answer some simple questions:"
   ]
  },
  {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admit</th>\n",
       "      <th>gre</th>\n",
       "      <th>gpa</th>\n",
       "      <th>prestige</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>660.0</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>640.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2.93</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   admit    gre   gpa  prestige\n",
       "0      0  380.0  3.61       3.0\n",
       "1      1  660.0  3.67       3.0\n",
       "2      1  800.0  4.00       1.0\n",
       "3      1  640.0  3.19       4.0\n",
       "4      0  520.0  2.93       4.0"
      ]
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   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(os.path.join('..', '..', 'dataset', 'dataset-ucla-admissions.csv'))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 5.  Create a data dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: (Use the template below)\n",
    "\n",
    "Variable | Description | Type of Variable\n",
    "---|---|---\n",
    "Var 1 | 0 = Not thing, 1 = Thing | Categorical\n",
    "Var 2 | Thing in unit X | Continuous"
   ]
  },
  {
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   "source": [
    "Variable | Description | Type of Variable\n",
    "---| ---| ---\n",
    "Admit |0 - Not Admit 1 - Admit  | categorical\n",
    "GRE | 200 - 800 | continuous\n",
    "GPA | 0.0 - 4.0 | continuous\n",
    "Prestige | 1.0 - 4.0 | categorical\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to explore the association between X and Y."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 6.  What is the outcome?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: The outcome is Admit = Y , the X factors are GRE , GPA and Prestige"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 7.  What are the predictors/covariates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: GRE and GPA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 8.  What timeframe is this data relevent for?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: THere is no time frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 9.  What is the hypothesis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: People with lower GPA and GRE from higher prestige undergrads would get admitted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 10.  What's the problem statement?\n",
    "\n",
    "> Using your answers to the above questions, write a well-formed problem statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:\n",
    "\n",
    "What is the weight of the undergraduate prestige compared to GRE and GPA of a college grad for UCLA Admission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part C.  Create an exploratory analysis plan by answering the following questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the answers to these questions haven't yet been covered in class yet, this section is optional.  This is by design.  By having you guess or look around for these answers will help make sense once we cover this material in class.  You will not be penalized for wrong answers but we encourage you to give it a try!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 11. What are the goals of the exploratory analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 12.  What are the assumptions of the distribution of data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 13.  How will determine the distribution of your data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 14.  How might outliers impact your analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 15.  How will you test for outliers?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 16.  What is colinearity?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 17.  How will you test for covariance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Question 18.  What is your exploratory analysis plan?\n",
    "\n",
    "> Using the above information, write an exploratory analysis plan that would allow you or a colleague to reproduce your analysis one year from now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:"
   ]
  }
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