├── figs ├── cover.png └── review_safari.PNG ├── README.md └── jupyter_notebooks ├── chapter5.ipynb ├── .ipynb_checkpoints └── chapter5-checkpoint.ipynb └── chapter2.ipynb /figs/cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dvaughan79/analyticalskillsbook/HEAD/figs/cover.png -------------------------------------------------------------------------------- /figs/review_safari.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dvaughan79/analyticalskillsbook/HEAD/figs/review_safari.PNG -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Analytical Skills for AI and Data Science 2 | 3 | ![alt text](/figs/cover.png "Early Release Cover") 4 | 5 | This repository includes all the code used in the book _Analytical Skills for AI and Data Science_ and other additional information that may provide useful. 6 | 7 | **The book will be available online on the last week of May 2020.** 8 | 9 | 10 | ### Some reviews 11 | ![alt text](/figs/review_safari.PNG "review from Safari") 12 | 13 | ### Tips for installing python 14 | 15 | If you're new to python, I recommend downloading the [Anaconda Distribution](https://docs.anaconda.com/anaconda/install/). Please follow the instructions thereby provided. 16 | 17 | 18 | ### Organization 19 | 20 | All code is provided as chapter-specific Jupyter notebooks (download instructions [here](https://jupyter.org/install)) that are rendered on Github. 21 | 22 | ### Table of Contents 23 | 24 | * **Chapter 1**: *Analytical Thinking and the AI-Driven Enterprise* 25 | * **Chapter 2**: *[Intro to Analytical Thinking](/jupyter_notebooks/chapter2.ipynb)* 26 | * **Chapter 3**: *Learning to Ask Good Business Questions* 27 | * **Chapter 4**: *Actions, Levers, and Decisions* 28 | * **Chapter 5**: *[From Actions to Consquences: Learning How to Simplify](/jupyter_notebooks/chapter5.ipynb)* 29 | * **Chapter 6**: *[Uncertainty](/jupyter_notebooks/chapter6.ipynb)* 30 | * **Chapter 7**: *[Optimization](/jupyter_notebooks/chapter7.ipynb)* 31 | * **Chapter 8**: *Wrapping Up* 32 | * **Appendix**: *[A Brief Intro to Machine Learning (and AB testing)](/jupyter_notebooks/appendix.ipynb)* -------------------------------------------------------------------------------- /jupyter_notebooks/chapter5.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 118, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import pandas as pd\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "%matplotlib inline" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "# Business case for an offer from a startup\n", 20 | "\n", 21 | "* See corresponding section in Chapter 5" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 119, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "def discount_rate(i,T):\n", 31 | " '''\n", 32 | " Compute discount rate given base anual inflation (i) and maturity (T)\n", 33 | " '''\n", 34 | " r_it = (1+i)**T - 1\n", 35 | " return r_it" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 120, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "def npv_offer(wage_offer,discount_rate,stocks,price_stocks):\n", 45 | " '''\n", 46 | " Compute the simplified (2-steps) npv of the offer\n", 47 | " '''\n", 48 | " value_equity = stocks*price_stocks\n", 49 | " npv = wage_offer + (wage_offer + value_equity)/(1+discount_rate)\n", 50 | " return npv" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 121, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [ 59 | "def npv_base(wage_offer,discount_rate):\n", 60 | " '''\n", 61 | " Compute the simplified (2-steps) npv of base salary\n", 62 | " '''\n", 63 | " npv = wage_offer + wage_offer/(1+discount_rate)\n", 64 | " return npv" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 122, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "name": "stdout", 74 | "output_type": "stream", 75 | "text": [ 76 | "0.1698585600000002\n" 77 | ] 78 | } 79 | ], 80 | "source": [ 81 | "# Check discount_rate function\n", 82 | "inflation_rate = 0.04\n", 83 | "periods_vesting = 4\n", 84 | "print(discount_rate(i=inflation_rate, T=periods_vesting))" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 123, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "name": "stdout", 94 | "output_type": "stream", 95 | "text": [ 96 | "Value of the job offer = 165.48041910297258\n", 97 | "Value of current job = 185.48041910297258\n" 98 | ] 99 | } 100 | ], 101 | "source": [ 102 | "# Check npv functions\n", 103 | "wage_base = 100\n", 104 | "wage_reduction = 0.8\n", 105 | "wage_offer= wage_base*wage_reduction\n", 106 | "my_discount_rate = discount_rate(i=inflation_rate, T=periods_vesting)\n", 107 | "stocks = 100\n", 108 | "price_stocks = 0.2\n", 109 | "offer_value = npv_offer(wage_offer, my_discount_rate, stocks, price_stocks)\n", 110 | "base_value = npv_base(wage_base, my_discount_rate)\n", 111 | "print('Value of the job offer = {0}'.format(offer_value))\n", 112 | "print('Value of current job = {0}'.format(base_value))" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "metadata": {}, 118 | "source": [ 119 | "### Compute maximum wage reduction you're willing to accept" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 124, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "\n" 132 | ] 133 | }, 134 | { 135 | "data": { 136 | "image/png": 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\n", 137 | "text/plain": [ 138 | "
" 139 | ] 140 | }, 141 | "metadata": { 142 | "needs_background": "light" 143 | }, 144 | "output_type": "display_data" 145 | } 146 | ], 147 | "source": [ 148 | "# Plot for different wages\n", 149 | "wage_red_grid = 100*(1-np.linspace(0.8,1,40))\n", 150 | "reduction_df = pd.DataFrame(index=wage_red_grid, columns=['pct'])\n", 151 | "for w,red in enumerate(wage_red_grid):\n", 152 | " offer_red = wage_base*(1-red/100)\n", 153 | " offer_value_r = npv_offer(offer_red, my_discount_rate, stocks, price_stocks)\n", 154 | " reduction_df.pct.loc[red] = offer_value_r\n", 155 | " \n", 156 | "fig, ax = plt.subplots(figsize=(10,6))\n", 157 | "ax.plot(reduction_df.index, reduction_df.pct, color='k')\n", 158 | "ax.plot([0,20],[base_value,base_value], color='r')\n", 159 | "ymin,ymax=ax.get_ylim()\n", 160 | "ax.axis([0,20,ymin,ymax])\n", 161 | "ax.set_title('Finding the Maximum Wage Reduction', fontsize=18)\n", 162 | "ax.set_ylabel('$', fontsize=14)\n", 163 | "ax.set_xlabel('% Wage Reduction', fontsize=16)\n", 164 | "base_value_str = '$'+str(np.around(base_value,decimals=0))[:3]\n", 165 | "ax.text(2.5,base_value,'Current Base = {0}'.format(base_value_str), verticalalignment='top', color='r', fontsize=14)\n", 166 | "ax.text(2.0,200,'Offers for given wage reduction', fontsize=14)\n", 167 | "# plot max wage reduction: find the intersection of both lines\n", 168 | "max_wage_reduction = np.abs(reduction_df.pct-base_value).astype('float').idxmin()\n", 169 | "ax.plot([max_wage_reduction, max_wage_reduction],[ymin, base_value], ls='--', color='k')\n", 170 | "max_wage_str = '{0}%'.format(np.around(max_wage_reduction,decimals=1))\n", 171 | "ax.text(max_wage_reduction,170,max_wage_str, horizontalalignment='right',\n", 172 | " fontsize=12)\n", 173 | "print('')" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "### You can perform similar analyses:\n", 181 | "\n", 182 | "1. Given current wage reduction, what is the minimum equity value I'm willing to accept\n", 183 | "2. How much is simplifying to 2 periods buying for us? How different would my decision be if I do the actual calculation?" 184 | ] 185 | } 186 | ], 187 | "metadata": { 188 | "kernelspec": { 189 | "display_name": "Python 3", 190 | "language": "python", 191 | "name": "python3" 192 | }, 193 | "language_info": { 194 | "codemirror_mode": { 195 | "name": "ipython", 196 | "version": 3 197 | }, 198 | "file_extension": ".py", 199 | "mimetype": "text/x-python", 200 | "name": "python", 201 | "nbconvert_exporter": "python", 202 | "pygments_lexer": "ipython3", 203 | "version": "3.7.3" 204 | } 205 | }, 206 | "nbformat": 4, 207 | "nbformat_minor": 2 208 | } 209 | -------------------------------------------------------------------------------- /jupyter_notebooks/.ipynb_checkpoints/chapter5-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 118, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import pandas as pd\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "%matplotlib inline" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "# Business case for an offer from a startup\n", 20 | "\n", 21 | "* See corresponding section in Chapter 5" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 119, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "def discount_rate(i,T):\n", 31 | " '''\n", 32 | " Compute discount rate given base anual inflation (i) and maturity (T)\n", 33 | " '''\n", 34 | " r_it = (1+i)**T - 1\n", 35 | " return r_it" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 120, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "def npv_offer(wage_offer,discount_rate,stocks,price_stocks):\n", 45 | " '''\n", 46 | " Compute the simplified (2-steps) npv of the offer\n", 47 | " '''\n", 48 | " value_equity = stocks*price_stocks\n", 49 | " npv = wage_offer + (wage_offer + value_equity)/(1+discount_rate)\n", 50 | " return npv" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 121, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [ 59 | "def npv_base(wage_offer,discount_rate):\n", 60 | " '''\n", 61 | " Compute the simplified (2-steps) npv of base salary\n", 62 | " '''\n", 63 | " npv = wage_offer + wage_offer/(1+discount_rate)\n", 64 | " return npv" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 122, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "name": "stdout", 74 | "output_type": "stream", 75 | "text": [ 76 | "0.1698585600000002\n" 77 | ] 78 | } 79 | ], 80 | "source": [ 81 | "# Check discount_rate function\n", 82 | "inflation_rate = 0.04\n", 83 | "periods_vesting = 4\n", 84 | "print(discount_rate(i=inflation_rate, T=periods_vesting))" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 123, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "name": "stdout", 94 | "output_type": "stream", 95 | "text": [ 96 | "Value of the job offer = 165.48041910297258\n", 97 | "Value of current job = 185.48041910297258\n" 98 | ] 99 | } 100 | ], 101 | "source": [ 102 | "# Check npv functions\n", 103 | "wage_base = 100\n", 104 | "wage_reduction = 0.8\n", 105 | "wage_offer= wage_base*wage_reduction\n", 106 | "my_discount_rate = discount_rate(i=inflation_rate, T=periods_vesting)\n", 107 | "stocks = 100\n", 108 | "price_stocks = 0.2\n", 109 | "offer_value = npv_offer(wage_offer, my_discount_rate, stocks, price_stocks)\n", 110 | "base_value = npv_base(wage_base, my_discount_rate)\n", 111 | "print('Value of the job offer = {0}'.format(offer_value))\n", 112 | "print('Value of current job = {0}'.format(base_value))" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "metadata": {}, 118 | "source": [ 119 | "### Compute maximum wage reduction you're willing to accept" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 124, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "\n" 132 | ] 133 | }, 134 | { 135 | "data": { 136 | "image/png": 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\n", 137 | "text/plain": [ 138 | "
" 139 | ] 140 | }, 141 | "metadata": { 142 | "needs_background": "light" 143 | }, 144 | "output_type": "display_data" 145 | } 146 | ], 147 | "source": [ 148 | "# Plot for different wages\n", 149 | "wage_red_grid = 100*(1-np.linspace(0.8,1,40))\n", 150 | "reduction_df = pd.DataFrame(index=wage_red_grid, columns=['pct'])\n", 151 | "for w,red in enumerate(wage_red_grid):\n", 152 | " offer_red = wage_base*(1-red/100)\n", 153 | " offer_value_r = npv_offer(offer_red, my_discount_rate, stocks, price_stocks)\n", 154 | " reduction_df.pct.loc[red] = offer_value_r\n", 155 | " \n", 156 | "fig, ax = plt.subplots(figsize=(10,6))\n", 157 | "ax.plot(reduction_df.index, reduction_df.pct, color='k')\n", 158 | "ax.plot([0,20],[base_value,base_value], color='r')\n", 159 | "ymin,ymax=ax.get_ylim()\n", 160 | "ax.axis([0,20,ymin,ymax])\n", 161 | "ax.set_title('Finding the Maximum Wage Reduction', fontsize=18)\n", 162 | "ax.set_ylabel('$', fontsize=14)\n", 163 | "ax.set_xlabel('% Wage Reduction', fontsize=16)\n", 164 | "base_value_str = '$'+str(np.around(base_value,decimals=0))[:3]\n", 165 | "ax.text(2.5,base_value,'Current Base = {0}'.format(base_value_str), verticalalignment='top', color='r', fontsize=14)\n", 166 | "ax.text(2.0,200,'Offers for given wage reduction', fontsize=14)\n", 167 | "# plot max wage reduction: find the intersection of both lines\n", 168 | "max_wage_reduction = np.abs(reduction_df.pct-base_value).astype('float').idxmin()\n", 169 | "ax.plot([max_wage_reduction, max_wage_reduction],[ymin, base_value], ls='--', color='k')\n", 170 | "max_wage_str = '{0}%'.format(np.around(max_wage_reduction,decimals=1))\n", 171 | "ax.text(max_wage_reduction,170,max_wage_str, horizontalalignment='right',\n", 172 | " fontsize=12)\n", 173 | "print('')" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "### You can perform similar analyses:\n", 181 | "\n", 182 | "1. Given current wage reduction, what is the minimum equity value I'm willing to accept\n", 183 | "2. How much is simplifying to 2 periods buying for us? How different would my decision be if I do the actual calculation?" 184 | ] 185 | } 186 | ], 187 | "metadata": { 188 | "kernelspec": { 189 | "display_name": "Python 3", 190 | "language": "python", 191 | "name": "python3" 192 | }, 193 | "language_info": { 194 | "codemirror_mode": { 195 | "name": "ipython", 196 | "version": 3 197 | }, 198 | "file_extension": ".py", 199 | "mimetype": "text/x-python", 200 | "name": "python", 201 | "nbconvert_exporter": "python", 202 | "pygments_lexer": "ipython3", 203 | "version": "3.7.3" 204 | } 205 | }, 206 | "nbformat": 4, 207 | "nbformat_minor": 2 208 | } 209 | -------------------------------------------------------------------------------- /jupyter_notebooks/chapter2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "%matplotlib inline\n", 13 | "\n", 14 | "from sklearn.linear_model import LinearRegression" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "### Simulating the effect of a confounder\n", 22 | "\n", 23 | "Idea:\n", 24 | "\n", 25 | "* $x$ and $y$ depend on a third variable $z$\n", 26 | "* When we plot $y$ as a function of $x$ _without controlling for z_ we get spurious correlation" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 13, 32 | "metadata": {}, 33 | "outputs": [ 34 | { 35 | "data": { 36 | "text/plain": [ 37 | "Text(0.5, 1.0, 'Example: Very Strong Correlation')" 38 | ] 39 | }, 40 | "execution_count": 13, 41 | "metadata": {}, 42 | "output_type": "execute_result" 43 | }, 44 | { 45 | "data": { 46 | "image/png": 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\n", 47 | "text/plain": [ 48 | "
" 49 | ] 50 | }, 51 | "metadata": {}, 52 | "output_type": "display_data" 53 | } 54 | ], 55 | "source": [ 56 | "# fix a seed for our random number generator and number of observations to simulate\n", 57 | "np.random.seed(422019)\n", 58 | "nobs = 1000\n", 59 | "# our third variable will be standard normal\n", 60 | "z = np.random.randn(nobs,1)\n", 61 | "# let's say that z --> x and z--> y\n", 62 | "# Notice that x and Y are not related!\n", 63 | "x = 0.5 + 0.4*z + 0.1*np.random.randn(nobs,1)\n", 64 | "y = 1.5 + 0.2*z + 0.01*np.random.randn(nobs,1)\n", 65 | "\n", 66 | "# ready to plot\n", 67 | "fig, ax = plt.subplots(figsize=(8,6))\n", 68 | "plt.scatter(x,y, s=8)\n", 69 | "plt.yticks([])\n", 70 | "plt.xticks([])\n", 71 | "plt.ylabel('$Y$ ', rotation=0, fontsize=18)\n", 72 | "plt.xlabel('$X$', rotation=0, fontsize=18)\n", 73 | "plt.title('Example: Very Strong Correlation', fontsize=18)" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "## Using the Frisch-Waugh-Lovell theorem in the Appendix we can get a _controlled_ view\n", 81 | "\n", 82 | "* Run regressions for $x$/$y$ on $z$ and save residuals\n", 83 | "* Plot residuals: these are net of the Z-effect" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 28, 89 | "metadata": {}, 90 | "outputs": [ 91 | { 92 | "name": "stdout", 93 | "output_type": "stream", 94 | "text": [ 95 | "Coefficient of Z on X equation = 0.399\n", 96 | "Coefficient of Z on Y equation = 0.2\n" 97 | ] 98 | }, 99 | { 100 | "data": { 101 | "text/plain": [ 102 | "Text(0.5, 1.0, 'Absence of Correlation: Controlled Version')" 103 | ] 104 | }, 105 | "execution_count": 28, 106 | "metadata": {}, 107 | "output_type": "execute_result" 108 | }, 109 | { 110 | "data": { 111 | "image/png": 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\n", 112 | "text/plain": [ 113 | "
" 114 | ] 115 | }, 116 | "metadata": { 117 | "needs_background": "light" 118 | }, 119 | "output_type": "display_data" 120 | } 121 | ], 122 | "source": [ 123 | "# Regress Y and X on Z and plot the corresponding residuls\n", 124 | "reg_x = LinearRegression(fit_intercept=True).fit(z, x)\n", 125 | "reg_y = LinearRegression(fit_intercept=True).fit(z, y)\n", 126 | "# just as a check, print the coefficients (compare with the real ones above)\n", 127 | "print('Coefficient of Z on X equation = {0}'.format(np.around(reg_x.coef_.flatten()[0],decimals=3)))\n", 128 | "print('Coefficient of Z on Y equation = {0}'.format(np.around(reg_y.coef_.flatten()[0],decimals=3)))\n", 129 | "# Compute residuals\n", 130 | "resids_x = x-reg_x.predict(z)\n", 131 | "resids_y = y-reg_y.predict(z)\n", 132 | "# plot: correlation is no longer there\n", 133 | "fig, ax = plt.subplots(figsize=(8,6))\n", 134 | "ax.scatter(resids_x, resids_y, s=8)\n", 135 | "ax.set_xlabel('Residuals of X on Z', fontsize=14)\n", 136 | "ax.set_ylabel('Residuals of Y on Z', fontsize=14)\n", 137 | "ax.set_title('Absence of Correlation: Controlled Version',fontsize=18)" 138 | ] 139 | } 140 | ], 141 | "metadata": { 142 | "kernelspec": { 143 | "display_name": "Python 3", 144 | "language": "python", 145 | "name": "python3" 146 | }, 147 | "language_info": { 148 | "codemirror_mode": { 149 | "name": "ipython", 150 | "version": 3 151 | }, 152 | "file_extension": ".py", 153 | "mimetype": "text/x-python", 154 | "name": "python", 155 | "nbconvert_exporter": "python", 156 | "pygments_lexer": "ipython3", 157 | "version": "3.7.3" 158 | } 159 | }, 160 | "nbformat": 4, 161 | "nbformat_minor": 2 162 | } 163 | --------------------------------------------------------------------------------