diff --git a/module-1/lab-resolving-git-conflicts/your-code/about-me.md b/module-1/lab-resolving-git-conflicts/your-code/about-me.md index 30a999d50..9243ab822 100644 --- a/module-1/lab-resolving-git-conflicts/your-code/about-me.md +++ b/module-1/lab-resolving-git-conflicts/your-code/about-me.md @@ -1,7 +1,10 @@ -Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque viverra laoreet lorem et dapibus. Integer auctor dignissim egestas. Ut id purus neque. Pellentesque imperdiet lacus in libero laoreet, at tempus felis tristique. Cras fermentum erat a dui vulputate gravida. Nulla aliquet nisi interdum nulla pretium, ac vestibulum diam congue. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Phasellus lacus risus, sodales vitae viverra quis, maximus ac ipsum. Sed consequat viverra mattis. Curabitur iaculis varius mollis. -Ut porttitor iaculis tellus bibendum euismod. Morbi porta, ante nec tempus porta, felis mi faucibus lacus, sed tristique purus nunc sed est. Aenean pulvinar urna ut lacus interdum aliquam. Pellentesque sit amet magna accumsan, sagittis metus a, volutpat velit. Mauris vitae ex vehicula, posuere nisi sed, sagittis nunc. Ut scelerisque, mi non tristique tristique, mi enim luctus nunc, eu mattis sem quam auctor nunc. Donec lobortis tellus eget blandit ultricies. Vivamus euismod metus eget leo blandit, at malesuada magna efficitur. Praesent sodales faucibus mi, ullamcorper ultrices orci. Vivamus maximus malesuada massa, nec placerat leo feugiat vel. Nam vitae eleifend enim. Nullam interdum ipsum velit, vitae faucibus lectus blandit euismod. +I am Meliné Minassian. I grew up in London and am originally from Armenia. +I used to work for QVC as a senior planner. +I have never worked with technology before and have only done fairly basic data analysis in my job. -Suspendisse ut malesuada ex. Nulla ultricies nisl et nisi rhoncus sollicitudin. Vestibulum maximus iaculis ligula, nec commodo nunc ullamcorper nec. Duis quis condimentum sapien. Cras vestibulum interdum felis eu auctor. Quisque semper, magna at dapibus faucibus, felis risus semper ligula, id aliquam lectus ligula vel nisi. In hac habitasse platea dictumst. Donec arcu sapien, suscipit ac dictum et, imperdiet id tortor. Maecenas ornare sodales interdum. Mauris dictum felis eu eros vestibulum cursus. Phasellus accumsan, turpis ut malesuada sollicitudin, augue leo venenatis ante, vel convallis tellus diam sit amet lacus. Aenean eu mauris eros. Praesent ante lacus, gravida sit amet tellus nec, laoreet ultrices lacus. Integer commodo semper vestibulum. Fusce felis massa, consectetur facilisis rutrum nec, pulvinar et nisi. -Morbi fermentum ultricies tortor, vehicula ultrices eros elementum a. Duis ornare aliquam facilisis. Proin aliquam tincidunt odio vitae dignissim. Sed malesuada lacinia massa, nec blandit urna auctor elementum. Duis auctor non tortor in consequat. Mauris id vestibulum risus. In eget erat sed lacus efficitur viverra sed eu est. Aliquam interdum consequat molestie. Aliquam metus nisi, blandit non semper ut, blandit vel leo. Cras dictum turpis erat, sed iaculis ligula facilisis dapibus. Aliquam posuere dignissim fermentum. Praesent at neque sit amet lectus ornare iaculis. Curabitur id urna quis lorem varius ultrices eu sit amet sapien. Curabitur maximus volutpat suscipit. Proin imperdiet elementum lacus a eleifend. Sed tempor lacus posuere diam vehicula iaculis. +I decided to come here because I wanted a career change. +I expect to learn programming and statistics. + +I am still thinking about which industry I'd like to join but I'd rather not go into retail. diff --git a/module-2/LAB-understanding-descriptives/ages_population.csv b/module-2/LAB-understanding-descriptives/ages_population.csv new file mode 100644 index 000000000..c3cb2bbf6 --- /dev/null +++ b/module-2/LAB-understanding-descriptives/ages_population.csv @@ -0,0 +1,1001 @@ +observation +68 +12 +45 +38 +49 +27 +39 +12 +42 +33 +30 +25 +30 +44 +53 +46 +50 +22 +6 +29 +29 +27 +35 +38 +28 +26 +60 +41 +38 +41 +44 +52 +46 +39 +44 +46 +32 +23 +15 +40 +42 +32 +45 +29 +22 +41 +39 +63 +39 +31 +34 +28 +45 +33 +32 +61 +64 +37 +56 +44 +33 +38 +40 +38 +56 +14 +52 +34 +14 +34 +31 +46 +50 +37 +13 +12 +25 +28 +51 +13 +36 +52 +13 +30 +36 +35 +26 +34 +51 +52 +35 +44 +23 +29 +25 +30 +27 +42 +18 +39 +42 +48 +30 +40 +34 +28 +48 +48 +42 +53 +34 +37 +32 +29 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Save the information in a dataframe.\n", + "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", + "\n", + "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", + "\n", + "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 5\n", + "Now is the turn of `ages_population3.csv`.\n", + "\n", + "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus challenge\n", + "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/module-2/LAB-understanding-descriptives/roll_the_dice_hundred.csv 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+978,978,2 +979,979,2 +980,980,2 +981,981,5 +982,982,3 +983,983,4 +984,984,4 +985,985,4 +986,986,4 +987,987,1 +988,988,2 +989,989,6 +990,990,4 +991,991,6 +992,992,4 +993,993,1 +994,994,3 +995,995,1 +996,996,4 +997,997,4 +998,998,3 +999,999,6 \ No newline at end of file diff --git a/module-2/lab-intro-to-proba.ipynb b/module-2/lab-intro-to-proba.ipynb new file mode 100644 index 000000000..53f36fb6b --- /dev/null +++ b/module-2/lab-intro-to-proba.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction To Probability\n", + "## Challenge 1\n", + "\n", + "A and B are events of a probability space with $(\\omega, \\sigma, P)$ such that $P(A) = 0.3$, $P(B) = 0.6$ and $P(A \\cap B) = 0.1$\n", + "\n", + "Which of the following statements are false?\n", + "* $P(A \\cup B) = 0.6$\n", + "* $P(A \\cap B^{C}) = 0.2$\n", + "* $P(A \\cap (B \\cup B^{C})) = 0.4$\n", + "* $P(A^{C} \\cap B^{C}) = 0.3$\n", + "* $P((A \\cap B)^{C}) = 0.9$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "There is a box with 10 white balls, 12 red balls and 8 black balls. Calculate the probability of:\n", + "* Taking a white ball out.\n", + "* Taking a white ball out after taking a black ball out.\n", + "* Taking a red ball out after taking a black and a red ball out.\n", + "* Taking a red ball out after taking a black and a red ball out with reposition.\n", + "\n", + "**Hint**: Reposition means putting back the ball into the box after taking it out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "\n", + "You are planning to go on a picnic today but the morning is cloudy. You hate rain so you don't know whether to go out or stay home! To help you make a decision, you gather the following data about rainy days:\n", + "\n", + "* 50% of all rainy days start off cloudy!\n", + "* Cloudy mornings are common. About 40% of days start cloudy. \n", + "* This month is usually dry so only 3 of 30 days (10%) tend to be rainy. \n", + "\n", + "What is the chance of rain during the day?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "\n", + "One thousand people were asked through a telephone survey whether they thought more street lighting is needed at night or not.\n", + "\n", + "Out of the 480 men that answered the survey, 324 said yes and 156 said no. On the other hand, out of the 520 women that answered, 351 said yes and 169 said no. \n", + "\n", + "We wonder if men and women have a different opinions about the street lighting matter. Is gender relevant or irrelevant to the question?\n", + "\n", + "Consider the following events:\n", + "- The answer is yes, so the person that answered thinks that more street lighting is needed.\n", + "- The person who answered is a man.\n", + "\n", + "We want to know if these events are independent, that is, if the fact of wanting more light depends on whether one is male or female. Are these events independent or not?\n", + "\n", + "**Hint**: To clearly compare the answers by gender, it is best to place the data in a table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/module-2/lab-intro-to-probability/lab-intro-to-proba.ipynb b/module-2/lab-intro-to-probability/lab-intro-to-proba.ipynb new file mode 100644 index 000000000..53f36fb6b --- /dev/null +++ b/module-2/lab-intro-to-probability/lab-intro-to-proba.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction To Probability\n", + "## Challenge 1\n", + "\n", + "A and B are events of a probability space with $(\\omega, \\sigma, P)$ such that $P(A) = 0.3$, $P(B) = 0.6$ and $P(A \\cap B) = 0.1$\n", + "\n", + "Which of the following statements are false?\n", + "* $P(A \\cup B) = 0.6$\n", + "* $P(A \\cap B^{C}) = 0.2$\n", + "* $P(A \\cap (B \\cup B^{C})) = 0.4$\n", + "* $P(A^{C} \\cap B^{C}) = 0.3$\n", + "* $P((A \\cap B)^{C}) = 0.9$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "There is a box with 10 white balls, 12 red balls and 8 black balls. Calculate the probability of:\n", + "* Taking a white ball out.\n", + "* Taking a white ball out after taking a black ball out.\n", + "* Taking a red ball out after taking a black and a red ball out.\n", + "* Taking a red ball out after taking a black and a red ball out with reposition.\n", + "\n", + "**Hint**: Reposition means putting back the ball into the box after taking it out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "\n", + "You are planning to go on a picnic today but the morning is cloudy. You hate rain so you don't know whether to go out or stay home! To help you make a decision, you gather the following data about rainy days:\n", + "\n", + "* 50% of all rainy days start off cloudy!\n", + "* Cloudy mornings are common. About 40% of days start cloudy. \n", + "* This month is usually dry so only 3 of 30 days (10%) tend to be rainy. \n", + "\n", + "What is the chance of rain during the day?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "\n", + "One thousand people were asked through a telephone survey whether they thought more street lighting is needed at night or not.\n", + "\n", + "Out of the 480 men that answered the survey, 324 said yes and 156 said no. On the other hand, out of the 520 women that answered, 351 said yes and 169 said no. \n", + "\n", + "We wonder if men and women have a different opinions about the street lighting matter. Is gender relevant or irrelevant to the question?\n", + "\n", + "Consider the following events:\n", + "- The answer is yes, so the person that answered thinks that more street lighting is needed.\n", + "- The person who answered is a man.\n", + "\n", + "We want to know if these events are independent, that is, if the fact of wanting more light depends on whether one is male or female. Are these events independent or not?\n", + "\n", + "**Hint**: To clearly compare the answers by gender, it is best to place the data in a table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your solution here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/module-2/statistical-analysis-project/README.md b/module-2/statistical-analysis-project/README.md index 2213bf684..1e4dc8e96 100644 --- a/module-2/statistical-analysis-project/README.md +++ b/module-2/statistical-analysis-project/README.md @@ -1,66 +1,187 @@ -![IronHack Logo](https://s3-eu-west-1.amazonaws.com/ih-materials/uploads/upload_d5c5793015fec3be28a63c4fa3dd4d55.png) - -# Project: Statistical Analysis - -## Overview - -The goal of this project is for you to practice statistical analysis using the iterative data analysis process. For this project, you will use this [Housing Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data) dataset we choose for you. You need to download the `train.csv` dataset then use your statistical analysis skills to analyze this dataset. **The goal of your analysis is to identify the most important features of houses that affect the sale prices.** - -**You will be working individually for this project**, but we'll be guiding you along the process and helping you as you go. - ---- - -## Technical Requirements - -The technical requirements for this project are as follows: - -* Try to apply everything you have learned so far about data analysis (in creative ways if you can) such as data cleaning, data manipulation, data visualization, and various statistical analysis methods. - -* Apply the iterative data analysis process -- setting expectations, collecting information, and reacting to data / revising expectations. - -* Conduct your analysis in Jupyter Notebook using Pandas, Numpy, Scipy, Matplotlib, Seaborn, Plotly, and other Python libraries you have learned, as necessary. - -## Necessary Deliverables - -The following deliverables should be pushed to your Github repo for this project. - -* **A Jupyter Notebook (statistical-analysis.ipynb)** containing your Python codes, outputs, and data visualizations. Make sure to include explanations for each of your steps in Markdown cells or Python comments. - -* [optional] A `README.md` file containing any additional information. - -## Suggested Ways to Get Started - -1. Explore data and understand what the fields mean. - -1. Examine the relationships between the sales price and other features in the dataset. Use data visualization techniques to help you gain intuitive understanding of the relationships. - -1. Make informed guess on which features should be investigated in depth. - -1. Data cleaning & manipulation. Apply the following techniques as appropriate: - * Adjust skewed data distribution. - * Remove columns with high proportion of missing values. - * Remove records with missing values. - * Feature reduction. - * Convert categorical data to numerical. - -1. Compute field relationship scores with the chosen statistical model. - -1. Present your findings in statistical summary and/or data visualizations. - -## Project Feedback + Evaluation - -* __Technical Requirements__: Did you deliver a project that met all the technical requirements? Given what the class has covered so far, did you build something that was reasonably complex? - -* __Creativity__: Did you add a personal spin or creative element into your project submission? Did you incorporate domain knowledge or unique perspective into your analysis. - -* __Code Quality__: Did you follow code style guidance and best practices covered in class? - -* __Total__: Your instructors will give you a total score on your project between: - - **Score**|**Expectations** - -----|----- - 0|Does not meet expectations - 1|Meets expectactions, good job! - 2|Exceeds expectations, you wonderful creature, you! - -This will be useful as an overall gauge of whether you met the project goals, but __the more important scores are described in the specs above__, which can help you identify where to focus your efforts for the next project! +Ironhack Logo + + + +# Welcome to the Data Guru Project! + +In this project you will get all the data and its insights by yourself! :scream: + + + +## Content +- [Project Description](#project-description) +- [Project Goals](#project-goals) +- [Requirements](#requirements) +- [Deliverables](#deliverables) +- [Mentoring](#mentoring) +- [Schedule](#schedule) +- [Presentation](#presentation) +- [Tips & Tricks](#tips-&-tricks) +- [Resources](#resources) + + + + + + +## Project Description +In this project, you will: +1. Select the topic to analyze. The topic shoud be related to your interests - kitesurfing, stock prices, human poverty, wine consumption. Whatever touches your heart. +2. Collect your data - find it among thousands of Databases available online, collect using API or be a thief and scrape some websites. **Two data sources are required!**. +3. Organize your data. Clean your data within the first 2 days - you won't have any time later on. +4. Visualize your data. Remember, 1 proper plot worth of 5 tables. +5. Calculate important statistics, describe your data. Start exploring the hidden knowledge of your data! +6. Prepare a model. Forecast or classify - your choice. Model should be tested and validated. Try to analyze your data from different angles and you will find very interesting insights, believe me! +7. Present your project. Code should be organized and structured. Presentation should include all the steps you have done. + + + + + +## Project Goals +* Learn how to develop an interesting question and find the data to answer it. +* Learn how to obtain data from different sources, including APIs, open source datasets and/or scrape data from the web. +* Build a database from the data you find for the whole team to use. +* Explain more complex arguments with plots. +* Learn the importance of statistical analysis. +* Prepare your first real data model that describes the reality we live no matter how happy or sad it is. + + + + +## Requirements +* You must plan your project. That is why creating a Kanban or Trello Board is mandatory. You have a template for Trello [here](https://trello.com/b/VZkmCy2O/data-guru-project). +* You **CAN'T CODE** until you project is planned. +* Your project must include data from at least 2 different data sources (APIs & web, dataset & APIs, ...) +* You must prepare at least 3 models during analysis and present the results during presentation. The best model should be used for final result calculation. + + + + +## Deliverables +You are required to turn in the following: +1. Link to your repository with your code and README. +2. Access information to your database in Google Cloud with a description of each table and how they relate. +3. Links to the data you are using (sources) and your organization (trello). +4. Slides for your presentation. + + + + +## Mentoring +One of the Teaching Team will be your mentor! +Your mentor will: +* Follow your project in general, will be the second person that knows more about the project, after you. +* Check if you are following the tasks, your blockers, etc. +* Help/support you in specific questions. + +Your mentor is not meant to: +* Know everything. +* Prepare code for you. If Eldiias noticed cheating, you **will be fined and will not get any help from Teaching Team for 1 day.** +* Be your manager. You have to be the responsible person to do the tasks! + +If your mentor doesn't have enough knowledge/experience in the topic you chose to provide sufficient help, you have a right to discuss it with Eldiias to find a solution. If the complain has no basis, team is fined for 1 day - no help from Teaching Team. + + + + +## Schedule +*Friday* +* Look for an interesting topic and make some hypothesis or think about some questions to answer about it. +* Investigate which data sources are available for that topic. +* Reach some best practices agreements as a team. +* Plan your project and organize. Think some risks you can have. +* Start working on your database. + + +*Monday* +* Be ready for the topic and ideas validation with the Lead Teacher and TAs. + + +*Wednesday* +* Be ready for the database validation. Data should be clean and tidy by this date. +* Start working on your analysis and plots. Think about the plots you want to create and the structure of your paper. + + +*Friday* +* All the visualizations and descriptives should be calculated. +* Most of the variables should be defined already. Think of deriving some indices and additional parameters like 'day of the week' and etc. + + +*Wednesday* +* At least 1 working model should be built already. +* Start working on presentation + + +*Thursday* +* Finish your analysis and presentation. + + +*Friday* +* Presentation! + + + + +## Presentation +You will have 20 minutes to present your project. The below are some ideas for slides you could include in your presentation; those marked with an **(M)** are mandatory! + + +* **(M)** Title of the project +* **(M)** Your topic. Why did you choose it? +* **(M)** Presentation of the team +* Main challenges & strengths +* **(M)** Team. Did you follow your workflow plan? Did you add something after starting the project? Did you follow your best practices agreements? Did you think about the risk management? +* About your data: useful sources, incomplete data, data that would have been great to have, etc. +* Data cleaning: how and why you cleaned your data the way you did. +* **(M)** Data Viz. 1 slide per plot! +* Data Viz: Prepare insights for every plot. Plots should have a soul. +* **(M)** Stats and Models: Every analysis has a story. Explain what you wanted to obtain by doing this analysis and what were the results. +* **(M)** Models: one slide per model. Explain what data your model depends on and what are the outcomes. +* Hypotheses testing and Assumptions for every model. +* **(M)** Main insights: one slide per insight! +* Questions you couldn't answer. +* Something funny that happened during the project. +* Things you learned during this project. +* If you could start from scratch, what would you do differently? + + + + +## Tips & Tricks +* First, choose your topic and look for sources available. +* Before you start coding and integrate more data, propose some interesting questions you could answer with the data you have. + + + + +## Resources +### Lists +[AnyAPI](https://any-api.com/) +[Top 50 Most Popular APIs on RapidAPI](https://blog.rapidapi.com/most-popular-apis/) +[18 Fun APIs For Your Next Project](https://medium.com/@vicbergquist/18-fun-apis-for-your-next-project-8008841c7be9) + +### Some Ideas +Slack your alumnis! They can share a lot of useful insights on what to do! +[WeatherBit](https://www.weatherbit.io/api) +[Strava](https://developers.strava.com/docs/reference/) +[GitHub](https://developer.github.com/v3/) +[Twitter](https://developer.twitter.com/en/docs.html) +[LastFM](https://www.last.fm/api) +[Spotify](https://developer.spotify.com/documentation/web-api/reference/) +[NYTimes](https://web.archive.org/web/20150325135221/http://developer.nytimes.com/docs/times_newswire_api/) +[News](https://newsapi.org/docs) +[Reddit](https://github.com/reddit-archive/reddit/wiki/API) +[Medium](https://github.com/Medium/medium-api-docs) +[Twitch](https://dev.twitch.tv/docs/api/reference) +[IGDB](https://api-docs.igdb.com/) +[OMDB](http://www.omdbapi.com/) +[GIPHY](https://developers.giphy.com/docs/) +[StackExchange](https://api.stackexchange.com/docs) +[YouTube](https://developers.google.com/youtube/v3/docs/) +[TheSportsDB](https://github.com/enen92/script.module.thesportsdb) +[NBA API](https://pypi.org/project/nba-api/) + +### Paper Examples +[Data Analysis with Python](https://medium.com/@williamkoehrsen/data-analysis-with-python-19434f5d6324) +[The Best Mario Kart Character According To Data Science](https://medium.com/civis-analytics/the-best-mario-kart-character-according-to-data-science-7dfb65d4c18e) \ No newline at end of file