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update part 3
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lessons/Part3/13_Numpy.ipynb

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{
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"cell_type": "code",
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"execution_count": 77,
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"metadata": {},
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"source": [
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"np.array(converted_df)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"## Challenge: operation in numpy to manipulate array with a couple of different functions \n",
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"\n",
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"## 1) indexing\n",
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"##) reshaping\n",
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"\n",
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"## argsort/argwhere for arrays\n",
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"\n",
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"\n",
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"## initialize arrays and fill in values (preallocation)"
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]
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}
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],
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"metadata": {

lessons/Part3/14_Statsmodels.ipynb

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"\n",
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"Choose one of the following options: \n",
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"\n",
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"1. Use a t-test or linear regression on another combination of variables (e.g. predict flipper_length)\n",
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"1. Use a t-test or linear regression on another combination of variables (e.g. predict flipper_length) \n",
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"2. Pairwise ttest / logistic regression / wilcoxan test? \n",
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"\n",
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"2. From the [documentation](https://www.statsmodels.org/dev/api.html) choose another model or test (consider those you might use in your work) and apply it to the penguins dataset. \n",
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"\n",

lessons/Part4/017_Project.ipynb renamed to lessons/Part4/17_Project.ipynb

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"source": [
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"## Introducing the Dataset\n",
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"\n",
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"- Find a bunch of text files\n"
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"- airline tweet dataset\n",
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"\n"
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]
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},
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{
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"## Import data:\n",
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"\n",
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"Skills to include: \n",
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"- import multiple files in a directory, (with some simple parsing funtions)\n",
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"- import multiple files in a directory, (with some simple parsing funtions) (for-loop, if statement to filter for the relevant files)\n",
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"- combine them into a single dataframe (pd.concat)\n",
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"- write a function called parse_files\n"
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"- combine into parse files function\n"
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]
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{
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"Skills:\n",
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"- Subsetting a dataframe\n",
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"- Modifying a column\n",
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"- "
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"- numpy \n",
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"- feature engineering \n",
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"- introduce a new package (string processing)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## One statistical model \n",
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"your choice\n",
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"\n",
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"linear regression on retweets? \n",
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"\n",
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"\n"
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]
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},
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"metadata": {},
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"source": [
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"## One visualization\n",
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"your choice"
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"\n",
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"date time? "
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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