diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..13566b8
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/.idea/Data-Science-For-Beginners.iml b/.idea/Data-Science-For-Beginners.iml
new file mode 100644
index 0000000..3cb6b3b
--- /dev/null
+++ b/.idea/Data-Science-For-Beginners.iml
@@ -0,0 +1,11 @@
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
new file mode 100644
index 0000000..03d9549
--- /dev/null
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/jpa-buddy.xml b/.idea/jpa-buddy.xml
new file mode 100644
index 0000000..966d5f5
--- /dev/null
+++ b/.idea/jpa-buddy.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..e64d47e
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..ad56d8e
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/1-Introduction/04-stats-and-probability/assignment.ipynb b/1-Introduction/04-stats-and-probability/assignment.ipynb
index a6f8147..32f97a5 100644
--- a/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,9 +3,9 @@
{
"cell_type": "markdown",
"source": [
- "## Introduction to Probability and Statistics\r\n",
- "## Assignment\r\n",
- "\r\n",
+ "## Introduction to Probability and Statistics\n",
+ "## Assignment\n",
+ "\n",
"In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
],
"metadata": {}
@@ -14,10 +14,10 @@
"cell_type": "code",
"execution_count": 13,
"source": [
- "import pandas as pd\r\n",
- "import numpy as np\r\n",
- "\r\n",
- "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
"df.head()"
],
"outputs": [
@@ -149,16 +149,16 @@
{
"cell_type": "markdown",
"source": [
- "\r\n",
- "In this dataset, columns as the following:\r\n",
- "* Age and sex are self-explanatory\r\n",
- "* BMI is body mass index\r\n",
- "* BP is average blood pressure\r\n",
- "* S1 through S6 are different blood measurements\r\n",
- "* Y is the qualitative measure of disease progression over one year\r\n",
- "\r\n",
- "Let's study this dataset using methods of probability and statistics.\r\n",
- "\r\n",
+ "\n",
+ "In this dataset, columns as the following:\n",
+ "* Age and sex are self-explanatory\n",
+ "* BMI is body mass index\n",
+ "* BP is average blood pressure\n",
+ "* S1 through S6 are different blood measurements\n",
+ "* Y is the qualitative measure of disease progression over one year\n",
+ "\n",
+ "Let's study this dataset using methods of probability and statistics.\n",
+ "\n",
"### Task 1: Compute mean values and variance for all values"
],
"metadata": {}
@@ -201,8 +201,8 @@
{
"cell_type": "markdown",
"source": [
- "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
- "\r\n",
+ "### Task 4: Test the correlation between different variables and disease progression (Y)\n",
+ "\n",
"> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
],
"metadata": {}
@@ -249,4 +249,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/1-Introduction/04-stats-and-probability/notebook.ipynb b/1-Introduction/04-stats-and-probability/notebook.ipynb
index 208eee5..5d74eb9 100644
--- a/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
@@ -30,16 +30,16 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 118,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
- "Mean = 5.433333333333334\n",
- "Variance = 10.178888888888887\n"
+ "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+ "Mean = 4.266666666666667\n",
+ "Variance = 8.195555555555556\n"
]
}
],
@@ -59,19 +59,17 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
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+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -91,168 +89,22 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 120,
"metadata": {},
"outputs": [
{
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Name | \n",
- " Team | \n",
- " Role | \n",
- " Height | \n",
- " Weight | \n",
- " Age | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 0 | \n",
- " Adam_Donachie | \n",
- " BAL | \n",
- " Catcher | \n",
- " 74 | \n",
- " 180.0 | \n",
- " 22.99 | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " Paul_Bako | \n",
- " BAL | \n",
- " Catcher | \n",
- " 74 | \n",
- " 215.0 | \n",
- " 34.69 | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " Ramon_Hernandez | \n",
- " BAL | \n",
- " Catcher | \n",
- " 72 | \n",
- " 210.0 | \n",
- " 30.78 | \n",
- "
\n",
- " \n",
- " 3 | \n",
- " Kevin_Millar | \n",
- " BAL | \n",
- " First_Baseman | \n",
- " 72 | \n",
- " 210.0 | \n",
- " 35.43 | \n",
- "
\n",
- " \n",
- " 4 | \n",
- " Chris_Gomez | \n",
- " BAL | \n",
- " First_Baseman | \n",
- " 73 | \n",
- " 188.0 | \n",
- " 35.71 | \n",
- "
\n",
- " \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " 1029 | \n",
- " Brad_Thompson | \n",
- " STL | \n",
- " Relief_Pitcher | \n",
- " 73 | \n",
- " 190.0 | \n",
- " 25.08 | \n",
- "
\n",
- " \n",
- " 1030 | \n",
- " Tyler_Johnson | \n",
- " STL | \n",
- " Relief_Pitcher | \n",
- " 74 | \n",
- " 180.0 | \n",
- " 25.73 | \n",
- "
\n",
- " \n",
- " 1031 | \n",
- " Chris_Narveson | \n",
- " STL | \n",
- " Relief_Pitcher | \n",
- " 75 | \n",
- " 205.0 | \n",
- " 25.19 | \n",
- "
\n",
- " \n",
- " 1032 | \n",
- " Randy_Keisler | \n",
- " STL | \n",
- " Relief_Pitcher | \n",
- " 75 | \n",
- " 190.0 | \n",
- " 31.01 | \n",
- "
\n",
- " \n",
- " 1033 | \n",
- " Josh_Kinney | \n",
- " STL | \n",
- " Relief_Pitcher | \n",
- " 73 | \n",
- " 195.0 | \n",
- " 27.92 | \n",
- "
\n",
- " \n",
- "
\n",
- "
1034 rows × 6 columns
\n",
- "
"
- ],
- "text/plain": [
- " Name Team Role Height Weight Age\n",
- "0 Adam_Donachie BAL Catcher 74 180.0 22.99\n",
- "1 Paul_Bako BAL Catcher 74 215.0 34.69\n",
- "2 Ramon_Hernandez BAL Catcher 72 210.0 30.78\n",
- "3 Kevin_Millar BAL First_Baseman 72 210.0 35.43\n",
- "4 Chris_Gomez BAL First_Baseman 73 188.0 35.71\n",
- "... ... ... ... ... ... ...\n",
- "1029 Brad_Thompson STL Relief_Pitcher 73 190.0 25.08\n",
- "1030 Tyler_Johnson STL Relief_Pitcher 74 180.0 25.73\n",
- "1031 Chris_Narveson STL Relief_Pitcher 75 205.0 25.19\n",
- "1032 Randy_Keisler STL Relief_Pitcher 75 190.0 31.01\n",
- "1033 Josh_Kinney STL Relief_Pitcher 73 195.0 27.92\n",
- "\n",
- "[1034 rows x 6 columns]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Empty DataFrame\n",
+ "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+ "Index: []\n"
+ ]
}
],
"source": [
- "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
- "df"
+ "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+ "df\n"
]
},
{
@@ -266,19 +118,19 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Age 28.736712\n",
- "Height 73.697292\n",
- "Weight 201.689255\n",
+ "Height 201.726306\n",
+ "Weight 73.697292\n",
"dtype: float64"
]
},
- "execution_count": 5,
+ "execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
@@ -296,14 +148,14 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 122,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+ "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
]
}
],
@@ -313,16 +165,16 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Mean = 73.6972920696325\n",
- "Variance = 5.316798081118074\n",
- "Standard Deviation = 2.3058183105175645\n"
+ "Mean = 201.72630560928434\n",
+ "Variance = 441.6355706557866\n",
+ "Standard Deviation = 21.01512718628623\n"
]
}
],
@@ -342,19 +194,17 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
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