diff --git a/module2/exo1/Tutorial.ipynb b/module2/exo1/Tutorial.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7fec51502cbc3200b3d0ffc6bbba1fe85e197f3d
--- /dev/null
+++ b/module2/exo1/Tutorial.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module2/exo1/Untitled.ipynb b/module2/exo1/Untitled.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e578fa7a2ce3f2f9cf1d27ffb6252da4a15d6e68
--- /dev/null
+++ b/module2/exo1/Untitled.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " 2+2  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'pritn' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-faa952c1b3ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpritn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'pritn' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "x= 18\n",
+    "pritn(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=18\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = x + 10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Petit exemple de completion "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utilisation d'autres langages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "UsageError: Cell magic `%%R` not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "plot(cars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}