diff --git a/01_IntroductionToPython/01_Tutorial_PythonIntroduction.ipynb b/01_IntroductionToPython/01_Tutorial_PythonIntroduction.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..47eeaf116a07b29a69de37fe7fc795cf68a1f069
--- /dev/null
+++ b/01_IntroductionToPython/01_Tutorial_PythonIntroduction.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1a932130-baf8-499d-b2a2-16ec25b2b6b2",
+   "metadata": {},
+   "source": [
+    "# Tutorial 1: Introduction to Python\n",
+    "\n",
+    "In the first [exercise](https://git.rwth-aachen.de/introduction-to-robotics-course/introduction-to-robotics-2023/-/tree/main/01_IntroductionToPython) you got introduced to the fundamentals of `Python` (variables, data types, operators, decision structures, loops, functions and modules). In order to consolidate and extend the knowledge aquired, the following tutorial tasks should be solved / implemented.\n",
+    "\n",
+    "## Task 1.1\n",
+    "\n",
+    "The **forward_kinematics** function from the [forward_kinematics module](https://git.rwth-aachen.de/introduction-to-robotics-course/introduction-to-robotics-2023/-/blob/main/01_IntroductionToPython/forward_kinematics.py) is implemented for an manipulator of **n links** and **n+1 revolute joints** that starts from an **initial position** with all joint rotations $\\theta=0$.\n",
+    "\n",
+    "Implement a function named **fk_task_1** inside the [forward_kinematics module](https://git.rwth-aachen.de/introduction-to-robotics-course/introduction-to-robotics-2023/-/blob/main/01_IntroductionToPython/forward_kinematics.py) that ***returns*** a list which contains for each time step all joint coordinates $x_i$ and $y_i$ for a manipulator (**n links** and **n+1 revolute joints**) with an initial joint rotation that is described by the current rotations $\\theta_{i,0}$ and the desired incremental joint rotations at time t $\\Delta\\theta_{i,t}$ for joint i. The base link is rotating around the given point $[x_0 = 0, y_0 = 1]$. \n",
+    "\n",
+    "Test the implemented function inside this jupyter notebook in the following cells. Use the given structure to\n",
+    "\n",
+    "1. **Import** necessary modules.\n",
+    "2. **Define** the **parameters** needed to test the 'fk_task_1' function.\n",
+    "    - base link coordinates $x_0$ and $y_0$\n",
+    "    - link lengths $a_i=[1,1,1,1,1]$\n",
+    "    - initial joint roations $\\theta_{i,t=0}=[90,0,0,0,0]$ in degree\n",
+    "    - incremental change of joint rotation $\\Delta\\theta_{i,t=1} = [0,0,90,-30,45]$\n",
+    "3. **Call** the 'fk_task_1' **function** and save the return values under appropriate named variables.\n",
+    "4. **Create** a **plot** containing the initial manipulator position (red) and the position after incremental rotation (green).\n",
+    "\n",
+    "**Hint:** Use the the data type [array](https://numpy.org/doc/stable/reference/arrays.ndarray.html) from the [numpy package](https://numpy.org/doc/stable/) to define define the variables which contains sequence of values. That allows you to use arithmetic operators directly on the variables.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "59a9ad77-e04e-40ac-83d6-2e379901020f",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Use this cell to import the necessary modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "2e47f84e-0363-4a6c-a148-41a4ef2614ea",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Use this cell to define the gemoetry and rotation parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "22db17d3-491b-480d-87ac-eff27acb3712",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Use this cell to call the function 'fk_task_1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "c12897f2-b5d4-4f8c-9d22-0f9511780a23",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use this cell to plot the initial maniplator position and the position after incremental rotation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b0921a2-a094-429c-84f6-405e62f53c6b",
+   "metadata": {},
+   "source": [
+    "## Task 1.2\n",
+    "\n",
+    "After implementing the **fk_task_1** function and teststing it for one time step, respectiveley one increment, test the function now for several time steps. The goal is now to reach the rotation position $\\theta_i$ from **Task 1.1** in 5 evenly spaced steps and generate one plot which contains the resulting configuration of the arm manipulator for all time steps. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9dfed8f7",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculation of the incremental rotation steps to reach the final rotation position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1716ad7e",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Use this cell to call the function 'fk_task_1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "c698d670-1f83-4d34-9b96-4b1662878030",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot here one plot which contains for each time step the corresponding arm manipulator position "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": ""
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}