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-# UE5 Fundamentals of Algorithms
-### Bachelor of Science in Data Science for Responsible Business
-
-Instructor: [Romain Vuillemot](romain.vuillemot@ec-lyon.fr)
-
-<div style="text-align: center;">
- <img src="../figures/logo-ecl.png" width="40%" style="width:20%; display:inline-block; vertical-align:middle;">
- <img src="../figures/logo-emlyon.png" width="10%" style="width:20%; display:inline-block; vertical-align:middle;">
-</div>
-
-## Assignments
diff --git a/assignments/assignment-01.ipynb b/assignments/assignment-01.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a1d61493",
+ "metadata": {},
+ "source": [
+ "NAME:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db477b79",
+ "metadata": {},
+ "source": [
+ "<center>\n",
+ " <h3>ASSIGNMENT#1 (UE5 Algorithms)</h3>\n",
+ "</center>\n",
+ "\n",
+ "Due date: **November 8th, 2024, 5pm** (no extension will be allowed).\n",
+ "\n",
+ "- Submit your solution by email: [romain.vuillemot@ec-lyon.fr](romain.vuillemot@ec-lyon.fr)\n",
+ "\n",
+ "- You have to work alone for this assignement.\n",
+ "\n",
+ "## Goal of this assignment\n",
+ "\n",
+ "In this assignment we provide you with a dataset of characters from a movie. Your role will be to answer the questions below programmatically, using Pyhon. **Please note you need to answer with fully working python code embedded in this notebook as solution (no external modules or files can be included).** You may then replace the code below with your answer for each question:\n",
+ "\n",
+ "```python\n",
+ "# YOUR CODE HERE\n",
+ "raise NotImplementedError()\n",
+ "```\n",
+ "\n",
+ "Do not forget to also provide 1) examples of use of the solution, and 2) serveral tests (using `assert`).\n",
+ "\n",
+ "## Grading\n",
+ "\n",
+ "- 30% for the results to the questions\n",
+ "- 60% for the code quality\n",
+ "- 10% for the notebook presentation\n",
+ "- +10% bonus question\n",
+ "\n",
+ "## Getting started\n",
+ "\n",
+ "We provide you with a dataset containing movie characters. Each character is represented as a row, along with connections to other characters, based on the movie script. You will use those connections to create a graph-based data structure and answer the questions. \n",
+ "\n",
+ "To get you started with the dataset, we provide you with the code that loads it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cf409997-537f-4f89-a039-c2b3494972f2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "with open('users.csv', 'r') as file:\n",
+ " lines = file.readlines()\n",
+ " data = [tuple(line.strip().split(',')) for line in lines[1:]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c48b6391-d858-4a35-b428-b99434ee2d57",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "The `data` variable contains the list of characters. You may look at the `users.csv` file to grasp the values of this variable. Here is a sample:\n",
+ "\n",
+ "```\n",
+ "[('Tony_Stark',\n",
+ " '40',\n",
+ " 'Male',\n",
+ " '1.85',\n",
+ " 'Steve_Rogers Natasha_Romanoff Bruce_Banner Thor_Odinson'),\n",
+ " ('Steve_Rogers',\n",
+ " '98',\n",
+ " 'Male',\n",
+ " '1.88',\n",
+ " 'Tony_Stark Natasha_Romanoff Sam_Wilson Bucky_Barnes'),\n",
+ "...\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "70e93e78-f4dd-435f-b0f7-d56d928d7051",
+ "metadata": {},
+ "source": [
+ "**Question 1 -** How many characters are there in the dataset? Are there any duplicate?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9022b450-6fa6-41d0-999e-e019917cda79",
+ "metadata": {
+ "deletable": false,
+ "nbgrader": {
+ "cell_type": "code",
+ "checksum": "6474e3afbe1bbdb5b06ce64fc0534c4f",
+ "grade": false,
+ "grade_id": "cell-f45150625899eb53",
+ "locked": false,
+ "schema_version": 3,
+ "solution": true,
+ "task": false
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# YOUR CODE HERE\n",
+ "raise NotImplementedError()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a1a4c6c-f95c-4dc3-8bb3-863d1048ae79",
+ "metadata": {},
+ "source": [
+ "**Question 2 -** Write a function that turns the `data` variable into a dictionnary data structure like the one below:\n",
+ "\n",
+ "```\n",
+ "{'Tony_Stark': ['Steve_Rogers', 'Natasha_Romanoff', 'Bruce_Banner', 'Thor_Odinson'], 'Steve_Rogers': ['Tony_Stark', 'Natasha_Romanoff', 'Sam_Wilson', 'Bucky_Barnes'], ..\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6cd4d69c-f7fb-4661-a737-42bea97431c7",
+ "metadata": {
+ "deletable": false,
+ "nbgrader": {
+ "cell_type": "code",
+ "checksum": "e414bc5c79fc378ab2466fc5df186739",
+ "grade": false,
+ "grade_id": "cell-b624a3dccb825990",
+ "locked": false,
+ "schema_version": 3,
+ "solution": true,
+ "task": false
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# YOUR CODE HERE\n",
+ "raise NotImplementedError()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a25a532d-6ec8-4982-9ec5-31428e4b98a5",
+ "metadata": {},
+ "source": [
+ "**Question 3 -** Count the number of friends for each character, and return it as a dictionnary where 1) the key is the character, and 2) the value the number of friends."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b4b930d6-eaea-4106-b6c8-a69c5ee581fa",
+ "metadata": {
+ "deletable": false,
+ "nbgrader": {
+ "cell_type": "code",
+ "checksum": "94fb1535f4fee547db2bf0d600f73f8e",
+ "grade": false,
+ "grade_id": "cell-4762ba6b7ddfc059",
+ "locked": false,
+ "schema_version": 3,
+ "solution": true,
+ "task": false
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# YOUR CODE HERE\n",
+ "raise NotImplementedError()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f852068-4b41-4ff3-82c7-c76ae0e544d2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "**Question 4 -** Return the one with most friend and their count, as a `Tuple` (a pair where the first element is the character, and the second one the number of friends). If there are many characters for this solution, then return them as a `Tuple` where the left part is a list."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b3f79181-c16c-4b42-92f3-809695ac7b70",
+ "metadata": {
+ "deletable": false,
+ "nbgrader": {
+ "cell_type": "code",
+ "checksum": "9cc09f861147be1d77b137692f826dab",
+ "grade": false,
+ "grade_id": "cell-c7ae717bc690c271",
+ "locked": false,
+ "schema_version": 3,
+ "solution": true,
+ "task": false
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# YOUR CODE HERE\n",
+ "raise NotImplementedError()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a62b350-2bc8-476d-b9c6-13a08d649bdc",
+ "metadata": {},
+ "source": [
+ "**Question 5 -** Use the `Set` data structure to write a function that counts the number of different `Gender` in the original dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ad718e6-ad13-4197-aee6-d9a6d51c3c93",
+ "metadata": {
+ "deletable": false,
+ "nbgrader": {
+ "cell_type": "code",
+ "checksum": "8539c3a7566b0552ef9b0b7c7323d3aa",
+ "grade": false,
+ "grade_id": "cell-a3e91eca6f8d5368",
+ "locked": false,
+ "schema_version": 3,
+ "solution": true,
+ "task": false
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# YOUR CODE HERE\n",
+ "raise NotImplementedError()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74ba60d1-ac85-4d61-969a-3bdaa14fbc45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Bonus\n",
+ "\n",
+ "**Question 6** - Find interesting facts in this dataset (in case you need some [background](https://en.wikipedia.org/wiki/Marvel_Cinematic_Universe)) and use the above functions or new ones to support your findings. Example of facts could be relationships between characters, distribution of attributes (eg age), particular group of characters, etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22507d4f-41ff-479a-930f-1ebdd154e7dd",
+ "metadata": {
+ "deletable": false,
+ "nbgrader": {
+ "cell_type": "code",
+ "checksum": "a2ae9546a36fd1fe98820cb6ca9a8787",
+ "grade": false,
+ "grade_id": "cell-f49266562ffd4509",
+ "locked": false,
+ "schema_version": 3,
+ "solution": true,
+ "task": false
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# YOUR CODE HERE\n",
+ "raise NotImplementedError()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}