vault backup: 2024-12-05 23:59:54

.obsidian/community-plugins.json

@@ -1,6 +1,5 @@
 [
   "omnisearch",
-  "obsidian-ocr",
   "text-extractor",
   "better-word-count",
   "obsidian-enhancing-export",

.obsidian/core-plugins.json

@@ -1,21 +1,31 @@
-[
-  "file-explorer",
-  "global-search",
-  "switcher",
-  "graph",
-  "backlink",
-  "canvas",
-  "outgoing-link",
-  "tag-pane",
-  "properties",
-  "page-preview",
-  "daily-notes",
-  "templates",
-  "note-composer",
-  "command-palette",
-  "editor-status",
-  "bookmarks",
-  "outline",
-  "word-count",
-  "file-recovery"
-]
+{
+  "file-explorer": true,
+  "global-search": true,
+  "switcher": true,
+  "graph": true,
+  "backlink": true,
+  "canvas": true,
+  "outgoing-link": true,
+  "tag-pane": true,
+  "page-preview": true,
+  "daily-notes": true,
+  "templates": true,
+  "note-composer": true,
+  "command-palette": true,
+  "slash-command": false,
+  "editor-status": true,
+  "starred": true,
+  "markdown-importer": false,
+  "zk-prefixer": false,
+  "random-note": false,
+  "outline": true,
+  "word-count": true,
+  "slides": false,
+  "audio-recorder": false,
+  "workspaces": false,
+  "file-recovery": true,
+  "publish": false,
+  "sync": false,
+  "bookmarks": true,
+  "properties": true
+}

.obsidian/workspace.json

@@ -13,11 +13,31 @@
             "state": {
               "type": "markdown",
               "state": {
-                "file": "WS2425/Web Tech/Übung/3/ü3.md",
-                "mode": "preview",
+                "file": "WS2425/Data Science/VL/Zusammenfassung.md",
+                "mode": "source",
                 "source": false
+              },
+              "icon": "lucide-file",
+              "title": "Zusammenfassung"
             }
           }
+        ]
+      },
+      {
+        "id": "575ada4754eaee23",
+        "type": "tabs",
+        "children": [
+          {
+            "id": "91af7024b9a5f7d4",
+            "type": "leaf",
+            "state": {
+              "type": "pdf",
+              "state": {
+                "file": "WS2425/Data Science/VL/lecture_09.pdf"
+              },
+              "icon": "lucide-file-text",
+              "title": "lecture_09"
+            }
           }
         ]
       }
@@ -39,7 +59,9 @@
               "type": "file-explorer",
               "state": {
                 "sortOrder": "alphabetical"
-              }
+              },
+              "icon": "lucide-folder-closed",
+              "title": "Files"
             }
           },
           {
@@ -48,13 +70,15 @@
             "state": {
               "type": "search",
               "state": {
-                "query": "",
+                "query": "path:\"WS2425/Data Science/\" ",
                 "matchingCase": false,
                 "explainSearch": false,
                 "collapseAll": false,
                 "extraContext": false,
                 "sortOrder": "alphabetical"
-              }
+              },
+              "icon": "lucide-search",
+              "title": "Search"
             }
           },
           {
@@ -62,7 +86,9 @@
             "type": "leaf",
             "state": {
               "type": "starred",
-              "state": {}
+              "state": {},
+              "icon": "lucide-file",
+              "title": "Plugin no longer active"
             }
           },
           {
@@ -70,7 +96,9 @@
             "type": "leaf",
             "state": {
               "type": "bookmarks",
-              "state": {}
+              "state": {},
+              "icon": "lucide-bookmark",
+              "title": "Bookmarks"
             }
           }
         ]
@@ -93,7 +121,7 @@
             "state": {
               "type": "backlink",
               "state": {
-                "file": "WS2425/Web Tech/Übung/3/ü3.md",
+                "file": "WS2425/Data Science/VL/Zusammenfassung.md",
                 "collapseAll": false,
                 "extraContext": false,
                 "sortOrder": "alphabetical",
@@ -101,7 +129,9 @@
                 "searchQuery": "",
                 "backlinkCollapsed": false,
                 "unlinkedCollapsed": true
-              }
+              },
+              "icon": "links-coming-in",
+              "title": "Backlinks for Zusammenfassung"
             }
           },
           {
@@ -110,10 +140,12 @@
             "state": {
               "type": "outgoing-link",
               "state": {
-                "file": "WS2425/Web Tech/Übung/3/ü3.md",
+                "file": "WS2425/Data Science/VL/Zusammenfassung.md",
                 "linksCollapsed": false,
                 "unlinkedCollapsed": true
-              }
+              },
+              "icon": "links-going-out",
+              "title": "Outgoing links from Zusammenfassung"
             }
           },
           {
@@ -124,7 +156,9 @@
               "state": {
                 "sortOrder": "frequency",
                 "useHierarchy": true
-              }
+              },
+              "icon": "lucide-tags",
+              "title": "Tags"
             }
           },
           {
@@ -133,8 +167,10 @@
             "state": {
               "type": "outline",
               "state": {
-                "file": "WS2425/Web Tech/Übung/3/ü3.md"
-              }
+                "file": "WS2425/Data Science/VL/Zusammenfassung.md"
+              },
+              "icon": "lucide-list",
+              "title": "Outline of Zusammenfassung"
             }
           },
           {
@@ -142,7 +178,9 @@
             "type": "leaf",
             "state": {
               "type": "advanced-tables-toolbar",
-              "state": {}
+              "state": {},
+              "icon": "spreadsheet",
+              "title": "Advanced Tables"
             }
           },
           {
@@ -154,7 +192,9 @@
                 "sortOrder": "frequency",
                 "showSearch": false,
                 "searchQuery": ""
-              }
+              },
+              "icon": "lucide-archive",
+              "title": "All properties"
             }
           },
           {
@@ -162,7 +202,9 @@
             "type": "leaf",
             "state": {
               "type": "calendar",
-              "state": {}
+              "state": {},
+              "icon": "calendar-with-checkmark",
+              "title": "Calendar"
             }
           },
           {
@@ -170,7 +212,9 @@
             "type": "leaf",
             "state": {
               "type": "juggl_nodes",
-              "state": {}
+              "state": {},
+              "icon": "ag-node-list",
+              "title": "Juggl nodes"
             }
           },
           {
@@ -178,7 +222,9 @@
             "type": "leaf",
             "state": {
               "type": "juggl_style",
-              "state": {}
+              "state": {},
+              "icon": "ag-style",
+              "title": "Juggl style"
             }
           }
         ]
@@ -190,6 +236,7 @@
   },
   "left-ribbon": {
     "hiddenItems": {
+      "omnisearch:Omnisearch": false,
       "switcher:Open quick switcher": false,
       "graph:Open graph view": false,
       "canvas:Create new canvas": false,
@@ -206,22 +253,36 @@
   },
   "active": "8f0d65f1974eff73",
   "lastOpenFiles": [
-    "WS2425/Web Tech/Übung/2/Ü2.md",
+    "WS2425/Data Science/VL/lecture_09_notes.md",
+    "Pasted image 20241205235837.png",
+    "Pasted image 20241205235827.png",
+    "Pasted image 20241205235635.png",
+    "Pasted image 20241205235614.png",
+    "Pasted image 20241205235604.png",
+    "Pasted image 20241205235524.png",
+    "WS2425/Data Science/VL/lecture_09.pdf",
+    "WS2425/Data Science/VL/lecture_08_notes.md",
+    "WS2425/Data Science/VL/lecture_08_neu.pdf",
+    "Pasted image 20241205235403.png",
+    "Pasted image 20241205235347.png",
+    "Pasted image 20241205235322.png",
+    "Pasted image 20241205235306.png",
+    "WS2425/Data Science/VL/lecture_07.pdf",
+    "WS2425/Data Science/VL/lecture_07_notes.md",
+    "WS2425/Data Science/VL/lecture_08.pdf",
+    "WS2425/Data Science/VL/lecture_06.pdf",
+    "WS2425/Data Science/VL/Zusammenfassung.md",
+    "WS2425/Data Science/Ue_P/exercise_7/excercise_7.pdf",
+    "WS2425/Data Science/Ue_P/exercise_8/excercise_8.pdf",
+    "WS2425/Data Science/Ue_P/exercise_9/excercise_9.pdf",
+    "WS2425/Data Science/VL/lecture_02.pdf",
+    "WS2425/Data Science/VL/lecture_01.pdf",
     "WS2425/Web Tech/Übung/3/ü3.md",
-    "WS2425/Web Tech/Übung/3",
+    "WS2425/Web Tech/Übung/2/Ü2.md",
     "WS2425/SWT D/P2.md",
-    "WS2425/SWT D/swtd-p-02.pdf",
-    "WS2425/SWT D/swtd-ue-02.pdf",
     "WS2425/SWT D/Ue 2.md",
     "WS2425/Web Tech/Praktikum/Untitled.md",
-    "WS2425/SWT D",
-    "WS2425/Web Tech/Übung/1/uebung01.pdf",
-    "WS2425/Web Tech/Praktikum",
     "WS2425/Web Tech/Übung/1/Ü1.md",
-    "SS24/SWT2/KW19/03_SWT2_Architekturstile_I.pdf",
-    "WS2425/Web Tech/Übung/1",
-    "WS2425/Web Tech/Übung",
-    "WS2425/Theoretische Informatik/Blatt0-Einfuehrung (1).pdf",
     "SS24/TdS/Canvas.canvas",
     "SS24/SWT2/KW19/KW19.md",
     "SS24/SWT2/SWT2.canvas",
@@ -238,21 +299,6 @@
     "README.md",
     "Untitled.md",
     "IHK/Probeklausur/Klausur Winter 2020-2021.md",
-    "IHK/Rechenaufgaben/Rechenaufgaben.md",
-    "IHK/Selbsttest/Selbsttest Teil 2.md",
-    "IHK/Selbsttest/img/Element1.png",
-    "IHK/Probeklausur/blobid1673287222977.png",
-    "IHK/Probeklausur/blobid1673287138447.png",
-    "IHK/Probeklausur/blobid1673287064541.png",
-    "IHK/Probeklausur/blobid1673286850637.png",
-    "IHK/Probeklausur/blobid1673286771726.png",
-    "IHK/Probeklausur/blobid1673286238586.png",
-    "IHK/Probeklausur/blobid1673285754812.png",
-    "IHK/Probeklausur/blobid1673284745609.png",
-    "IHK/Probeklausur/blobid1673284486298.png",
-    "IHK/Selbsttest/Selbsttest Teil 1.md",
-    "WS2324/Untitled.md",
-    "WS2324/Datenbank/Unterricht/13 Tutorium/Aufgaben.md",
-    "WS2324/Datenbank/Unterricht/08. Datenbankanfragen (Teil 2) (28.11.2023)/Praktikum/Aufgaben.md"
+    "IHK/Rechenaufgaben/Rechenaufgaben.md"
   ]
 }

WS2425/Data Science/Ue_P/exercise_1/p01_crash_course_python.ipynb

@@ -0,0 +1,487 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "753cfea7-6082-484d-a916-50554ca4cb9c",
+   "metadata": {},
+   "source": [
+    "# Crash Course Python\n",
+    "During this session, you will get a brief introduction into Python. Therefore, follow the instructions in this notebook step by step. Do not hesitate to ask questions! The instruction given are not complete, therefore: Try it on your own, play a little with the code and take a look at the official documentation of python:\n",
+    "https://docs.python.org/3.11/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beb6f785-e6a0-48e8-b838-a0e5a0987f8e",
+   "metadata": {},
+   "source": [
+    "## Data types\n",
+    "There are different built in data types in python. A variable takes the corresponding data type, if it is assigned to an instance of this type. with the Python command `type` one can check the type of a given variable or value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f529564-bfee-4d55-8110-8be138863d75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f40d465-531f-420a-8442-6875e877bb5e",
+   "metadata": {},
+   "source": [
+    "### Boolean, Numbers & Strings\n",
+    "In Python one can easily work with boolean, numbers and strings. \n",
+    " - True and False are the constants for boolean. Operators are often written out (not False, True or False ...)\n",
+    " - Strings can be defined by \"...\" and '...', but also \"\"\"...\"\"\" for multi-line strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "962dfa3d-90b5-4ed8-b5f5-f32093f1daf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "long_string = \"\"\"Hallo,\n",
+    "I'm a long string\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8592fb25-7882-456e-8418-b02ac9f0140c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = True\n",
+    "B = False\n",
+    "\n",
+    "not (A or B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ac1a2e0-a7ec-43d2-90f6-220a717a3415",
+   "metadata": {},
+   "source": [
+    "**Task:** Play around with numbers, strings an boolean. Sum up some strings, define numbers and perform some basic math."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db978452-f0d4-4511-9fba-e0d4231e3e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7142dffd-b4f0-40ea-8d0c-fee412705ab9",
+   "metadata": {},
+   "source": [
+    "Especially strings, have some built in functions. E.g. with upper() one can convert a string in only upper letters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1292afe-e088-4ca7-a32c-8026d68e37e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"hallo\".upper()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af78c85f-4e66-45f5-a114-4f8c992b8b07",
+   "metadata": {},
+   "source": [
+    "**Task:** Take a look into the documentation for strings and check some further functions one can directly use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b297a9b2-a72d-477d-81aa-97c4af6f4d39",
+   "metadata": {},
+   "source": [
+    "### Lists\n",
+    "One special data type are lists. Similar to an array, a list is a chain of values. In Python a list is defined by [] and can store different data types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48d27fe8-609d-4f9a-9f50-03c221dad093",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[0, \"hallo\", False]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ce9c921-ff6a-4539-b0b8-b3c19f155bca",
+   "metadata": {},
+   "source": [
+    "A value in a list can be accessed by given the position in brackets []. E.g. in the following example the second (index 1) element is requested. One can also access elements backwards by using a negative index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ed90d0e-9181-46ea-9d07-19cdc8d75538",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[0, \"hallo\", False][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a3bea0f-1f45-4131-bb20-2fc30ce459a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[0, \"hallo\", False][-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de6acc40-a65f-45cc-8cb6-8e77a5cdb79f",
+   "metadata": {},
+   "source": [
+    "One can also access a range of elements, by using the following notation in the brackets: start:end. The result is a list again. \n",
+    "\n",
+    "**Task** Take the upper list and access the first two elements of the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d1a5e9d-43a3-4793-9064-2b1f001923d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ebe85d9-5735-45c8-9aec-7e88732ce585",
+   "metadata": {},
+   "source": [
+    "Elements can be deleted from a list with the `del` command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cda33a05-45eb-4463-a6d8-ebad296719d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp_list = [0, \"hallo\", False]\n",
+    "del tmp_list[-2]\n",
+    "\n",
+    "tmp_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "813ffc8e-9d2f-4cb1-bd2e-34b66a2541bc",
+   "metadata": {},
+   "source": [
+    "### Dictionaries\n",
+    "Next to lists (tuples and sets - not handled here), Python offers Dictionaries as an additional data type. A dictionary is a list of key-value pairs, where a value is accessed by the value. A dictionary is defined by {...}, keys are typically strings, values can be nearly anything one like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74b09698-9fb5-4fe0-9402-cee7085f637d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_functions = {\n",
+    "    \"print\": print,\n",
+    "    \"input\": input,\n",
+    "}\n",
+    "\n",
+    "some_functions[\"print\"](\"dictionaries can store nearly everything\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1306508-e1a2-43da-a995-4fb09acc5fba",
+   "metadata": {},
+   "source": [
+    "**Task:** Create a dictionary which stores for a semester a list of lectures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d693c391-727f-4da9-83e5-a8efb0395d34",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66f74d92-497a-433c-9026-267308114e40",
+   "metadata": {},
+   "source": [
+    "## If statement\n",
+    "The if statement has the following structure: `if` CONDITION: Where condition is something evaluating to `True` or `False`. After the : a intended block begins, which is evaluated if the CONDITION is True. With else: and `elif` CONDITION the else or else if case can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb3755b6-109f-419b-8ed2-0102a89479b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp_number = 3\n",
+    "\n",
+    "if tmp_number > 4:\n",
+    "    print(f\"{tmp_number} is larger than 4\")\n",
+    "elif tmp_number < 4:\n",
+    "    print(f\"{tmp_number} is smaller than 4\")\n",
+    "else:\n",
+    "    print(f\"{tmp_number} equals 4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6ba6285-5228-4140-b03e-44733dddac05",
+   "metadata": {},
+   "source": [
+    "**Task:** Create an if statement which checks if a given number is even or odd. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a4d25b0-f476-4201-9f78-781a3d38b7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64fb2bdf-e2e2-428b-9ef1-bf79a296086d",
+   "metadata": {},
+   "source": [
+    "## for statement\n",
+    "The `for` statement has the following structure: for VALUE in ITERATOR: Where VALUE takes all values given in ITERATOR. After the : a intended block begins, which is evaluated for every step. ITERATOR can be everything one can iterate over. There are plenty functions like range to iterate over a list of numbers, but one can also use a list to iterate over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a4c109c-0966-4343-b4f3-c4a692adf52a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10):\n",
+    "    print(i**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5848b9ff-5a7d-4299-b9d2-fb0fc92fa455",
+   "metadata": {},
+   "source": [
+    "**Task:** Create a loop which iterates of every entry in a list of lectures and checks if the lecture is called \"Data Science\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "debf84c2-79cf-4b2b-9420-28c1a9ededf1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7650dc1e-8521-4ae2-b63f-d6954805385d",
+   "metadata": {},
+   "source": [
+    "## Functions\n",
+    "A function has the following structure: def NAME(VAR1,VAR2,VAR3=DEFAULTVAL3): Where after the : a intended block starts, which is evaluated when the function is called. The parameters is a list of Variables, where a variable can also have a default value. The returned value is given by a return statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d739bd2e-bfc7-4256-a9a5-947f7de0ada9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f725e9f-9c99-417c-8911-439c96e33e24",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3eabab2-7e88-428f-b81f-2b910d61af74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_add(x,y=2):\n",
+    "    return x+y\n",
+    "\n",
+    "print(my_add(2,4))\n",
+    "print(my_add(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f5fca24-7b2c-493c-bd0e-af649ac89a0e",
+   "metadata": {},
+   "source": [
+    "**Task:** Create a function which takes a list of numbers and returns a list where every number is multiplied with a factor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f1d96ed-9af3-435c-8df1-d15c89a83e6c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7b67ab6-b009-414b-8f35-da4b1ec09564",
+   "metadata": {},
+   "source": [
+    "## Classes\n",
+    "Take a look into the official Python tutorial for the way how a class is defined in Python:\n",
+    "https://docs.python.org/3/tutorial/classes.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "430b950b-b9a8-4e42-8f71-9dd938959ac5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85f4f762-c825-4865-abab-74509038fbc2",
+   "metadata": {},
+   "source": [
+    "**Task:** Create a class which represents a lecture. Every lecture has a list of students and a title. Furthermore create a function which adds students to a lecture and a function which returns the number of students. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1efbb8a3-938a-4ebe-b05c-88687a49a6d6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d85fef-d5eb-4c9a-aea3-c4647777d58b",
+   "metadata": {},
+   "source": [
+    "## Modules\n",
+    "Python has a lot of modules included, but there is also a huge amount of models which can be installed. A modul can be imported with the import command. A module can be installed with the help of pip (command line tool). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8b0d5b5-ec05-4c29-948b-a9c09a7944c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tqdm\n",
+    "\n",
+    "r = 0\n",
+    "for i in tqdm.tqdm(range(10000000)):\n",
+    "    r += i\n",
+    "\n",
+    "print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5c40f75-f103-47c6-b5ca-a7fae135bd2f",
+   "metadata": {},
+   "source": [
+    "In jupyter a command line command can be executed in a code cell if it starts with a !."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42c49615-29ef-40b3-9954-8cf589bd505b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cmd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbcc2c40-05c9-42cc-9281-51fd307d44f9",
+   "metadata": {},
+   "source": [
+    "**Task:** Take a look on PyPI and search for an interesting module. Install the module and try it out. Some example modules:\n",
+    " - tensorflow (deep learning)\n",
+    " - numpy (numerical methods)\n",
+    " - sklearn (machine learning)\n",
+    " - ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93cf0ef8-c5a6-4e6b-b7d8-9d55728e4ff4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

WS2425/Data Science/VL/Zusammenfassung.md

@@ -0,0 +1,185 @@
+# 1
+1. **Organizational Information**
+    
+    - **Page:** 2
+    - **Notes:**
+        - Contact: [klaus.kaiser@fh-dortmund.de](mailto:klaus.kaiser@fh-dortmund.de)
+        - Room: B.2.04
+        - Professor Klaus Kaiser has a background in data science across various industries.
+2. **Introduction to Data Science**
+    
+    - **Page:** 4-7
+    - **Notes:**
+        - Definition: Data science is about turning raw data into meaningful insights.
+        - Interdisciplinary field combining statistics, computing, and domain knowledge.
+        - Historical context of the term “data science” from 1962 to 2001.
+3. **What is Data Science?**
+    
+    - **Page:** 8-10
+    - **Notes:**
+        - Data science involves using methods and systems to extract knowledge from data.
+        - The intersection of math/statistics, computer science, and domain knowledge is crucial.
+4. **Practical Example of Data Science Project: Monkey Detection**
+    
+    - **Page:** 16-24
+    - **Notes:**
+        - Steps include understanding the problem, data collection, labeling, model training, and deployment.
+5. **Related Fields in Data Science**
+    
+    - **Page:** 14-15
+    - **Notes:**
+        - Data Engineering: Building systems for data collection and processing.
+        - Data Analysis: Inspecting and transforming data to inform decisions.
+6. **Tasks in Data Science**
+    
+    - **Page:** 16
+    - **Notes:**
+        - Overview of different tasks within classical machine learning.
+7. **Real-World Examples of Data Science Applications**
+    
+    - **Page:** 25-32
+    - **Notes:**
+        - Applications include autonomous driving, face recognition, predictive maintenance, fraud detection, recommendation systems, and cancer detection.
+8. **Overview of Lecture Content**
+    
+    - **Page:** 34-35
+    - **Notes:**
+        - Basic topics include data basics, statistics, presentation techniques, and machine learning.
+9. **Organizational: Schedule and Exam Information**
+    
+    - **Page:** 38-40
+    - **Notes:**
+        - Lecture and exercise schedules, language of instruction, and exam details (written exam with bonus points for data analytics).
+10. **Expectations from Students**
+    
+    - **Page:** 42-43
+    - **Notes:**
+        - Emphasis on respect, professionalism, and willingness to participate.
+11. **How to Continue in Data Science**
+    
+    - **Page:** 46-48
+    - **Notes:**
+        - Suggested literature for further reading and related courses available in the curriculum.
+12. **Summary & References**
+    
+    - **Page:** 51-55
+    - **Notes:**
+        - Key takeaways: ability to explain data science and recognize its applications.
+        - Important references for further study are provided.
+
+# 2
+- **Data Science Definition**: Creating knowledge from data using math, statistics, and computer science.
+    
+- **Data Types**:
+    
+    - **Structured**: Follows a predefined model (e.g., tables).
+        
+    - **Unstructured**: Lacks explicit structure (e.g., text, images).
+        
+- **Data Categories**:
+    
+    - Discrete vs. Continuous
+        
+    - Nominal, Ordinal, Interval, Ratio
+        
+    - Qualitative vs. Quantitative
+        
+- **Data Interchange Formats**: Common formats include CSV and JSON.
+    
+- **Data Trust**: Importance of data quality dimensions: accuracy, completeness, consistency, timeliness, uniqueness, validity.
+
+# 3
+- **Data Categories**: Discrete, continuous, nominal, ordinal, interval, ratio, qualitative, and quantitative.
+    
+- **Data Interchange Formats**: Common formats include CSV and JSON.
+    
+- **Data Quality Dimensions**: Accuracy, completeness, consistency, timelessness, uniqueness, validity.
+    
+- **Data Types**: Primary (real-time, specific) vs. secondary (past, economical).
+    
+- **Data Acquisition Methods**: Capturing (sensors, surveys), retrieving (databases, APIs), collecting (web scraping).
+    
+- **FAIR and Open Data**: Principles for sustainable data usage and importance in scientific reproducibility.
+
+# 4
+- **Primary vs. Secondary Data**: Primary data is collected for a specific purpose, while secondary data is sourced from existing datasets.
+    
+- **Data Collection Techniques**: Includes scraping, which extracts data from websites, and considerations for legality and data protection.
+    
+- **Data Protection**: Emphasizes GDPR compliance, anonymization, and pseudonymization of personal data.
+    
+- **Statistics Basics**: Introduces descriptive and inductive statistics, frequency distributions, and graphical representations like histograms and bar charts.
+    
+- **FAIR Principles**: Focus on data findability, accessibility, interoperability, and reusability.
+
+# 5
+- **Data Scraping**: Extracts data from program outputs; should be a last resort.
+    
+- **Anonymization**: Removes personal info to protect identity; pseudonymization allows identification with additional info.
+    
+- **Statistics Types**: Descriptive, explorative, and inductive statistics.
+    
+- **Frequencies**: Absolute and relative frequencies; visualized through histograms, pie charts, and bar charts.
+    
+- **Central Tendencies**: Mode, median, and mean; box plots visualize data distribution.
+    
+- **Statistical Dispersion**: Measures spread of data; includes range, quartile range, and empirical variance.
+
+# 6
+- **Histograms**: Visual representation of frequency for continuous data.
+    
+- **Cumulative Frequency**: Measures total frequency up to a certain value.
+    
+- **Statistical Dispersion**: Includes empirical variance and standard deviation.
+    
+- **Bivariate Analysis**: Examines relationships between two variables.
+    
+- **Correlation Coefficients**: Quantifies the strength and direction of relationships.
+    
+- **Contingency Tables**: Displays frequencies of categorical variables.
+    
+- **Pearson Coefficient**: Measures linear correlation between metric variables.
+    
+- **Ordinal Data**: Can be analyzed using rank correlation methods.
+
+# 7
+- **Correlation**: Describes relationships between two variables using correlation coefficients based on variable types (nominal, ordinal, metric).
+    
+- **Contingency Tables**: Used for two-dimensional frequency distributions; includes conditional frequencies and measures of association.
+    
+- **Probability Theory**: Introduces random experiments, events, and Kolmogorov axioms; covers Laplace experiments and combinatorics.
+    
+- **Bayes’ Theorem**: Explains conditional probability and its application in real-world scenarios, such as medical testing.
+    
+- **Outcome**: Understanding of probability basics, combinatorial calculations, and Bayes’ theorem application.
+
+# 8
+- **Random Experiment**: Defined by well-defined conditions with unpredictable outcomes (e.g., dice throw).
+    
+- **Kolmogorov Axioms**: Fundamental properties of probability measures.
+    
+- **Random Variables**: Assign outcomes to numbers; can be discrete (countable values) or continuous (any value in an interval).
+    
+- **Distributions**: Includes discrete (e.g., binomial, uniform) and continuous (e.g., normal) distributions.
+    
+- **Expected Value & Variance**: Key metrics for understanding random variables' behavior.
+    
+- **Applications**: Used in statistical tests and linear regression.
+
+# 9
+- **Random Variables**: Defined as functions mapping outcomes to real numbers.
+    
+- **Discrete vs. Continuous Distributions**: Discrete has countable outcomes; continuous uses probability density functions.
+    
+- **Simple Linear Regression**: Models correlation between independent (X) and dependent (Y) variables.
+    
+- **Key Concepts**:
+    
+    - **Residual Analysis**: Evaluates fit of regression line.
+        
+    - **Determinacy Measure (R²)**: Indicates model fit; ranges from 0 to 1.
+        
+- **Estimation**: Parameters (β0, β1) estimated using least squares method.
+    
+- **Applications**: Used in various fields to predict outcomes based on correlations.
+

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