Update Syllabus and README

UVADS · Aug 15, 2023 · 0a3d51a · 0a3d51a
1 parent 5731c6a
commit 0a3d51a
Show file tree

Hide file tree

Showing 2 changed files with 69 additions and 35 deletions.
diff --git a/02_R_function_basics/02_Lecture_Python.ipynb b/02_R_function_basics/02_Lecture_Python.ipynb
@@ -198,26 +198,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "b3520bbc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initiated datasets repo at: /Users/amelianorman/.pydataset/\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "from pydataset import data"
+    "from pydataset import data # need to pip install this package, not part of the conda distubution"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "c7700c05",
    "metadata": {},
    "outputs": [
@@ -339,7 +331,7 @@
        "5         audi    a4    2.8  1999    6    auto(l5)   f   16   26  p  compact"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1045,7 +1037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 18,
    "id": "2ce945cd",
    "metadata": {},
    "outputs": [
@@ -1237,13 +1229,13 @@
        "[5 rows x 35 columns]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "weather = pd.read_csv('~/Desktop/DS-3001/data/weather.csv')   # Tip: use read_excel if using an excel file!\n",
+    "weather = pd.read_csv('../data/weather.csv')   # Tip: use read_excel if using an excel file!\n",
     "weather.head()"
    ]
   },
@@ -1259,7 +1251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "id": "21029a92",
    "metadata": {},
    "outputs": [
@@ -1451,7 +1443,7 @@
        "[5 rows x 31 columns]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1474,7 +1466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "id": "ad067e3e",
    "metadata": {},
    "outputs": [
@@ -1666,7 +1658,7 @@
        "[5 rows x 31 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2215,6 +2207,48 @@
     "## Basic data types in `pandas`\n",
     "![Data Types](pandas_datatypes.png)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b5fc3b1",
+   "metadata": {},
+   "source": [
+    "## Some other Python Basics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "97b59a2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'c:\\\\Users\\\\Brian Wright\\\\Documents\\\\3001Python\\\\DS-3001\\\\02_R_function_basics'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How to check our current working directory\n",
+    "import os\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7d35231f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How to load data in Python using Pandas and a relative path\n",
+    "data = pd.read_csv(\"../data/bank.csv\") # Here we are using the relative path to load data from the data folder, thus the \"..\". "
+   ]
   }
  ],
  "metadata": {

diff --git a/README.md b/README.md
@@ -113,21 +113,21 @@ The books below are essentially a starter Machine Learning Library. I will use a
 
 | Week 	| Theme 	| Topics 	| Lab 	| Reading/Repo (Prior to Class) 	|
 |:---:	|:---:	|:---:	|:---:	|:---:	|
-| Week 1 	| What is this “Data Science” that you speak of and tech stack 	| - Assessment - Videos: DS Overview and History 	| - Find DS Dream Job - Create your first project,  load the dataset, visualize using the code provided what questions could this data answer? 	|  Synchronous: Short Lab  	|
-| Week 2 	|  Getting back up to “coding speed” 	| 'Dataframing' with pandas functions 	| [- Group Case Study - Questions + PsuedoCode + Code + Functions  = High Quality Data Science](02_R_function_basics/02_Lecture_Python.ipynb) | TBD|
-| Week 3 	| How to share nicely  	| Using Quarto to Create HTML Docs	|  (03_knitr_Comms) 	|  [Documentation](https://quarto.org/docs/output-formats/html-basics.html) 	|
-| Week 4 	| Introduction to ML Concepts I	|Language of ML	| [Case Studies](https://github.com/UVADS/DS-3001/tree/main/04_ML_Concepts_I_Foundations)|H: Chapter 1 and 2  	|
-| Week 5 | Introduction to ML Concepts II | Data Preparation:kNN 	|[ML Concepts](https://github.com/UVADS/DS-3001/tree/main/05_ML_Concepts_II_Data_Prep)|H: 3 and 4  | 
-| Week 6	| Introduction to ML Concepts III	| Machine Learning Process:kNN|[ML Concepts  ](https://github.com/UVADS/DS-3001/tree/main/06_ML_Concepts_II_KNN) |H: Chapters 3 and 4 |
-| Week 7| Spring Break| | | 
-| Week 8 | Introduction to ML Concepts IV	| Evaluation	| [Evaluation Lab](https://github.com/UVADS/DS-3001/tree/main/07_ML_Eval_Metrics) | All of B. and G.- Chapter 11 	|
-| Week 9 | Nature's Perfect ML analogy: Trees Part I	|  Classification: Decisions Trees 	| [ Decision Trees](https://github.com/UVADS/DS-3001/tree/main/08_DT_Class)	| F. Chapter 5 and G. Chapter 14.1-14.3 	|
-| Week 10 | Nature's Perfect ML analogy: Trees Part II  	| Regression: Decision Trees  	| [Predicting Income for Big Brother]	| F. Chapter 5 and G. Chapter 8 	|
-| Week 11 | Wisdom of the Crowd	| Ensemble Methods I	| [Random Forest Classifier ](https://github.com/UVADS/DS-3001/tree/main/12_Ensemble_RF)	| TBD	|
-| Week 12 | Kaggle Competition  	|  	|	| 	|
-| Week 13 | Let's gather together... but separately 	|Unsupervised: Overview of Clustering Kmeans 	| [NBA Scout for the worst team in the league](https://github.com/UVADS/DS-3001/tree/main/10_kMeans%20Clustering)|  F. Chapter 1 and Chapter 9  |
-| Week 14 | Do the next right thing…ethics |  Bias in AI Discussion -Simple methods for identifying bias   - Protected Classes	|[Fairness Overview & Ethical Reflections](https://github.com/UVADS/DS-3001/tree/main/14_ML_Bias) 	| Weapons of Math Destruction  	|
-| Week 15 | Final Project Prep	|[Final Project Overview](https://github.com/UVADS/DS-3001/blob/main/final_project_overview.md)	| | Ethical Reflection Due |
+| Week 1 Aug 20th	| What is this “Data Science” that you speak of and tech stack 	| - Assessment - Videos: DS Overview and History 	| - Find DS Dream Job - Create your first project,  load the dataset, visualize using the code provided what questions could this data answer? 	|  Synchronous: Short Lab  	|
+| Week 2 Aug 27th	|  Getting back up to “coding speed” 	| 'Dataframing' with pandas functions 	| [- Group Case Study - Questions + PsuedoCode + Code + Functions  = High Quality Data Science](02_R_function_basics/02_Lecture_Python.ipynb) | TBD|
+| Week 3 Sep 3rd	| How to share nicely  	| Using Quarto to Create HTML Docs	|  (03_knitr_Comms) 	|  [Documentation](https://quarto.org/docs/output-formats/html-basics.html) 	|
+| Week 4 Sep 10th	| Introduction to ML Concepts I	|Language of ML	| [Case Studies](https://github.com/UVADS/DS-3001/tree/main/04_ML_Concepts_I_Foundations)|TBD  	|
+| Week 5 Sep 17th | Introduction to ML Concepts II | Data Preparation:kNN 	|[ML Concepts](https://github.com/UVADS/DS-3001/tree/main/05_ML_Concepts_II_Data_Prep)|TBD  | 
+| Week 6  Sep 24th	| Introduction to ML Concepts III	| Machine Learning Process:kNN|[ML Concepts  ](https://github.com/UVADS/DS-3001/tree/main/06_ML_Concepts_II_KNN) |TBD |
+| Week 7 Oct 1st| Fall Break no Tuesday Class| | | 
+| Week 8 Oct 8th | Introduction to ML Concepts IV	| Evaluation	| [Evaluation Lab](https://github.com/UVADS/DS-3001/tree/main/07_ML_Eval_Metrics) | All of B. and G.- Chapter 11 	|
+| Week 9 Oct 15th | Nature's Perfect ML analogy: Trees Part I	|  Classification: Decisions Trees 	| [ Decision Trees](https://github.com/UVADS/DS-3001/tree/main/08_DT_Class)	| TBD and G. Chapter 14.1-14.3 	|
+| Week 10 Oct 22nd | Nature's Perfect ML analogy: Trees Part II  	| Regression: Decision Trees  	| [Predicting Income for Big Brother]	| F. Chapter 5 and G. Chapter 8 	|
+| Week 11 Oct 29th | Wisdom of the Crowd	| Ensemble Methods I	| [Random Forest Classifier ](https://github.com/UVADS/DS-3001/tree/main/12_Ensemble_RF)	| TBD	|
+| Week 12 Nov 5th | Kaggle Competition - No Class on the 7th Election Day  	|  	|	| 	|
+| Week 13 Nov 12th | Let's gather together... but separately 	|Unsupervised: Overview of Clustering Kmeans 	| [NBA Scout for the worst team in the league](https://github.com/UVADS/DS-3001/tree/main/10_kMeans%20Clustering)|  TBD  |
+| Week 14 Nov 19th | Do the next right thing…ethics |  Bias in AI Discussion -Simple methods for identifying bias   - Protected Classes	|[Fairness Overview & Ethical Reflections](https://github.com/UVADS/DS-3001/tree/main/14_ML_Bias) 	| Weapons of Math Destruction  	|
+| Week 15 Nov 26th | Final Project Prep	|[Final Project Overview](https://github.com/UVADS/DS-3001/blob/main/final_project_overview.md)	| | Ethical Reflection Due |
 | Week 16 - Final TBD	| Final Projects Presentations	| [Final Project Overview](https://github.com/UVADS/DS-3001/blob/main/final_project_overview.md) 	|  	|  	|
 
 ## A few Policies that will Govern the Class