Update to Video Links

00. Introduction.ipynb

@@ -72,7 +72,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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01. Overview.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 1. Overview\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=r_LB_k17MGE&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=r_LB_k17MGE&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience01.png)](https://www.youtube.com/watch?v=r_LB_k17MGE&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "### Introduction\n",
@@ -424,7 +423,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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02. Import_Export.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 2. Import and Export Data\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=H05A_rftppU&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=H05A_rftppU&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience02.png)](https://www.youtube.com/watch?v=H05A_rftppU&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "Python has functions for reading, creating, and deleting files. The high-level steps for many data science applications is to import data, analyze data, and export results.\n",
@@ -355,7 +354,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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03. Analyze.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 3. Analyze Data\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=5yv_ID4YNTI&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=5yv_ID4YNTI&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience03.png)](https://www.youtube.com/watch?v=5yv_ID4YNTI&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "Once data is read into Python, a first step is to analyze the data with summary statistics. This is especially true if the data set is large. Summary statistics include the count, mean, standard deviation, maximum, minimum, and quartile information for the data columns. \n",

04. Visualize.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 4. Visualize Data\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=w97CsaLuEvI&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=w97CsaLuEvI&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience04.png)](https://www.youtube.com/watch?v=w97CsaLuEvI&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "In addition to summary statistics, data visualization helps to understand the data characteristics and how different variables are related.\n",
@@ -539,7 +538,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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05. Prepare_data.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 5. Prepare Data\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=tBfGYKITno8&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=tBfGYKITno8&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience05.png)](https://www.youtube.com/watch?v=tBfGYKITno8&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "Much of data science and machine learning work is getting clean data into the correct form. This may include data cleansing to remove outliers or bad information, scaling for machine learning algorithms, splitting into train and test sets, and enumeration of string data. All of this needs to happen before regression, classification, or other model training. Fortunately, there are functions that help with automating data preparation.\n",
@@ -352,7 +351,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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06. Regression.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 6. Regression\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=iOJbOfPHXLg&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=iOJbOfPHXLg&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience06.png)](https://www.youtube.com/watch?v=iOJbOfPHXLg&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "Regression is the process of adjusting model parameters to fit a prediction `y` to measured values `z`. There are independent variables `x` as inputs to the model to generate the predictions `y`. For machine learning, the objective is to minimize a loss function by adjusting model parameters. A common loss function is the sum of squared errors between the predicted `y` and measured `z` values.\n",
@@ -618,7 +617,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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07. Features.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 7. Features\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=aNVkTrCS6lE&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=aNVkTrCS6lE&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience07.png)](https://www.youtube.com/watch?v=aNVkTrCS6lE&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "**Classification** predicts *discrete labels (outcomes)* such as `yes`/`no`, `True`/`False`, or any number of discrete levels such as a letter from text recognition. An example of classification is to suggest a movie you will want to watch next (label) based on your prior viewing history (feature). **Regression** is different than classification with continuous outcomes such as any floating point number in a range. An example of regression is to build a correlation of the temperature of a pan of water (label) based on the time it has been heating (feature). The temperature values are continuous while the next movie is one of many discrete options.\n",
@@ -372,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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08. Classification.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 8. Classification\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=VLKEj9EN2ew&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=VLKEj9EN2ew&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience08.png)](https://www.youtube.com/watch?v=VLKEj9EN2ew&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "**Classification** predicts *discrete labels (outcomes)* such as `yes`/`no`, `True`/`False`, or any number of discrete levels such as a letter from text recognition, or a word from speech recognition. There are two main methods for training classifiers: unsupervised and supervised learning. The difference between the two is that unsupervised learning does not use labels while supervised learning uses labels to build the classifier. The goal of unsupervised learning is to cluster input features but without labels to guide the grouping. "
@@ -680,7 +679,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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09. Interpolation.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 9. Interpolation\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=XstK9M4jzBY&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=XstK9M4jzBY&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience09.png)](https://www.youtube.com/watch?v=XstK9M4jzBY&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "Interpolation constructs new prediction points from a discrete set of known data points. There are many types of interpolation such as nearest neighbor (piecewise constant), linear, polynomial, [cubic spline](https://apmonitor.com/wiki/index.php/Main/ObjectCspline), and [basis spline](https://apmonitor.com/wiki/index.php/Main/ObjectBspline). In interpolation, the data provide the shape of the approximate function, with piecewise or higher-order polynomial equations to exactly match the data points at those given discrete locations.\n",
@@ -153,7 +152,6 @@
    ]
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -315,7 +313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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10. Solve_Equations.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 10. Solve Equations\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=c40z75JnT44&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=c40z75JnT44&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience10.png)](https://www.youtube.com/watch?v=c40z75JnT44&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "Equations are at the root of data science. It is what turns data into actionable information by developing mathematical expressions that mimic physical systems. Some math expressions are simple and can be calculated sequentially such as\n",
@@ -489,7 +488,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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11. Differential_Equations.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 11. Differential Equations\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=HReAo38LoM4&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=HReAo38LoM4&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience11.png)](https://www.youtube.com/watch?v=HReAo38LoM4&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "Specific types of equations with differential terms arise from fundamental relationships such as conservation of mass, energy, and momentum. For example, the accumulation of mass $\\frac{dm}{dt}$ in a control volume is equal to the mass inlet $\\dot m_{in}$ minus mass outlet $\\dot m_{out}$ from that volume.\n",
@@ -283,7 +282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
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12. Time_Series.ipynb

@@ -6,8 +6,7 @@
    "source": [
     "## 12. Time Series\n",
     "\n",
-    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=yfgE0GheCWY&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)",
-    "\n",
+    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=yfgE0GheCWY&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
     "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience12.png)](https://www.youtube.com/watch?v=yfgE0GheCWY&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
     "\n",
     "**Time series** data is produced sequentially as new measurements are recorded. Models derived from the data give insight into what happens next. They also show how the system can be changed to achieved a different future outcome. Time series models are a representation of a dynamic system in discrete time. Putting a model into time series form is the basis for many methods in dynamics and control. A **digital twin** is a virtual representation of a process that runs in parallel to the physical system. A time series model can be considered a digital twin in the narrow definition of just specific inputs and outputs included in the model. Below is the time series model with a single input `u` and single output `y` with `k` as an index that refers to the time step.\n",
@@ -691,7 +690,7 @@
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    "pygments_lexer": "ipython3",
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XX. Final_Project.ipynb

@@ -388,7 +388,7 @@
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    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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