Uploaded final versions of the Jupyter notebooks for the python example.

@@ -5,8 +5,14 @@
    "metadata": {},
    "source": [
     "# A Brief Introduction to  the Python Data Science Ecosystem\n",
-    "### Presented by: L. Markowsky (UMaine ECE Department and CCI)\n",
-    "---\n",
+    "### Presented by: L. Markowsky (linda.markowsky@maine.edu)\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "### References:\n",
     "- McKinney, Wes. _Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Second Edition_, O'Reilly Media, September 2018. https://wesmckinney.com/pages/book.html (author's page) and https://github.com/wesm/pydata-book (datasets and Jupyter notebooks)\n",
     "- Python Software Foundation. _Python 3.7.3 Documentation_, 2001-2019. https://docs.python.org/3/ (start with the official Python Tutorial)\n",
@@ -64,6 +70,8 @@
     "    - encourages execut-explore workflow instead of edit-compile-run workflow\n",
     "    - documentation: https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/examples_index.html\n",
     "    - gallery of notebooks: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks\n",
+    "    - notebook presentation of a paper: https://nbviewer.jupyter.org/github/cossatot/lanf_earthquake_likelihood/blob/master/notebooks/lanf_manuscript_notebook.ipynb\n",
+    "    - launching the Jupyter notebook: https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html\n",
     "- ***NumPy*** - Numerical Python\n",
     "    - ndarray - efficient multi-dimensional array object\n",
     "    - element-wise array operations\n",
@@ -82,6 +90,7 @@
     "- ***Matplotlib*** and ***Seaborn***\n",
     "    - Matplotlib - popular Python plotting library\n",
     "        - examples (with source code): https://matplotlib.org/gallery/index.html\n",
+    "        - plotting the coherence of two signals: https://matplotlib.org/gallery/lines_bars_and_markers/cohere.html#sphx-glr-gallery-lines-bars-and-markers-cohere-py\n",
     "    - Seaborn - high-level interface for statistical visualizations; built on matplotlib\n",
     "        - examples (with source code): https://seaborn.pydata.org/examples/index.html\n",
     "    - both produce publication-quality plots\n",
@@ -98,7 +107,8 @@
     "            - linear regression models\n",
     "            - robust regression\n",
     "            - time series analysis\n",
-    "        - examples, tutorials, and recipes: https://www.statsmodels.org/stable/examples/index.html"
+    "        - examples, tutorials, and recipes: https://www.statsmodels.org/stable/examples/index.html\n",
+    "        - robust linear models: https://www.statsmodels.org/stable/examples/notebooks/generated/robust_models_0.html"
    ]
   },
   {
@@ -414,7 +424,25 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Tab completion reveals attributes and methods. Try:\n",
     "#     x.reverse\n",
@@ -426,7 +454,9 @@
     "#     x.reverse()\n",
     "#     x\n",
     "\n",
-    "#x."
+    "x.count(1)\n",
+    "print(x.reverse())\n",
+    "x"
    ]
   },
   {