Fixed up introductory material

Author: adamcc

README.md

@@ -8,18 +8,18 @@ Ho](https://twitter.com/jacuzzijo), [Yishan
 Mai](https://twitter.com/myish_irl), and [Adam
 Claridge-Chang](http://www.claridgechang.net/)
 
-### This material is used in the following classes:
+### Introducing biomedical scientists to data analysis with Python
+
+The goal of this class is to introduce biomedical scientists to data
+analysis with Python notebooks. Ideally, this should be a semester-long
+course. This 2-hour class is presented to two groups.
 
 - Duke-NUS PhD programme in Clinical and Translational Sciences.
   Foundations of Precision Medicine Hands-on Workshops (GMS6812).
 - Duke-NUS PhD programme in Integrated Biology and Medicine. Ethics and
   Personal and Professional Development Sessions.
 
-The goal of this class is to introduce biomedical scientists to data
-analysis with Python notebooks. Ideally, this should be a semester-long
-course, but has been limited to this 2-hour class.
-
-The class has the following steps:
+### The class has the following steps
 
 1.  **Preparation 1.** Read the [Introduction](introduction.html) and
     install the prerequisite software on your laptop.
@@ -34,6 +34,8 @@ The class has the following steps:
 5.  **Short introduction to estimation and LLMs.** Introduction to
     estimation statistics [web app](https://www.estimationstats.com/#/)
     and [python package](dabest_introduction.html).
+6.  **Continuing education.** Review the Additional Resources page to
+    continue your self-education in data analysis.
 
 If you have any questions about these materials, please contact your
 course coordinator.

nbs/01_Introduction.ipynb

@@ -32,29 +32,35 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19964860-2836-43b1-963e-063dfe336287",
+   "id": "9a9d5566-8e29-44a9-8c60-9a909ed34c56",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<iframe width=\"600\" height=\"400\"\n",
-       "src=\"https://www.youtube.com/embed/kgicuytCkoY\">\n",
-       "</iframe>\n"
+       "\n",
+       "<iframe width=\"700\" height=\"400\" src=\"https://www.youtube.com/embed/kgicuytCkoY\" \n",
+       "        frameborder=\"0\" allowfullscreen></iframe>\n"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
+     "execution_count": null,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%%HTML\n",
-    "<iframe width=\"600\" height=\"400\"\n",
-    "src=\"https://www.youtube.com/embed/kgicuytCkoY\">\n",
-    "</iframe>"
+    "# Python is a general programming language. \n",
+    "# This website was generated with Python tools.\n",
+    "# This window is a tiny demonstration of how Python is integrated with the web.\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML(\"\"\"\n",
+    "<iframe width=\"700\" height=\"400\" src=\"https://www.youtube.com/embed/kgicuytCkoY\" \n",
+    "        frameborder=\"0\" allowfullscreen></iframe>\n",
+    "\"\"\")"
    ]
   },
   {
@@ -123,17 +129,14 @@
    "source": [
     "## In class\n",
     "\n",
-    "10.  An overview of data analysis.\n",
+    "10. **Session Coding.** Bring your laptop to class, and we will go through the examples in [Data Analysis with Jupyter](data_analysis_with_jupyter_and_python.html).\n",
     "\n",
-    "11.  A tour of the estimationstats web app.\n",
+    "11. **Lecture.** Crash course on the history of and key issues in data visualization.\n",
     "\n",
-    "12.  Presentation of a Jupyter\n",
-    "    [notebook](https://drive.google.com/file/d/1o4Ou2fHY73l6Nb7MUp2GqwtrJbcQ80Ix/view?usp=sharing)\n",
-    "    that introduces techniques in data analysis using Python.\n",
+    "12. **Short introduction to estimation and LLMs.** Introduction to estimation statistics [web app](https://www.estimationstats.com/#/) and [python package](dabest_introduction.html).\n",
     "\n",
-    "13.  Try JupyterLite, an experimental web version of JupyterLab, with a\n",
-    "    class notebook\n",
-    "    [here](https://sangyu.github.io/Evidence-Session/lab?path=Notebooks%2F01.+Data+Analysis+with+Jupyter+and+Python.ipynb)."
+    "\n",
+    "Optional: Try JupyterLite, an experimental web version of JupyterLab, with a class notebook [here](https://sangyu.github.io/Evidence-Session/lab?path=Notebooks%2F01.+Data+Analysis+with+Jupyter+and+Python.ipynb)."
    ]
   },
   {

nbs/index.ipynb

@@ -26,7 +26,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### This material is used in the following classes:\n",
+    "### Introducing biomedical scientists to data analysis with Python\n",
+    "\n",
+    "The goal of this class is to introduce biomedical scientists to data analysis with Python notebooks. Ideally, this should be a semester-long course. This 2-hour class is presented to two groups.\n",
     "\n",
     "* Duke-NUS PhD programme in Clinical and Translational Sciences. Foundations of Precision Medicine Hands-on Workshops (GMS6812).\n",
     "* Duke-NUS PhD programme in Integrated Biology and Medicine. Ethics and Personal and Professional Development Sessions."
@@ -36,17 +38,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The goal of this class is to introduce biomedical scientists to data analysis with Python notebooks. Ideally, this should be a semester-long course, but has been limited to this 2-hour class. \n",
-    "\n",
-    "The class has the following steps: \n",
+    "### The class has the following steps\n",
     "\n",
     "1. **Preparation 1.** Read the [Introduction](introduction.html) and install the prerequisite software on your laptop.\n",
     "2. **Preparation 2.** Go through a [Quick Tour of the Notebook](quick_tour_of_the_notebook.html) to familiarise yoruself to the jupyter notebook environment.\n",
     "3. **Session Coding.** Bring your laptop to class, and we will go through the examples in [Data Analysis with Jupyter](data_analysis_with_jupyter_and_python.html).\n",
     "4. **Lecture.** Crash course on the history of and key issues in data visualization.\n",
     "5. **Short introduction to estimation and LLMs.** Introduction to estimation statistics [web app](https://www.estimationstats.com/#/) and [python package](dabest_introduction.html).\n",
-    "\n",
-    "\n",
+    "6. **Continuing education.** Review the Additional Resources page to continue your self-education in data analysis.\n",
     "\n",
     "If you have any questions about these materials, please contact your course coordinator.\n"
    ]