Update documentation

_sources/chapters/module-1/tech_stack.md

@@ -1,14 +1,15 @@
 # Tech Stack
 
+This section provides a brief introduction to the different technologies we will use throughout the course.
+
 ## Open OnDemand
-Open OnDemand is a graphical user interface that allows access to HPC via a web browser.  The Open OnDemand access point is ood.hpc.virginia.edu.  Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server & FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters.  Let's have a quick look at their detailed <a href="https://www.rc.virginia.edu/userinfo/hpc/ood">documentation</a>
+Open OnDemand is a graphical user interface that allows access to HPC via a web browser. **We will use it to run all our code without requiring any local installations**. The Open OnDemand access point is ood.hpc.virginia.edu.  Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server & FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters.  Let's have a quick look at their detailed <a href="https://www.rc.virginia.edu/userinfo/hpc/ood">documentation</a>
 
 ## JupyterLab
-In this course we are going to work with **Jupyter notebooks**, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. You can get familiar to the use of jupyter notebook in our future <a href="jupyter_notebooks.html">lesson</a>.
-
-
+In this course we will work with **Jupyter notebooks**, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. We will become familiar with Jupyter notebooks in our future <a href="jupyter_notebooks.html">lesson</a>.
 
 ## Canvas
+Canvas will be used to share all course materials, including Jupyter notebooks for each lesson, assignments, and in-class activities. Quizzes will also be completed on this platform, and assignments will be submitted through it. Stay tuned on Canvas for announcements!
 
 
 

chapters/module-1/tech_stack.html

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@@ -351,16 +351,18 @@ <h2> Contents </h2>
 
   <section class="tex2jax_ignore mathjax_ignore" id="tech-stack">
 <h1>Tech Stack<a class="headerlink" href="#tech-stack" title="Link to this heading">#</a></h1>
+<p>This section provides a brief introduction to the different technologies we will use throughout the course.</p>
 <section id="open-ondemand">
 <h2>Open OnDemand<a class="headerlink" href="#open-ondemand" title="Link to this heading">#</a></h2>
-<p>Open OnDemand is a graphical user interface that allows access to HPC via a web browser.  The Open OnDemand access point is <a class="reference external" href="http://ood.hpc.virginia.edu">ood.hpc.virginia.edu</a>.  Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server &amp; FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters.  Let’s have a quick look at their detailed <a href="https://www.rc.virginia.edu/userinfo/hpc/ood">documentation</a></p>
+<p>Open OnDemand is a graphical user interface that allows access to HPC via a web browser. <strong>We will use it to run all our code without requiring any local installations</strong>. The Open OnDemand access point is <a class="reference external" href="http://ood.hpc.virginia.edu">ood.hpc.virginia.edu</a>.  Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server &amp; FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters.  Let’s have a quick look at their detailed <a href="https://www.rc.virginia.edu/userinfo/hpc/ood">documentation</a></p>
 </section>
 <section id="jupyterlab">
 <h2>JupyterLab<a class="headerlink" href="#jupyterlab" title="Link to this heading">#</a></h2>
-<p>In this course we are going to work with <strong>Jupyter notebooks</strong>, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. You can get familiar to the use of jupyter notebook in our future <a href="jupyter_notebooks.html">lesson</a>.</p>
+<p>In this course we will work with <strong>Jupyter notebooks</strong>, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. We will become familiar with Jupyter notebooks in our future <a href="jupyter_notebooks.html">lesson</a>.</p>
 </section>
 <section id="canvas">
 <h2>Canvas<a class="headerlink" href="#canvas" title="Link to this heading">#</a></h2>
+<p>Canvas will be used to share all course materials, including Jupyter notebooks for each lesson, assignments, and in-class activities. Quizzes will also be completed on this platform, and assignments will be submitted through it. Stay tuned on Canvas for announcements!</p>
 </section>
 </section>