diff --git a/environment.yml b/environment.yml
index 39d6a2796a15836757d42bd0c75095b956beca27..3dd1d4422a224d866fa7a978cc9ec9d5686c342f 100644
--- a/environment.yml
+++ b/environment.yml
@@ -18,6 +18,9 @@ dependencies:
   - gfortran_linux-64
   - numba
   - jax
+  - dask
+  - graphviz
+  - python-graphviz
   - mpich
   - mpi4py
   - hdf5=*=mpi*
diff --git a/notebooks/2a--NumPy.ipynb b/notebooks/2a--NumPy.ipynb
index a63227d9c8881c1af9f4e11018dc53fa6d400f9c..b5ebb9d7804299e1854219c0b2c56ee4c9c78340 100644
--- a/notebooks/2a--NumPy.ipynb
+++ b/notebooks/2a--NumPy.ipynb
@@ -529,11 +529,12 @@
     }
    },
    "source": [
-    "### NumPy Array - Indexing\n",
+    "### NumPy Array - Advanced Indexing\n",
     "\n",
     "\n",
-    "* indexes can be:\n",
-    "    * a list (or an array!) of positions\n",
+    "* the selection object (as in `x[obj]`) can be:\n",
+    "    * a non-tuple sequence object (i.e., list or range) or ndarray of indices\n",
+    "    * or a tuple with at least one non-tuple squence object or ndarray\n",
     "    * a boolean array $\\to$ must have same dimensions"
    ]
   },
@@ -566,7 +567,7 @@
     "# for i in range(len(x)):\n",
     "#     if y[i]<10:\n",
     "#         x[i] = 100\n",
-    "# This is actually a view! we discuss it next\n",
+    "# Indexing can be used to assign values to indexed arrays\n",
     "x[x < y] = 100\n",
     "print(\"Oneliner x: \", x)"
    ]
@@ -581,10 +582,11 @@
    "source": [
     "### NumPy Arrays vs Views\n",
     "\n",
-    "* Using slicing on arrays usually returns a view on the original array\n",
+    "* Basic slicing on arrays usually returns a view on the original array\n",
     "* Data is **not** copied $\\to$ changing the original array also changes the view\n",
     "* Views may be <span style=\"color:red\">non-contiguous data</span>\n",
-    "* use `copy()` for copying the data over"
+    "* use `copy()` for copying the data over\n",
+    "* Advanced indexing directly returns a copy of the indexed data"
    ]
   },
   {
diff --git a/notebooks/4b--Parallel_Frameworks.ipynb b/notebooks/4b--Parallel_Frameworks.ipynb
index 37734adfea10f8389bf07355d1397f34e21c8061..bbcd0c274872d949c36d0c323c6780d059d0e0ea 100644
--- a/notebooks/4b--Parallel_Frameworks.ipynb
+++ b/notebooks/4b--Parallel_Frameworks.ipynb
@@ -16,7 +16,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
     "## Outline\n",
     "\n",
@@ -29,7 +33,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
     "## Why Python frameworks for parallel computing?\n",
     "\n",
@@ -40,63 +48,562 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
-    "## Comparison of parallel frameworks (selection)\n",
+    "## Overview on parallel frameworks (selection)\n",
     "\n",
     "* [Apache Spark](https://spark.apache.org): designed for distributed big data analytics, features include SQL, distributed caching, multi-language bindings including Python\n",
-    "* [Dask](https://www.dask.org): parallel distributed computing, based on a reimplementation of the NumPy API (similarly for Pandas and scikit-learn) in combination with a powerful task scheduler\n",
-    "* [Ray](https://www.ray.io): core library for distributed computing, plus growing ecosystem with specific libraries (often from AI)"
+    "* [Dask](https://www.dask.org): parallel distributed computing, provides an implementation of the NumPy API (similarly for Pandas and scikit-learn) in combination with a powerful task scheduler, feels quite *pythonic*\n",
+    "* [Ray](https://www.ray.io): core library for distributed computing, plus growing ecosystem with specific libraries (very often for AI applications)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
-    "## Cloud vs HPC environments\n",
+    "## Background: Cloud vs HPC environments\n",
+    "\n",
+    "### How to run and scale Python codes using such parallel frameworks?\n",
     "\n",
     "* Cloud\n",
     "    * (software) design often centered around web services\n",
-    "    * scaling works typically via container orchestration systems (e.g. Kubernetes)\n",
-    "    * Python frameworks for parallel computing are often designed with Cloud environments in mind (as these are available to a broader audience in contrast to HPC systems)\n",
+    "    * scaling works e.g. via container orchestration systems (e.g. Kubernetes)\n",
+    "    * Python frameworks for parallel computing are often designed with Cloud environments in mind (as these are available to a broader audience in contrast to 'real' HPC systems)\n",
     "\n",
     "* HPC\n",
-    "    * workloads managed via batch jobs\n",
-    "    * non-interactive use preferred\n",
-    "    * Practical challenge: How to get Python parallel frameworks to operate in concert with a batch scheduler?"
+    "    * not per-se designed to run web services (e.g. interactive dashboards, dedicated scheduler services)\n",
+    "    * workloads are submitted via batch jobs\n",
+    "    * non-interactive use highly preferred\n",
+    "\n",
+    "**Practical challenge: Need get Python parallel frameworks to operate in concert with a HPC batch scheduler and infrastructure**"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
     "## Example: Dask"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
     "### Dask array"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "        <td>\n",
+       "            <table>\n",
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+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 781.25 kiB </td>\n",
+       "                        <td> 78.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <td> 10 Chunks </td>\n",
+       "                    </tr>\n",
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+       "                    <th> Type </th>\n",
+       "                    <td> int64 </td>\n",
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+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<array, shape=(200, 500), dtype=int64, chunksize=(100, 100), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a chunked Dask array from a NumPy array\n",
+    "import numpy as np\n",
+    "import dask.array as da\n",
+    "\n",
+    "data = np.arange(100_000).reshape(200, 500)\n",
+    "a = da.from_array(data, chunks=(100, 100))\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 100)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect information about the chunking\n",
+    "a.chunksize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 78.12 kiB </td>\n",
+       "                        <td> 78.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (100, 100) </td>\n",
+       "                        <td> (100, 100) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 11 Tasks </td>\n",
+       "                        <td> 1 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> int64 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">100</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<blocks, shape=(100, 100), dtype=int64, chunksize=(100, 100), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect information about a particular chunk of data\n",
+    "a.blocks[1, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
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+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 400 B </td>\n",
+       "                        <td> 400 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (50,) </td>\n",
+       "                        <td> (50,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 11 Tasks </td>\n",
+       "                        <td> 1 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> int64 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<getitem, shape=(50,), dtype=int64, chunksize=(50,), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# indexing/slicing works just as with NumPy\n",
+    "a[:50, 200]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  200,   700,  1200,  1700,  2200,  2700,  3200,  3700,  4200,\n",
+       "        4700,  5200,  5700,  6200,  6700,  7200,  7700,  8200,  8700,\n",
+       "        9200,  9700, 10200, 10700, 11200, 11700, 12200, 12700, 13200,\n",
+       "       13700, 14200, 14700, 15200, 15700, 16200, 16700, 17200, 17700,\n",
+       "       18200, 18700, 19200, 19700, 20200, 20700, 21200, 21700, 22200,\n",
+       "       22700, 23200, 23700, 24200, 24700])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lazy evaluation (only a task graph is created), we need to call compute() \n",
+    "# explicitly to get the result from any Dask object!\n",
+    "a[:50, 200].compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
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+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
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+       "                        <td> 8 B </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> () </td>\n",
+       "                        <td> () </td>\n",
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+       "                        <th> Count </th>\n",
+       "                        <td> 26 Tasks </td>\n",
+       "                        <td> 1 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float64 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        \n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<mean_agg-aggregate, shape=(), dtype=float64, chunksize=(), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a Dask object to compute the mean value over the distributed array\n",
+    "a_mean = a.mean()\n",
+    "a_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# visualize the task graph necessary to perform the computation\n",
+    "a_mean.visualize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49999.5"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# perform the computation on the Dask array\n",
+    "a_mean.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49999.5"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# perform the computation on the original NumPy array\n",
+    "data.mean()"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
     "### Dask futures"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
     "### Dask-MPI"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
    "source": [
     "### Case Study"
    ]
@@ -110,6 +617,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Slideshow",
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",