Add section on Jupyter notebook

Author: fangohr

book/15-visualising-data.ipynb

@@ -14601,6 +14601,31 @@
     "ax.set_yticks(range(0, 21, 4));\n",
     "ax.grid('on')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Jupyter Notebooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [Jupyter Notebook](https://jupyter.org) has become a central tool for interactive data exploration and data analysis. I would go so far to say that most data scientists will see the Jupyter notebook as the default place to start a data exploration, analysis and machine learning project. \n",
+    "\n",
+    "Why is this so? The combination of annotation, code snippets, inlined results from computation or visualisation and the automatic logging of these steps in a notebook file can be of great use for research and development activities. A slightly longer summary is [available here](https://fangohr.github.io/blog/jupyter-for-computational-science-and-data-science.html).\n",
+    "\n",
+    "Some recent publications on the topic:\n",
+    "\n",
+    "- Brian Granger, Fernando Pérez. [*Jupyter: Thinking and Storytelling With Code and Data*](https://doi.org/10.1109/MCSE.2021.3059263), Computing in Science & Engineering, vol. 23, no. 2, pp. 7-14, 1 March-April 2021, doi: 10.1109/MCSE.2021.3059263 [Authorea preprint](https://www.authorea.com/users/394563/articles/508007-jupyter-thinking-and-storytelling-with-code-and-data)  (2021)\n",
+    "\n",
+    "- Hans Fangohr, Marijan Beg, et al, [*Data exploration and analysis with Jupyter notebooks*](http://accelconf.web.cern.ch/icalepcs2019/doi/JACoW-ICALEPCS2019-TUCPR02.html), Proceedings of the 17th International Conference on Accelerator and Large Experimental Physics Control Systems ICALEPCS2019, TUCPR02, doi: 10.18429/JACoW-ICALEPCS2019-TUCPR02 ([pdf](https://jacow.org/icalepcs2019/papers/tucpr02.pdf)) (2020)\n",
+    "\n",
+    "- Marijan Beg; Juliette Belin; Thomas Kluyver; Alexander Konovalov; Min Ragan-Kelley; Nicolas Thiery; Hans Fangohr. [*Using Jupyter for Reproducible Scientific Workflows*](https://ieeexplore.ieee.org/document/9325550) in Computing in Science & Engineering, vol. 23, no. 2, pp. 36-46, 1 March-April 2021, doi: 10.1109/MCSE.2021.3052101 [arXiv preprint](https://arxiv.org/abs/2102.09562) (2021)\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {