tweaks

README.md

@@ -14,7 +14,7 @@ Materials for DATS0001 Foundations of Data Science, ULiège, Fall 2023.
 | September 18 | _No class_ |
 | September 25 | [Course syllabus](https://glouppe.github.io/dats0001-foundations-of-data-science/?p=course-syllabus.md)<br> Lecture 1: Introduction<br>`nb01`: Build, compute, critique, repeat [[notebook](./nb01-box-loop.ipynb)]<br>Reading: Blei, [Build, Compute, Critique, Repeat](http://www.cs.columbia.edu/~blei/fogm/2020F/readings/Blei2014.pdf), 2014 [Section 1]<br>Reading: Box, [Science and Statistics](https://www.jstor.org/stable/2286841), 1976|
 | October 2 | Lecture 2: Data<br>`nb02a`: Tables [[notebook](./nb02a-tables.ipynb)]<br>`nb02b`: JAX [[notebook](./nb02b-jax.ipynb)]<br>`nb02c`: Data wrangling [[notebook](./nb02c-data-wrangling.ipynb)]<br>Reading: Harris et al, [Array programming with NumPy](https://www.nature.com/articles/s41586-020-2649-2), 2020 |
-| October 9 | Lecture 3: Visualization <br>`nb03a`: Plots [[notebook](./nb03a-plots.ipynb)]<br>`nb03b`: Data visualization principles [[notebook](./nb03b-visualization.ipynb)]<br>`nb03c`: High-dimensional data [[notebook](./nb03c-high-dimensional-data.ipynb)]<br>Reading: Rougier et al, [Ten Simple Rules for Better Figures](https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003833&type=printable), 2014  |
+| October 9 | Lecture 3: Visualization <br>`nb03a`: Plots [[notebook](./nb03a-plots.ipynb)]<br>`nb03b`: Data visualization principles [[notebook](./nb03b-visualization.ipynb)]<br>`nb03c`: High-dimensional data [[notebook](./nb03c-high-dimensional-data.ipynb)]<br>Reading: Rougier et al, [Ten Simple Rules for Better Figures](https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003833&type=printable), 2014<br>Reading: Rougier, [Scientific Visualization: Python+Matplotlib](https://github.com/rougier/scientific-visualization-book), 2022 |
 | October 16 | _No class_ |
 | October 23 | Lecture 4: Bayesian modeling <br>`nb04`: Latent variable models [[notebook](./nb04-latent-variable-models.ipynb)]<br>Reading: Gelman et al, [Bayesian workflow](https://arxiv.org/abs/2011.01808), 2020 [Sections 1 and 2]<br>Reading: Blei, [Build, Compute, Critique, Repeat](http://www.cs.columbia.edu/~blei/fogm/2020F/readings/Blei2014.pdf), 2014 [Sections 2 and 3] |
 | October 30 | Lecture 5: Spatial and temporal models<br>`nb05`: Spatial and temporal models [notebook] | 

nb01-box-loop.ipynb

@@ -11,7 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](./figures/nb01/boxloop.png)"
+    "![](./figures/nb01/boxloop.png)\n",
+    "\n",
+    "In this course, we will approach data science problems using the scientific method. We will build models, compute results, and critique our work. We will repeat this process until we are satisfied with our results. This process is called the **Box's Loop**. Upcoming lectures will dive into each of these steps in more detail."
    ]
   },
   {