updates

index.ipynb

@@ -414,7 +414,7 @@
    "source": [
     "# Scientific Programming Lab\n",
     "\n",
-    "**Data Science master @University of Trento** - AA 2018/19 \n",
+    "**Data Science master @University of Trento** - AA 2019/20\n",
     "\n"
    ]
   },
@@ -541,7 +541,7 @@
    "source": [
     "**Part A expectations**: performance does _not_ matters: if you are able to run the required algorithm on your computer and the tests pass, it should be fine. Just be careful when given a 100Mb file, in that case sometimes bad code may lead to very slow execution and/or clog the memory. \n",
     "\n",
-    "In particular, in lab computers the whole system can even hang, so watchou for errors such as:\n",
+    "In particular, in lab computers the whole system can even hang, so watch out for errors such as:\n",
     "\n",
     "- infinite `while` which keeps adding new elements to lists - whenever possible, prefer `for` loops\n",
     "- scanning a big pandas dataframe using a `for in` instead of pandas native transformations \n",

slides.ipynb

@@ -146,9 +146,15 @@
     "* [Second Google Colab](https://drive.google.com/open?id=1_McK48wpvTD3CVizIafavYcbt6EVZbgf)\n",
     " (for code shown during lesson)\n",
     "\n",
-    "* Lesson: [Python basics](exercises/basics/basics-solution.ipynb)\n",
-    "* Lesson: [strings (maybe, file IN PROGRESS)](exercises/strings/strings-solution.ipynb)"
+    "* Lesson: [Python basics](exercises/basics/basics-solution.ipynb)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {