Update markers on solutions with small edits (#140)

Author: stijnvanhoey

_solved/case1_bike_count.ipynb

@@ -59,7 +59,7 @@
    "source": [
     "The data were previously available on the open data portal of the city, and we downloaded them in the `CSV` format, and provided the original file as `data/fietstellingencoupure.csv`.\n",
     "\n",
-    "This data set contains the historical data of the bike counters, and consists of the following columns:\n",
+    "This dataset contains the historical data of the bike counters, and consists of the following columns:\n",
     "\n",
     "- The first column `datum` is the date, in `dd/mm/yy` format\n",
     "- The second column `tijd` is the time of the day, in `hh:mm` format\n",
@@ -633,7 +633,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, when we already know the format of the dates (and if this is consistent throughout the full data set), we can use this information to interpret the dates:"
+    "However, when we already know the format of the dates (and if this is consistent throughout the full dataset), we can use this information to interpret the dates:"
    ]
   },
   {
@@ -673,7 +673,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Write the data set cleaning as a function\n",
+    "### Write the dataset cleaning as a function\n",
     "\n",
     "In order to make it easier to reuse the code for the pre-processing we have implemented, let's convert the code to a Python function:"
    ]
@@ -817,7 +817,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Store our collected data set as an interim data product"
+    "### Store our collected dataset as an interim data product"
    ]
   },
   {
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now have a cleaned-up data set of the bike counts at Coupure Links in Ghent (Belgium). Next, we want to get an impression of the characteristics and properties of the data"
+    "We now have a cleaned-up dataset of the bike counts at Coupure Links in Ghent (Belgium). Next, we want to get an impression of the characteristics and properties of the data"
    ]
   },
   {
@@ -1549,11 +1549,8 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "clear_cell": false,
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "clear_cell": true,
+    "tags": []
    },
    "outputs": [
     {
@@ -1813,7 +1810,7 @@
     "\n",
     "**EXERCISE**\n",
     "\n",
-    "- Select a subset of the data set from 2013-12-31 12:00:00 until 2014-01-01 12:00:00 and assign the result to a new variable `newyear`\n",
+    "- Select a subset of the dataset from 2013-12-31 12:00:00 until 2014-01-01 12:00:00 and assign the result to a new variable `newyear`\n",
     "- Plot the selected data `newyear`.\n",
     "- Use a `rolling` function with a window of 10 values (check documentation of the function) to smooth the data of this period and make a plot of the smoothed version.\n",
     "\n",