diff --git a/.nojekyll b/.nojekyll
index 42a28e0..e12a212 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-c7ac9b71
\ No newline at end of file
+37cb911a
\ No newline at end of file
diff --git a/content/labs/Lab_7/IM939_Lab7-Part3.html b/content/labs/Lab_7/IM939_Lab7-Part3.html
index 87cfbfd..80cddff 100644
--- a/content/labs/Lab_7/IM939_Lab7-Part3.html
+++ b/content/labs/Lab_7/IM939_Lab7-Part3.html
@@ -1233,23 +1233,23 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <div class="cell-output cell-output-display column-page-right" data-execution_count="13">
 
 <style>
-  #altair-viz-815ca32eacd041c19256ab23c94783f7.vega-embed {
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 </style>
-<div id="altair-viz-815ca32eacd041c19256ab23c94783f7"></div>
+<div id="altair-viz-20d9baccd27c4bc4b97bdc179fd27d04"></div>
 <script type="text/javascript">
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-      outputDiv = document.getElementById("altair-viz-815ca32eacd041c19256ab23c94783f7");
+    if (outputDiv.id !== "altair-viz-20d9baccd27c4bc4b97bdc179fd27d04") {
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     const paths = {
       "vega": "https://cdn.jsdelivr.net/npm/vega@5?noext",
@@ -1330,23 +1330,23 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <div class="cell-output cell-output-display column-page-right" data-execution_count="14">
 
 <style>
-  #altair-viz-782bfbab8f1a46bf94e3eb500ec79675.vega-embed {
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-  #altair-viz-782bfbab8f1a46bf94e3eb500ec79675.vega-embed details summary {
+  #altair-viz-1cf15e7254714032b9a3c5d1eeddb178.vega-embed details,
+  #altair-viz-1cf15e7254714032b9a3c5d1eeddb178.vega-embed details summary {
     position: relative;
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 </style>
-<div id="altair-viz-782bfbab8f1a46bf94e3eb500ec79675"></div>
+<div id="altair-viz-1cf15e7254714032b9a3c5d1eeddb178"></div>
 <script type="text/javascript">
   var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
   (function(spec, embedOpt){
     let outputDiv = document.currentScript.previousElementSibling;
-    if (outputDiv.id !== "altair-viz-782bfbab8f1a46bf94e3eb500ec79675") {
-      outputDiv = document.getElementById("altair-viz-782bfbab8f1a46bf94e3eb500ec79675");
+    if (outputDiv.id !== "altair-viz-1cf15e7254714032b9a3c5d1eeddb178") {
+      outputDiv = document.getElementById("altair-viz-1cf15e7254714032b9a3c5d1eeddb178");
     }
     const paths = {
       "vega": "https://cdn.jsdelivr.net/npm/vega@5?noext",
@@ -1658,23 +1658,23 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <div class="cell-output cell-output-display" data-execution_count="21">
 
 <style>
-  #altair-viz-6efcb909f2314538aeb4dc72d767f099.vega-embed {
+  #altair-viz-b52b6c969a364ab4b324f618b0717acf.vega-embed {
     width: 100%;
     display: flex;
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-  #altair-viz-6efcb909f2314538aeb4dc72d767f099.vega-embed details summary {
+  #altair-viz-b52b6c969a364ab4b324f618b0717acf.vega-embed details,
+  #altair-viz-b52b6c969a364ab4b324f618b0717acf.vega-embed details summary {
     position: relative;
   }
 </style>
-<div id="altair-viz-6efcb909f2314538aeb4dc72d767f099"></div>
+<div id="altair-viz-b52b6c969a364ab4b324f618b0717acf"></div>
 <script type="text/javascript">
   var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
   (function(spec, embedOpt){
     let outputDiv = document.currentScript.previousElementSibling;
-    if (outputDiv.id !== "altair-viz-6efcb909f2314538aeb4dc72d767f099") {
-      outputDiv = document.getElementById("altair-viz-6efcb909f2314538aeb4dc72d767f099");
+    if (outputDiv.id !== "altair-viz-b52b6c969a364ab4b324f618b0717acf") {
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     const paths = {
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@@ -1757,23 +1757,23 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <div class="cell-output cell-output-display" data-execution_count="22">
 
 <style>
-  #altair-viz-31bb4bc557824a2ab033ea161f5b55f8.vega-embed {
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     position: relative;
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 </style>
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+<div id="altair-viz-3135474ccb004d5e854de8d20daa4ff5"></div>
 <script type="text/javascript">
   var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
   (function(spec, embedOpt){
     let outputDiv = document.currentScript.previousElementSibling;
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-      outputDiv = document.getElementById("altair-viz-31bb4bc557824a2ab033ea161f5b55f8");
+    if (outputDiv.id !== "altair-viz-3135474ccb004d5e854de8d20daa4ff5") {
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     const paths = {
       "vega": "https://cdn.jsdelivr.net/npm/vega@5?noext",
@@ -2055,11 +2055,11 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 </div>
 <div class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Add the data</span></span>
-<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>geo_states_england <span class="op">=</span> geo_states_england.merge(df_geography, left_on <span class="op">=</span> <span class="st">'CTYUA23CD'</span>, right_on <span class="op">=</span> <span class="st">'Code'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>geo_states_england_merged <span class="op">=</span> geo_states_england.merge(df_geography, left_on <span class="op">=</span> <span class="st">'CTYUA23CD'</span>, right_on <span class="op">=</span> <span class="st">'Code'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Check the merged data</span></span>
-<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>geo_states_england.head(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>geo_states_england_merged.head(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
 <div>
 
@@ -2208,7 +2208,7 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 </div>
 <div class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's plot the GPG by geography now</span></span>
-<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>post_GPG_England <span class="op">=</span> alt.Chart(geo_states_england, title<span class="op">=</span><span class="st">'GPG by region - England'</span>).mark_geoshape().encode(</span>
+<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>post_GPG_England <span class="op">=</span> alt.Chart(geo_states_england_merged, title<span class="op">=</span><span class="st">'GPG by region - England'</span>).mark_geoshape().encode(</span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>    color<span class="op">=</span><span class="st">'GPGmedian'</span>,</span>
 <span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>    tooltip<span class="op">=</span>[<span class="st">'Description'</span>, <span class="st">'GPGmedian'</span>]</span>
 <span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>).properties(</span>
@@ -2222,23 +2222,23 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <div class="cell-output cell-output-display" data-execution_count="28">
 
 <style>
-  #altair-viz-8aa20826d4e24624a2a09610e5ca9c3f.vega-embed {
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     width: 100%;
     display: flex;
   }
 
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-  #altair-viz-8aa20826d4e24624a2a09610e5ca9c3f.vega-embed details summary {
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+  #altair-viz-7f5f5f94d5134870aa0e96a7cd1e10e6.vega-embed details summary {
     position: relative;
   }
 </style>
-<div id="altair-viz-8aa20826d4e24624a2a09610e5ca9c3f"></div>
+<div id="altair-viz-7f5f5f94d5134870aa0e96a7cd1e10e6"></div>
 <script type="text/javascript">
   var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
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-      outputDiv = document.getElementById("altair-viz-8aa20826d4e24624a2a09610e5ca9c3f");
+    if (outputDiv.id !== "altair-viz-7f5f5f94d5134870aa0e96a7cd1e10e6") {
+      outputDiv = document.getElementById("altair-viz-7f5f5f94d5134870aa0e96a7cd1e10e6");
     }
     const paths = {
       "vega": "https://cdn.jsdelivr.net/npm/vega@5?noext",
@@ -2295,23 +2295,23 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <div class="cell-output cell-output-display column-page" data-execution_count="29">
 
 <style>
-  #altair-viz-7468ae69e1a5431e9915da92c5cceaeb.vega-embed {
+  #altair-viz-e5acef868d50445190a1eca9c7f7a72a.vega-embed {
     width: 100%;
     display: flex;
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-  #altair-viz-7468ae69e1a5431e9915da92c5cceaeb.vega-embed details summary {
+  #altair-viz-e5acef868d50445190a1eca9c7f7a72a.vega-embed details,
+  #altair-viz-e5acef868d50445190a1eca9c7f7a72a.vega-embed details summary {
     position: relative;
   }
 </style>
-<div id="altair-viz-7468ae69e1a5431e9915da92c5cceaeb"></div>
+<div id="altair-viz-e5acef868d50445190a1eca9c7f7a72a"></div>
 <script type="text/javascript">
   var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
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-      outputDiv = document.getElementById("altair-viz-7468ae69e1a5431e9915da92c5cceaeb");
+    if (outputDiv.id !== "altair-viz-e5acef868d50445190a1eca9c7f7a72a") {
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     const paths = {
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diff --git a/content/sessions/session-08_WorkshopBrief.html b/content/sessions/session-08_WorkshopBrief.html
index 14d7abe..f7661e0 100644
--- a/content/sessions/session-08_WorkshopBrief.html
+++ b/content/sessions/session-08_WorkshopBrief.html
@@ -573,12 +573,23 @@ <h4 data-number="36.2.1.2" class="anchored" data-anchor-id="potential-situations
 <li>Patient looking for self help – how to visualise information to inform them?</li>
 <li>GP giving advice to a patient – how to visualise information to support their conversation?</li>
 </ul>
-<p><strong><em>Tasks to complete</em></strong></p>
+<div class="callout callout-style-default callout-caution callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Tasks to complete
+</div>
+</div>
+<div class="callout-body-container callout-body">
 <ul class="task-list">
 <li><input type="checkbox"><strong>T1:</strong> Explore the datasets through the webpages above to get yourself familiar.</li>
 <li><input type="checkbox"><strong>T2:</strong> Identify a stakeholder and agree on a situation</li>
 <li><input type="checkbox"><strong>T3:</strong> Use this persona template from servicedesigntools.org [<a href="https://servicedesigntools.org/tools/personas">link to template page</a>] to help outline a stakeholder persona. Consider how you can help your stakeholder through data when responding to these challenges.</li>
 </ul>
+</div>
+</div>
 </section>
 </section>
 <section id="step-2-identify-analytical-questions-and-data" class="level3" data-number="36.2.2">
@@ -589,11 +600,22 @@ <h3 data-number="36.2.2" class="anchored" data-anchor-id="step-2-identify-analyt
 <li>Choose features that are relevant and speculate what you can achieve with them</li>
 <li>Consider if you can make use of any design thinking tools, some examples here: https://www.designkit.org/methods, for instance, you can reflect on this brainstorming guidance to help with your discussions as a group: https://www.designkit.org/methods/brainstorm</li>
 </ul>
-<p><strong><em>Tasks to complete</em></strong></p>
+<div class="callout callout-style-default callout-caution callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Tasks to complete
+</div>
+</div>
+<div class="callout-body-container callout-body">
 <ul class="task-list">
 <li><input type="checkbox"><strong>T4:</strong> Develop one or two analytical questions that you want to address</li>
 <li><input type="checkbox"><strong>T5:</strong> Clarify which data variables you will be using in your analysis in order to answer your research question</li>
 </ul>
+</div>
+</div>
 </section>
 <section id="step-3-plan-an-analysis-approachstrategy" class="level3" data-number="36.2.3">
 <h3 data-number="36.2.3" class="anchored" data-anchor-id="step-3-plan-an-analysis-approachstrategy"><span class="header-section-number">36.2.3</span> Step-3: Plan an analysis approach/strategy</h3>
@@ -605,10 +627,21 @@ <h3 data-number="36.2.3" class="anchored" data-anchor-id="step-3-plan-an-analysi
 <li>Also identify which data variables could be used with which technique.</li>
 <li>You can already use a Jupyter Notebook document as a template. Set some headers to indicate the different stages of your analysis.</li>
 </ul>
-<p><strong><em>Tasks to complete</em></strong></p>
+<div class="callout callout-style-default callout-caution callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Tasks to complete
+</div>
+</div>
+<div class="callout-body-container callout-body">
 <ul class="task-list">
 <li><input type="checkbox"><strong>T6:</strong> Develop an analysis plan on which techniques you want to use and which data variables you will incorporate.</li>
 </ul>
+</div>
+</div>
 </section>
 <section id="step-4-conduct-your-analysis" class="level3" data-number="36.2.4">
 <h3 data-number="36.2.4" class="anchored" data-anchor-id="step-4-conduct-your-analysis"><span class="header-section-number">36.2.4</span> Step-4: Conduct your analysis</h3>
@@ -618,10 +651,21 @@ <h3 data-number="36.2.4" class="anchored" data-anchor-id="step-4-conduct-your-an
 <li>Think of how you can visualise the data and the results from the tools you use</li>
 <li>Make use of existing Jupyter Notebooks and pieces of code from the labs</li>
 </ul>
-<p><strong><em>Tasks to complete</em></strong></p>
+<div class="callout callout-style-default callout-caution callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Tasks to complete
+</div>
+</div>
+<div class="callout-body-container callout-body">
 <ul class="task-list">
 <li><input type="checkbox"><strong>T7:</strong> Conduct your analysis using Jupyter Notebook by documenting your decisions as you go along.</li>
 </ul>
+</div>
+</div>
 </section>
 <section id="step-5-presenting-the-results-note-to-be-presented-on-week-10" class="level3" data-number="36.2.5">
 <h3 data-number="36.2.5" class="anchored" data-anchor-id="step-5-presenting-the-results-note-to-be-presented-on-week-10"><span class="header-section-number">36.2.5</span> Step-5: Presenting the results (Note: To be presented on Week-10)</h3>
@@ -635,10 +679,21 @@ <h3 data-number="36.2.5" class="anchored" data-anchor-id="step-5-presenting-the-
 <li>Present some results from your analysis</li>
 </ul></li>
 </ul>
-<p><strong><em>Tasks to complete</em></strong></p>
+<div class="callout callout-style-default callout-caution callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Tasks to complete
+</div>
+</div>
+<div class="callout-body-container callout-body">
 <ul class="task-list">
 <li><input type="checkbox"><strong>T8:</strong> Prepare to present your results</li>
 </ul>
+</div>
+</div>
 <div class="callout callout-style-default callout-note callout-titled">
 <div class="callout-header d-flex align-content-center">
 <div class="callout-icon-container">
diff --git a/search.json b/search.json
index f3f2fa3..8effdb7 100644
--- a/search.json
+++ b/search.json
@@ -1019,7 +1019,7 @@
     "href": "content/labs/Lab_7/IM939_Lab7-Part3.html#geography",
     "title": "34  Lab: Gender gaps",
     "section": "34.4 Geography",
-    "text": "34.4 Geography\n\n34.4.0.1 But first:\nSince moving on from our Iris and Wine datasets, the real-world datasets rarely come prepared (ready to use).\n\nIf you download the zip file for the latest 2023 Pay Gap ONS statistics, you will notice that they have color-coded their cells based on the certainty of estimates. On the one hand, this is very good practice - being transparent about the quality of the data. On the other hand, it tells us that we need to be careful about what insights we can draw from the data.\nIf you look at the statistics for one year, you can get a glimpse of what’s happening across various categories (geography, age, profession, etc.) in terms of GPG but it’s a cross-sectional view. But you can collate a longitudinal view should you wish to. E.g., by downloading the zip folders across the desired years and collating the information for desired categories for multiple years. But remember that this will be a ‘simplified approach’ to a longitudinal view and will have limitations. Also, recollect one of the figures Cagatay showed in the earlier lectures - it’s common to spend a lot of time at the start of your Data Science project just collating the necessary information. If you fancy, you can write a script to automate the data collation process!\nWe have geography information as area codes from the ONS source, but wouldn’t it be nice if we are able to visualise GPG by Geography on a map of England (with Levelling Up agenda and all). That’s the data hunt I went on. And the ONS’ Geodata portal provides datasets from the different administrative boundaries, so I downloaded this one:Counties and Unitary Authorities (May 2023) Boundaries UK BUC. Now let’s see what visualisation we can create with it.\n\n\n\n\n\n\n\nTakeaway\n\n\n\nCollating data from multiple sources is a significant, valuable and legitimate part of the Data Science project journey\n\n\n\n# Getting the geospatial polygons for England\nimport geopandas as gpd \nimport altair as alt\n\ngeo_states_england = gpd.read_file('data/Counties_and_Unitary_Authorities_May_2023_UK_BUC_-7406349609691062173.gpkg')\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nCTYUA23NMW\nBNG_E\nBNG_N\nLONG\nLAT\nGlobalID\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n\n447160\n531474\n-1.27018\n54.676102\n{224B1BB0-27FA-4B44-AD01-F22525CE232E}\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n\n451141\n516887\n-1.21099\n54.544701\n{8A06DF87-1F09-4A1C-9D6E-A32D40A0B159}\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n\n464361\n519597\n-1.00608\n54.567501\n{4A930CE8-4656-4A98-880E-8110EE3D8501}\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n\n444940\n518183\n-1.30664\n54.556900\n{304224A1-E808-4BF2-8F3E-AC43B0368BE8}\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n\n428029\n515648\n-1.56835\n54.535301\n{F7BBD06A-7E09-4832-90D0-F6CA591D4A1D}\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\nprint(geo_states_england.columns)\n\nIndex(['CTYUA23CD', 'CTYUA23NM', 'CTYUA23NMW', 'BNG_E', 'BNG_N', 'LONG', 'LAT',\n       'GlobalID', 'geometry'],\n      dtype='object')\n\n\n\n# Let's drop the columns we don't need\ngeo_states_england = geo_states_england.drop(['CTYUA23NMW', 'BNG_E', 'BNG_N', 'GlobalID'], axis=1)\n\n\n# Let's check again\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(geo_states_england, title='Map of England').mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n)\npre_GPG_England\n\n\n\n\n\n\n\nWait, what’s that?! That’s not what we were expecting!\n\n\n\n\n\n\nMap projections\n\n\n\nBecause the Earth is round, and maps are flat, geospatial data needs to be “projected”. There are many types of projecting geospatial data, and all of them come with some tradeoff in terms of distorting area and/or distance (in other words, none of them are perfect). You can read more here.\nNow, the geospatial dataset that we are using for this notebook was downloaded from the Office for National Statistics’ Geoportal and uses a Coordinate Reference System (CRS) known as EPSG:27700 - OSGB36 / British National Grid. Regretfully, Altair works with a different CRS: WGS 84 (also known as epsg:4326), and this is creating the conflict.\nWe have two options: either reproject our data using geopandas, or according to Altair documentation try using the project configuration (type: 'identity', reflectY': True). It draws the geometries without applying a projection.\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(\n    geo_states_england, title='Map of England'\n).mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npre_GPG_England\n\n\n\n\n\n\n\n\ndf_geography.info()\n\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nRangeIndex: 390 entries, 0 to 389\nData columns (total 4 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   Description  390 non-null    object \n 1   Code         390 non-null    object \n 2   GPGmedian    386 non-null    float64\n 3   GPGmean      387 non-null    float64\ndtypes: float64(2), object(2)\nmemory usage: 12.3+ KB\n\n\n\ngeo_states_england\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n...\n...\n...\n...\n...\n...\n\n\n213\nW06000020\nTorfaen\n-3.05101\n51.698399\nMULTIPOLYGON (((333723.000 192653.903, 330700....\n\n\n214\nW06000021\nMonmouthshire\n-2.90280\n51.778301\nMULTIPOLYGON (((329597.402 229251.797, 326793....\n\n\n215\nW06000022\nNewport\n-2.89769\n51.582298\nMULTIPOLYGON (((343091.833 184213.309, 342279....\n\n\n216\nW06000023\nPowys\n-3.43531\n52.348598\nMULTIPOLYGON (((322891.550 333139.949, 321104....\n\n\n217\nW06000024\nMerthyr Tydfil\n-3.36425\n51.748600\nMULTIPOLYGON (((308057.304 211036.201, 306294....\n\n\n\n\n218 rows × 5 columns\n\n\n\n\ndf_geography\n\n\n\n\n\n\n\n\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n1\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n2\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n3\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n4\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n...\n...\n...\n...\n...\n\n\n385\nSouth Lanarkshire\nS12000029\n6.1\n7.5\n\n\n386\nStirling\nS12000030\n7.4\n21.9\n\n\n387\nWest Dunbartonshire\nS12000039\n17.5\n12.8\n\n\n388\nWest Lothian\nS12000040\n8.3\n9.6\n\n\n389\nNorthern Ireland\nN92000002\n8.1\n9.6\n\n\n\n\n390 rows × 4 columns\n\n\n\n\n# Add the data\ngeo_states_england = geo_states_england.merge(df_geography, left_on = 'CTYUA23CD', right_on = 'Code')\n\n\n# Check the merged data\ngeo_states_england.head(10)\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n5\nE06000006\nHalton\n-2.68853\n53.334202\nMULTIPOLYGON (((358131.901 385425.802, 355191....\nHalton UA\nE06000006\n3.4\n4.6\n\n\n6\nE06000007\nWarrington\n-2.56167\n53.391602\nMULTIPOLYGON (((367582.201 396058.199, 367158....\nWarrington UA\nE06000007\n12.8\n14.6\n\n\n7\nE06000008\nBlackburn with Darwen\n-2.46360\n53.700802\nMULTIPOLYGON (((372966.498 423266.501, 371465....\nBlackburn with Darwen UA\nE06000008\n22.3\n15.3\n\n\n8\nE06000009\nBlackpool\n-3.02199\n53.821602\nMULTIPOLYGON (((333572.799 437130.702, 333041....\nBlackpool UA\nE06000009\n4.4\n3.5\n\n\n9\nE06000010\nKingston upon Hull, City of\n-0.30382\n53.769199\nMULTIPOLYGON (((515429.592 427689.472, 516047....\nKingston upon Hull UA\nE06000010\n16.1\n7.9\n\n\n\n\n\n\n\n\n# Let's plot the GPG by geography now\npost_GPG_England = alt.Chart(geo_states_england, title='GPG by region - England').mark_geoshape().encode(\n    color='GPGmedian',\n    tooltip=['Description', 'GPGmedian']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npost_GPG_England\n\n\n\n\n\n\n\n\n# side by side view\nGPG_England = pre_GPG_England | post_GPG_England\nGPG_England\n\n\n\n\n\n\n\nHow do the results in this workbook compare to the visualisation we saw during the lecture, for example, for the UK in Information is Beautiful But remember the earnings across the two might be for different years - do remember to check the metadata!"
+    "text": "34.4 Geography\n\n34.4.0.1 But first:\nSince moving on from our Iris and Wine datasets, the real-world datasets rarely come prepared (ready to use).\n\nIf you download the zip file for the latest 2023 Pay Gap ONS statistics, you will notice that they have color-coded their cells based on the certainty of estimates. On the one hand, this is very good practice - being transparent about the quality of the data. On the other hand, it tells us that we need to be careful about what insights we can draw from the data.\nIf you look at the statistics for one year, you can get a glimpse of what’s happening across various categories (geography, age, profession, etc.) in terms of GPG but it’s a cross-sectional view. But you can collate a longitudinal view should you wish to. E.g., by downloading the zip folders across the desired years and collating the information for desired categories for multiple years. But remember that this will be a ‘simplified approach’ to a longitudinal view and will have limitations. Also, recollect one of the figures Cagatay showed in the earlier lectures - it’s common to spend a lot of time at the start of your Data Science project just collating the necessary information. If you fancy, you can write a script to automate the data collation process!\nWe have geography information as area codes from the ONS source, but wouldn’t it be nice if we are able to visualise GPG by Geography on a map of England (with Levelling Up agenda and all). That’s the data hunt I went on. And the ONS’ Geodata portal provides datasets from the different administrative boundaries, so I downloaded this one:Counties and Unitary Authorities (May 2023) Boundaries UK BUC. Now let’s see what visualisation we can create with it.\n\n\n\n\n\n\n\nTakeaway\n\n\n\nCollating data from multiple sources is a significant, valuable and legitimate part of the Data Science project journey\n\n\n\n# Getting the geospatial polygons for England\nimport geopandas as gpd \nimport altair as alt\n\ngeo_states_england = gpd.read_file('data/Counties_and_Unitary_Authorities_May_2023_UK_BUC_-7406349609691062173.gpkg')\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nCTYUA23NMW\nBNG_E\nBNG_N\nLONG\nLAT\nGlobalID\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n\n447160\n531474\n-1.27018\n54.676102\n{224B1BB0-27FA-4B44-AD01-F22525CE232E}\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n\n451141\n516887\n-1.21099\n54.544701\n{8A06DF87-1F09-4A1C-9D6E-A32D40A0B159}\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n\n464361\n519597\n-1.00608\n54.567501\n{4A930CE8-4656-4A98-880E-8110EE3D8501}\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n\n444940\n518183\n-1.30664\n54.556900\n{304224A1-E808-4BF2-8F3E-AC43B0368BE8}\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n\n428029\n515648\n-1.56835\n54.535301\n{F7BBD06A-7E09-4832-90D0-F6CA591D4A1D}\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\nprint(geo_states_england.columns)\n\nIndex(['CTYUA23CD', 'CTYUA23NM', 'CTYUA23NMW', 'BNG_E', 'BNG_N', 'LONG', 'LAT',\n       'GlobalID', 'geometry'],\n      dtype='object')\n\n\n\n# Let's drop the columns we don't need\ngeo_states_england = geo_states_england.drop(['CTYUA23NMW', 'BNG_E', 'BNG_N', 'GlobalID'], axis=1)\n\n\n# Let's check again\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(geo_states_england, title='Map of England').mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n)\npre_GPG_England\n\n\n\n\n\n\n\nWait, what’s that?! That’s not what we were expecting!\n\n\n\n\n\n\nMap projections\n\n\n\nBecause the Earth is round, and maps are flat, geospatial data needs to be “projected”. There are many types of projecting geospatial data, and all of them come with some tradeoff in terms of distorting area and/or distance (in other words, none of them are perfect). You can read more here.\nNow, the geospatial dataset that we are using for this notebook was downloaded from the Office for National Statistics’ Geoportal and uses a Coordinate Reference System (CRS) known as EPSG:27700 - OSGB36 / British National Grid. Regretfully, Altair works with a different CRS: WGS 84 (also known as epsg:4326), and this is creating the conflict.\nWe have two options: either reproject our data using geopandas, or according to Altair documentation try using the project configuration (type: 'identity', reflectY': True). It draws the geometries without applying a projection.\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(\n    geo_states_england, title='Map of England'\n).mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npre_GPG_England\n\n\n\n\n\n\n\n\ndf_geography.info()\n\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nRangeIndex: 390 entries, 0 to 389\nData columns (total 4 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   Description  390 non-null    object \n 1   Code         390 non-null    object \n 2   GPGmedian    386 non-null    float64\n 3   GPGmean      387 non-null    float64\ndtypes: float64(2), object(2)\nmemory usage: 12.3+ KB\n\n\n\ngeo_states_england\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n...\n...\n...\n...\n...\n...\n\n\n213\nW06000020\nTorfaen\n-3.05101\n51.698399\nMULTIPOLYGON (((333723.000 192653.903, 330700....\n\n\n214\nW06000021\nMonmouthshire\n-2.90280\n51.778301\nMULTIPOLYGON (((329597.402 229251.797, 326793....\n\n\n215\nW06000022\nNewport\n-2.89769\n51.582298\nMULTIPOLYGON (((343091.833 184213.309, 342279....\n\n\n216\nW06000023\nPowys\n-3.43531\n52.348598\nMULTIPOLYGON (((322891.550 333139.949, 321104....\n\n\n217\nW06000024\nMerthyr Tydfil\n-3.36425\n51.748600\nMULTIPOLYGON (((308057.304 211036.201, 306294....\n\n\n\n\n218 rows × 5 columns\n\n\n\n\ndf_geography\n\n\n\n\n\n\n\n\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n1\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n2\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n3\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n4\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n...\n...\n...\n...\n...\n\n\n385\nSouth Lanarkshire\nS12000029\n6.1\n7.5\n\n\n386\nStirling\nS12000030\n7.4\n21.9\n\n\n387\nWest Dunbartonshire\nS12000039\n17.5\n12.8\n\n\n388\nWest Lothian\nS12000040\n8.3\n9.6\n\n\n389\nNorthern Ireland\nN92000002\n8.1\n9.6\n\n\n\n\n390 rows × 4 columns\n\n\n\n\n# Add the data\ngeo_states_england_merged = geo_states_england.merge(df_geography, left_on = 'CTYUA23CD', right_on = 'Code')\n\n\n# Check the merged data\ngeo_states_england_merged.head(10)\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n5\nE06000006\nHalton\n-2.68853\n53.334202\nMULTIPOLYGON (((358131.901 385425.802, 355191....\nHalton UA\nE06000006\n3.4\n4.6\n\n\n6\nE06000007\nWarrington\n-2.56167\n53.391602\nMULTIPOLYGON (((367582.201 396058.199, 367158....\nWarrington UA\nE06000007\n12.8\n14.6\n\n\n7\nE06000008\nBlackburn with Darwen\n-2.46360\n53.700802\nMULTIPOLYGON (((372966.498 423266.501, 371465....\nBlackburn with Darwen UA\nE06000008\n22.3\n15.3\n\n\n8\nE06000009\nBlackpool\n-3.02199\n53.821602\nMULTIPOLYGON (((333572.799 437130.702, 333041....\nBlackpool UA\nE06000009\n4.4\n3.5\n\n\n9\nE06000010\nKingston upon Hull, City of\n-0.30382\n53.769199\nMULTIPOLYGON (((515429.592 427689.472, 516047....\nKingston upon Hull UA\nE06000010\n16.1\n7.9\n\n\n\n\n\n\n\n\n# Let's plot the GPG by geography now\npost_GPG_England = alt.Chart(geo_states_england_merged, title='GPG by region - England').mark_geoshape().encode(\n    color='GPGmedian',\n    tooltip=['Description', 'GPGmedian']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npost_GPG_England\n\n\n\n\n\n\n\n\n# side by side view\nGPG_England = pre_GPG_England | post_GPG_England\nGPG_England\n\n\n\n\n\n\n\nHow do the results in this workbook compare to the visualisation we saw during the lecture, for example, for the UK in Information is Beautiful But remember the earnings across the two might be for different years - do remember to check the metadata!"
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-    "text": "36.2 Tasks\nAs also described above, the expectation to utilise the relation between the health indicators and socio-demographics information.\n\n36.2.1 Step-1: Sketch a hypothetical scenario and a stakeholder\n\nIdentify a stakeholder and define the setting. Think about the personas and think about the context. Think about questions such as:\n\nWhat are the needs of the people? What do they know and what data is available?\nWhat do they need to know?\nWhat are some sensitivities?\n\n\n\n36.2.1.1 Potential stakeholder involved could be:\nPublic Health Officer, Local Authority Officer, Patient-facing Clinician (GP), Clinical researcher, Patient…\n\n\n36.2.1.2 Potential situations could be:\nConsider yourself/your team to be a data scientists aiming to support:\n\nA local authority\nClinical researcher talking to a public health officer – how can research outcome inform policy?\nPatient looking for self help – how to visualise information to inform them?\nGP giving advice to a patient – how to visualise information to support their conversation?\n\nTasks to complete\n\nT1: Explore the datasets through the webpages above to get yourself familiar.\nT2: Identify a stakeholder and agree on a situation\nT3: Use this persona template from servicedesigntools.org [link to template page] to help outline a stakeholder persona. Consider how you can help your stakeholder through data when responding to these challenges.\n\n\n\n\n36.2.2 Step-2: Identify analytical questions and data\n\nIdentify a number of analytical questions that you can address using the data available and using the analytical tools that we have available.\nIdentify the data fields that fit with your scenario\nChoose features that are relevant and speculate what you can achieve with them\nConsider if you can make use of any design thinking tools, some examples here: https://www.designkit.org/methods, for instance, you can reflect on this brainstorming guidance to help with your discussions as a group: https://www.designkit.org/methods/brainstorm\n\nTasks to complete\n\nT4: Develop one or two analytical questions that you want to address\nT5: Clarify which data variables you will be using in your analysis in order to answer your research question\n\n\n\n36.2.3 Step-3: Plan an analysis approach/strategy\n\nDecide on an analysis strategy, which methods will you use and how they can help you respond to your question.\n\nTry to decide on some high level strategies, e.g., “will develop a regression model predicting XXX using variables associated with YYY”, or “will apply some clustering algorithms on variables associated with XXX to look for some emerging groups”\n\nAlso identify which data variables could be used with which technique.\nYou can already use a Jupyter Notebook document as a template. Set some headers to indicate the different stages of your analysis.\n\nTasks to complete\n\nT6: Develop an analysis plan on which techniques you want to use and which data variables you will incorporate.\n\n\n\n36.2.4 Step-4: Conduct your analysis\n\nConduct the analysis following your plan. You might find yourself reflecting back on your decisions in the earlier steps and doing things differently to your plan.\nMake good use of exploratory analysis, you might find out that data is different to what you were expecting it to be or new patterns might emerge.\nThink of how you can visualise the data and the results from the tools you use\nMake use of existing Jupyter Notebooks and pieces of code from the labs\n\nTasks to complete\n\nT7: Conduct your analysis using Jupyter Notebook by documenting your decisions as you go along.\n\n\n\n36.2.5 Step-5: Presenting the results (Note: To be presented on Week-10)\n\nThink about how you will present your results, extract insights, and extract some key visualisations\nIf you need more time to work as a group on your analysis, arrange some time to meet and finalise some bits\nStart a slide-deck and prepare max three slides to:\n\nIntroduce your persona/context\nList your analytical questions\nPresent some results from your analysis\n\n\nTasks to complete\n\nT8: Prepare to present your results\n\n\n\n\n\n\n\nNote\n\n\n\nWe’ll tell you how to get your slides to us"
+    "text": "36.2 Tasks\nAs also described above, the expectation to utilise the relation between the health indicators and socio-demographics information.\n\n36.2.1 Step-1: Sketch a hypothetical scenario and a stakeholder\n\nIdentify a stakeholder and define the setting. Think about the personas and think about the context. Think about questions such as:\n\nWhat are the needs of the people? What do they know and what data is available?\nWhat do they need to know?\nWhat are some sensitivities?\n\n\n\n36.2.1.1 Potential stakeholder involved could be:\nPublic Health Officer, Local Authority Officer, Patient-facing Clinician (GP), Clinical researcher, Patient…\n\n\n36.2.1.2 Potential situations could be:\nConsider yourself/your team to be a data scientists aiming to support:\n\nA local authority\nClinical researcher talking to a public health officer – how can research outcome inform policy?\nPatient looking for self help – how to visualise information to inform them?\nGP giving advice to a patient – how to visualise information to support their conversation?\n\n\n\n\n\n\n\nTasks to complete\n\n\n\n\nT1: Explore the datasets through the webpages above to get yourself familiar.\nT2: Identify a stakeholder and agree on a situation\nT3: Use this persona template from servicedesigntools.org [link to template page] to help outline a stakeholder persona. Consider how you can help your stakeholder through data when responding to these challenges.\n\n\n\n\n\n\n36.2.2 Step-2: Identify analytical questions and data\n\nIdentify a number of analytical questions that you can address using the data available and using the analytical tools that we have available.\nIdentify the data fields that fit with your scenario\nChoose features that are relevant and speculate what you can achieve with them\nConsider if you can make use of any design thinking tools, some examples here: https://www.designkit.org/methods, for instance, you can reflect on this brainstorming guidance to help with your discussions as a group: https://www.designkit.org/methods/brainstorm\n\n\n\n\n\n\n\nTasks to complete\n\n\n\n\nT4: Develop one or two analytical questions that you want to address\nT5: Clarify which data variables you will be using in your analysis in order to answer your research question\n\n\n\n\n\n36.2.3 Step-3: Plan an analysis approach/strategy\n\nDecide on an analysis strategy, which methods will you use and how they can help you respond to your question.\n\nTry to decide on some high level strategies, e.g., “will develop a regression model predicting XXX using variables associated with YYY”, or “will apply some clustering algorithms on variables associated with XXX to look for some emerging groups”\n\nAlso identify which data variables could be used with which technique.\nYou can already use a Jupyter Notebook document as a template. Set some headers to indicate the different stages of your analysis.\n\n\n\n\n\n\n\nTasks to complete\n\n\n\n\nT6: Develop an analysis plan on which techniques you want to use and which data variables you will incorporate.\n\n\n\n\n\n36.2.4 Step-4: Conduct your analysis\n\nConduct the analysis following your plan. You might find yourself reflecting back on your decisions in the earlier steps and doing things differently to your plan.\nMake good use of exploratory analysis, you might find out that data is different to what you were expecting it to be or new patterns might emerge.\nThink of how you can visualise the data and the results from the tools you use\nMake use of existing Jupyter Notebooks and pieces of code from the labs\n\n\n\n\n\n\n\nTasks to complete\n\n\n\n\nT7: Conduct your analysis using Jupyter Notebook by documenting your decisions as you go along.\n\n\n\n\n\n36.2.5 Step-5: Presenting the results (Note: To be presented on Week-10)\n\nThink about how you will present your results, extract insights, and extract some key visualisations\nIf you need more time to work as a group on your analysis, arrange some time to meet and finalise some bits\nStart a slide-deck and prepare max three slides to:\n\nIntroduce your persona/context\nList your analytical questions\nPresent some results from your analysis\n\n\n\n\n\n\n\n\nTasks to complete\n\n\n\n\nT8: Prepare to present your results\n\n\n\n\n\n\n\n\n\nNote\n\n\n\nWe’ll tell you how to get your slides to us"
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