Eugene Yan · Tech & AI
TIER 4 2019-04-30
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<h1 class="title">What does a Data Scientist really do?</h1>
<p class="date">
<info datetime="2019-04-30 00:00:00 +0000">
<span class="no-italics">[
<a class='tag' href="/tag/career/">career</a>
<a class='tag' href="/tag/datascience/">datascience</a>
]
</span> · 8 min read
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<p>As a data scientist, I sometimes get approached by others on questions related to data science. This could be while at work, or at the meetups I organise and attend, or questions on my site or linkedIn. Through these interactions, I realised there is significant misunderstanding about data science. Misunderstandings arise around the skills needed to practice data science, as well as what data scientists actually do.</p>
<h2 id="perception-of-what-is-needed-and-done">Perception of what is needed and done</h2>
<p>Many people are of the perception that deep technical and programming abilities, olympiad level math skills, and a PhD are the minimum requirements, and that having such skills and education qualifications will guarantee success in the field. This is slightly unrealistic and misleading, and does not help to mitigate the issue of scarce data science talent, such as those listed in <a href="https://www.nytimes.com/2017/10/22/technology/artificial-intelligence-experts-salaries.html" target="_blank">The New York Times</a> and <a href="https://www.bloomberg.com/news/articles/2018-02-07/just-how-shallow-is-the-artificial-intelligence-talent-pool" target="_blank">Bloomberg</a>.</p>
<p>Similarly, based on my interactions with people, as well as comments online, many perceive that a data scientist’s main job is machine learning, or researching the latest neural network architectures—essentially, Kaggle as a full time job. However, machine learning is just a slice of what data scientists actually do (personally, I find it constitutes < 20% of my day to day work).</p>
<h2 id="how-do-these-perceptions-come-about">How do these perceptions come about?</h2>
<p>One hypothesis is the statical fallacy of availability. For the average population, they would probably know about data scientists based on what they’ve seen/heard on the news and articles, or perhaps a course or two on Coursera.</p>
<p>What’s likely to be the background of these data scientists? If it’s from this <a href="https://www.forbes.com/sites/nicolemartin1/2019/03/27/turing-award-and-1-million-given-to-3-ai-pioneers/#66295b2a4784" target="_blank">Forbes article</a> on the recent Turing Award for contributions in AI, you’ll find three very distinguished gentlemen who have amazing publishing records and introduced the world to neural networks, backpropogation, CNNs, and RNNs.</p>
<p>Or perhaps you read the recent <a href="https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii" target="_blank">Deepmind post</a> about how neural networks and reinforcement learning achieved human expert level performance, and found that the team was largely comprised of PhDs. If it’s from a course, the person is likely to have a PhD, and went through deep mathematical proofs on machine learning techniques. Thus, based on what you can think of, or what is available in memory, many people tend to have a skewed perception on what background a data scientist should have.</p>
<p>The same goes for what data scientists actually do. Most of the sexy headlines on data science involve using machine learning to solve (currently) unsolvable problems, everything from research-based (computer games) to very much applied (self-driving cars). In addition, given that the majority of data science courses are on machine learning, its no wonder that the statistical fallacy of availability would skew people towards thinking that machine learning is the be all end all.</p>
<h2 id="such-perceptions-are-mostly-incorrect">Such perceptions are (mostly) incorrect</h2>
<p>Firstly, yes, there are researchers in labs who spend 80% of their time training tens of the same neural network architecture and hope for convergence on some of them, publish breakthrough research papers, and build cool applications that involve the latest and greatest. Nonetheless, they probably constitute < 1% of the overall data science community.</p>
<p>For most data scientists, while machine learning is a critical aspect of their work, it is only part of it. In addition, the perceived requirement for deep technical and math skills, as well as a PhD, to be effective in data science, is naive.</p>
<p>In my years of experience, first as a data scientist, then as a data science lead, I’ve had the opportunity to hire and assess many data scientists, and observed first hand what is needed for effective data science. In addition, I’ve also reached out and interviewed many experts, people who are Chief Data Officers, Chief Data Scientists, CTOs, and Heads of Data Science—they too, disagree with the flawed public perception.</p>
<h2 id="so-what-do-data-scientists-actually-do">So what do Data Scientists actually do?</h2>
<p>To provide some context, I’ll reference the <a href="https://www.quora.com/What-is-data-science/answer/Michael-Hochster" target="_blank">commonly used distinction</a> between Type A and Type B data scientists.</p>
<ul>
<li>Type A: The A stands for Analysis. Such data scientists are primarily concerned with making sense of data, or working with it in a fairly static way. They are very similar to a statistician.</li>
<li>Type B: The B stands for Building. They share some statistical background with Type A, but are also strong programmers and may be trained software engineers. They are mostly interested in serving data “in production”.</li>
</ul>
<p>The following is tilted towards Type B Data Scientists, due to my personal background, the teams I’ve built, and the objectives I’ve had to achieve. For Type B, the desired outcomes of most data science efforts is a data product that delivers value, either via providing insight for decisions, or automated decision making.</p>
<p>The journey towards putting a data product into production may involve many steps, which include:</p>
<p><strong>Understanding the problem and context, and framing problem statement (framing)</strong></p>
<ul>
<li>Understanding the problem to solve, and the available data</li>
<li>Framing the task and scope</li>
<li>Identifying constraints (data refresh rate, data security, etc.)</li>
<li>Identifying desired outcomes (including optimisation metrics)</li>
<li>Identifying ethical risks (e.g., how would predictions be misused, either deliberately or accidentally)</li>
</ul>
<p><strong>Data acquisition, exploration, and preparation (infra)</strong></p>
<ul>
<li>Laying the foundation for robust analytics</li>
<li>Understanding how to collect more data if needed</li>
<li>Understanding the data, including errors in the data and how to fix them</li>
<li>Preparing the data, including filling nulls, handling outliers, formatting, etc.</li>
<li>Figuring out how to join data across multiple different sources, ensuring that the process is valid and correct</li>
<li>Visualizing the data and understanding underlying signals in the data</li>
</ul>
<p><strong>Building frameworks (e.g., validation) and pipelines (e.g., data preparation and ML experiments)</strong></p>
<ul>
<li>Building a proper validation framework (e.g., can we use random shuffle k-fold, or should a time-based split be used)</li>
<li>Building data processing pipelines to prepare data, sometimes involving big data</li>
<li>Performing statistical analysis to understand the relationships between variables in the data</li>
<li>Building feature processing pipelines to convert prepared data into ML ready format (e.g., all features should be numerics)</li>
<li>Building ML pipelines to allow you to run parallel experiments, record results, visualise them, etc.</li>
<li>Pipelines should be built such that they allow for easily change in data prep or feature engineering approaches via config files, instead of being hardcoded</li>
<li>The same goes for ML experiment pipelines—they should work for all model types, params, etc.</li>
</ul>
<p><strong>Running experiments, monitoring, and analysing (testing)</strong></p>
<ul>
<li>Assessing multiple broad approaches before deciding on a model to use (e.g., trees, regression, svms, neural networks)</li>
<li>Deciding how to model the data (e.g., for forecasting, should it be in batch, with hand crafted features, or take in sequential data)</li>
<li>Running experiments with numerous hyperparams to understand how model learns on the data</li>
<li>Running experiments with other “tricks” (e.g., data augmentation, data weighting, different objective functions, etc)</li>
<li>Analyse the model performance (e.g., learning curves, error analysis)</li>
<li>Assess for underfitting/overfitting</li>
<li>Running online experiments, etc.</li>
</ul>
<p><strong>Putting the data product into production (data products)</strong></p>
<ul>
<li>Ensuring data and machine learning pipelines are scalable and robust</li>
<li>Building personalised data products</li>
<li>Creating APIs for machine learning models</li>
<li>Determining how to schedule your pipelines</li>
<li>Monitoring and maintaining data product and models over time</li>
<li>Monitoring input data, and model validation results</li>
<li>Communicating results to the organization</li>
<li>Convincing decision makers of their results</li>
<li>Rollback planning for incorrect models</li>
<li>Ethics of how the data is being use</li>
</ul>
<p>As you may have noticed, machine learning makes up a (small) portion of what data scientists actually do. While not every step is necessary in every project, and not every data scientists will do every step, most aspects will be de facto in many data science projects/products.</p>
<h2 id="so-what-abilities-do-data-scientists-need">So what abilities do Data Scientists need?</h2>
<p>Given the above, we get a sense that a strong understanding of machine learning alone is insufficient in the data science process. Having additional deep technical, math, and programming skills are useful, but don’t encompass the full picture.</p>
<p>What exactly is needed then? In my quest to understand this, I interviewed many data science experts and leaders, with questions such as:</p>
<ul>
<li>“What do you think makes a rockstar data scientist?”</li>
<li>“What do the best data scientists on your team do? What are they like?”</li>
<li>“How do you measure success in data science?</li>
</ul>
<p>The overall answer to the questions was this—the best data scientists work with data to “deliver measurable value”.</p>
<p>For me, this was completely out of the left field. I had imagined the answer to be based on math, research, programming, cutting edge techniques, and developing new algorithms. While the answers from the experts/mentors were simple, it was not something that could be replicated in a straightforward manner. If it were programming and technical abilities, I could just practise more and get better at it. If it were math and algorithms, I would study more and practise. However, this was not the case.</p>
<p>How does one practise “using data to deliver measurable value”?</p>
<p>Thus, I began on my next journey to understand what was required. I’ll share what I’ve found in a later post.</p>
<br>
<p>If you found this useful, please cite this write-up as:</p>
<blockquote class="blockquote-citation">
<p>Yan, Ziyou. (Apr 2019). What does a Data Scientist really do?. eugeneyan.com.
https://eugeneyan.com/writing/what-does-a-data-scientist-really-do/.</p>
</blockquote>
<p>or</p>
<div class="citation"><pre><code>@article{yan2019scientist,
title = {What does a Data Scientist really do?},
author = {Yan, Ziyou},
journal = {eugeneyan.com},
year = {2019},
month = {Apr},
url = {https://eugeneyan.com/writing/what-does-a-data-scientist-really-do/}
}</code></pre>
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height: 3.3em; /* Current height: 3.3em. For 2 lines with 0.75em font & 1.5 line-height, calculated height would be 2.25em. */
color: var(--c-interactive); /* Use theme's interactive color */
}
/* This container will wrap the image and score, taking the original image's layout space. */
#algolia-related-products .ais-RelatedProducts-item .recommendation-image-container {
position: relative; /* For positioning the score absolutely within */
display: block; /* Matches original image display and ensures proper block layout */
width: calc(100% - 4px); /* Adopts width from original image styling */
margin: 2px; /* Adopts margin from original image styling */
margin-bottom: 8px; /* Adopts specific bottom margin from original image's inline style */
line-height: 0; /* Prevents unexpected space if child elements are treated as inline */
}
/* The image itself, now filling the container */
#algolia-related-products .ais-RelatedProducts-item .recommendation-image-container img {
display: block;
width: 100%; /* Fill the container's width */
max-width: 20em; /* Optional: retain original max-width constraint for the image content */
height: auto; /* Maintain aspect ratio by default */
max-height: 12em; /* Constrain image height (adjust as needed) */
object-fit: cover; /* Ensures image covers the allocated space, cropping if necessary */
margin: 0 auto; /* Center image if max-width kicks in and it's narrower than container */
}
/* The score overlay box */
#algolia-related-products .ais-RelatedProducts-item .recommendation-score {
position: absolute;
bottom: 3px; /* Padding from the bottom edge of the container */
right: 3px; /* Padding from the right edge of the container */
background-color: color-mix(in srgb, var(--c-background) 85%, var(--green) 15%); /* Theme-aware light green */
color: var(--green); /* Theme's green color for text */
padding: 3px 6px; /* Slightly adjusted padding */
font-family: 'Raleway', Helvetica, sans-serif;
font-size: 0.75em;
font-weight: bold;
border-radius: 10px; /* More rounded corners like the example */
border: 1px solid var(--green); /* Theme's green color for border */
line-height: 1; /* Critical for small text in a small box */
z-index: 10; /* Ensure it's above the image */
box-shadow: 0 1px 2px rgba(0,0,0,0.15); /* Softer shadow */
display: flex; /* To align icon and text nicely */
align-items: center; /* Vertically center icon and text */
}
/* Styling for the SVG icon within the score box */
#algolia-related-products .ais-RelatedProducts-item .recommendation-score-icon {
width: 0.9em; /* Scale with score's font size */
height: 0.9em;
vertical-align: -0.1em; /* Fine-tune vertical alignment */
margin-right: 4px; /* Space between icon and score number */
fill: var(--green); /* Theme's green color for icon */
}
/* Frequently Bought Together Widget Styles */
#algolia-fbt .ais-FrequentlyBoughtTogether-list {
display: flex;
flex-direction: row; /* Arrange items horizontally */
flex-wrap: nowrap; /* Prevent wrapping to new lines, if possible */
justify-content: flex-start; /* Align items to the start of the container */
padding-left: 0; /* Remove default list padding */
list-style-type: none; /* Remove list bullets */
margin: 0;
}
#algolia-fbt .ais-FrequentlyBoughtTogether-item {
width: 32%; /* Adjust for 3 items: 32% * 3 items + 2% * 2 margins = 100% */
margin-right: 2%;
box-sizing: border-box; /* Include padding and border in the element's total width */
/* Optional: Basic card styling (uncomment to use) */
border: 1px solid color-mix(in srgb, var(--c-background) 85%, var(--c-text) 15%); /* Theme-aware light grey border */
padding: 0; /* Remove overall card padding, will be handled by elements */
text-align: left; /* Or 'center' if you prefer */
background-color: var(--c-background);
border-radius: 4px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
#algolia-fbt .ais-FrequentlyBoughtTogether-item:last-child {
margin-right: 0; /* No margin for the last item in the row */
}
/* Styling for images within recommendation items */
#algolia-fbt .ais-FrequentlyBoughtTogether-item img {
display: block; /* Can help remove extra space below image */
width: calc(100% - 4px); /* Full width minus 2px L/R margins */
max-width: 100%; /* Ensures image does not exceed container if intrinsically smaller */
/* height: auto; -- Controlled by inline style's max-height and object-fit */
object-fit: cover; /* Ensure image covers the area, also in inline style */
margin: 2px; /* 2px margin on top, left, right. Bottom is overridden by inline style. */
/* margin-bottom: 8px; -- This is set by inline style in JS template */
}
/* Styling for the wrapper link to make the whole card clickable */
#algolia-fbt .ais-FrequentlyBoughtTogether-item a.ais-FrequentlyBoughtTogether-item-link-wrapper {
display: block; /* Make the link fill the list item */
text-decoration: none; /* Remove underline */
color: inherit; /* Use parent's text color */
}
#algolia-fbt .fbt-product-title {
font-family: 'Raleway', Helvetica, sans-serif;
font-size: 0.75em;
display: -webkit-box;
-webkit-line-clamp: 2; /* Limit to 2 lines for WebKit browsers */
line-clamp: 2; /* Standard property */
-webkit-box-orient: vertical;
overflow: hidden;
text-overflow: ellipsis;
padding: 0 7px 7px 7px; /* 0 top, 7px L/R/B for text area */
line-height: 1.5; /* Adjust for better readability */
height: 3.3em; /* Current height: 3.3em. For 2 lines with 0.75em font & 1.5 line-height, calculated height would be 2.25em. */
color: var(--c-interactive); /* Use theme's interactive color */
}
/* This container will wrap the image and score, taking the original image's layout space. */
#algolia-fbt .ais-FrequentlyBoughtTogether-item .recommendation-image-container {
position: relative; /* For positioning the score absolutely within */
display: block; /* Matches original image display and ensures proper block layout */
width: calc(100% - 4px); /* Adopts width from original image styling */
margin: 2px; /* Adopts margin from original image styling */
margin-bottom: 8px; /* Adopts specific bottom margin from original image's inline style */
line-height: 0; /* Prevents unexpected space if child elements are treated as inline */
}
/* The image itself, now filling the container */
#algolia-fbt .ais-FrequentlyBoughtTogether-item .recommendation-image-container img {
display: block;
width: 100%; /* Fill the container's width */
max-width: 20em; /* Optional: retain original max-width constraint for the image content */
height: auto; /* Maintain aspect ratio by default */
max-height: 12em; /* Constrain image height (adjust as needed) */
object-fit: cover; /* Ensures image covers the allocated space, cropping if necessary */
margin: 0 auto; /* Center image if max-width kicks in and it's narrower than container */
}
/* The score overlay box */
#algolia-fbt .ais-FrequentlyBoughtTogether-item .recommendation-score {
position: absolute;
bottom: 3px; /* Padding from the bottom edge of the container */
right: 3px; /* Padding from the right edge of the container */
background-color: color-mix(in srgb, var(--c-background) 85%, var(--green) 15%); /* Theme-aware light green */
color: var(--green); /* Theme's green color for text */
padding: 3px 6px; /* Slightly adjusted padding */
font-family: 'Raleway', Helvetica, sans-serif;
font-size: 0.75em;
font-weight: bold;
border-radius: 10px; /* More rounded corners like the example */
border: 1px solid var(--green); /* Theme's green color for border */
line-height: 1; /* Critical for small text in a small box */
z-index: 10; /* Ensure it's above the image */
box-shadow: 0 1px 2px rgba(0,0,0,0.15); /* Softer shadow */
display: flex; /* To align icon and text nicely */
align-items: center; /* Vertically center icon and text */
}
/* Styling for the SVG icon within the score box */
#algolia-fbt .ais-FrequentlyBoughtTogether-item .recommendation-score-icon {
width: 0.9em; /* Scale with score's font size */
height: 0.9em;
vertical-align: -0.1em; /* Fine-tune vertical alignment */
margin-right: 4px; /* Space between icon and score number */
fill: var(--green); /* Theme's green color for icon */
}
</style>
<script type="df208f2ee46166f8b9a9e059-text/javascript">
// Function to load a script and return a promise
function loadScript(src) {
return new Promise((resolve, reject) => {
if (document.querySelector(`script[src="${src}"]`)) {
resolve(); // Already loaded
return;
}
const script = document.createElement('script');
script.src = src;
script.onload = () => resolve();
script.onerror = () => reject(new Error(`Script load error for ${src}`));
document.head.appendChild(script);
});
}
function initAlgoliaRecommendations() {
Promise.all([
loadScript('https://cdn.jsdelivr.net/npm/algoliasearch@4/dist/algoliasearch-lite.umd.js'),
loadScript('https://cdn.jsdelivr.net/npm/instantsearch.js@4')
])
.then(() => {
// Initialize the Related Products widget
initRelatedProducts();
// Initialize the Frequently Bought Together widget
initFrequentlyBoughtTogether();
})
.catch(error => {
// Handle error silently
});
}
function initRelatedProducts() {
// Ensure container exists
if (!document.getElementById('algolia-related-products')) {
// Container not found, stopping initialization
return;
}
const recSearchClient = algoliasearch(
'2XJCLEABQD',
'b61ec4cb64bd32d62c053466fccbfa43'
);
const relatedSearch = instantsearch({
indexName: 'eugeneyan.com',
searchClient: recSearchClient,
clickAnalytics: true,
insights: true, // Enable insights for click tracking on recommendations
});
relatedSearch.addWidgets([
instantsearch.widgets.relatedProducts({
container: '#algolia-related-products',
objectIDs: ['/writing/what-does-a-data-scientist-really-do/'],
limit: 3,
queryParameters: {
attributesToRetrieve: ['title', 'url', 'image', 'score', '_score'], // Specify only needed attributes
attributesToHighlight: [], // Disable highlighting
attributesToSnippet: [] // Disable snippeting
},
translations: {
title: '', // Custom title is in _layouts/post.html
},
transformItems: function(items) {
const containerElement = document.getElementById('algolia-recs-container');
const relatedElement = document.getElementById('algolia-related-products');
// Hide the Related Products container element first if there are no items
if (items.length === 0 && relatedElement) {
relatedElement.style.display = 'none';
} else if (relatedElement) {
relatedElement.style.display = 'block';
}
// Then handle the main recommendations container visibility
if (items.length > 0 && containerElement) {
containerElement.style.display = 'block';
} else if (containerElement && !document.getElementById('algolia-fbt').hasChildNodes()) {
containerElement.style.display = 'none';
} else {
}
return items;
},
templates: {
header() { // Removed unused results, html parameters
// Return a PLAIN string for the header
return '<h4 class="algolia-recs-section-header">You Might Also Like (content-based)</h4>';
},
item: function(hit, { html, sendEvent }) { // Added sendEvent to params
const itemUrl = `${hit.url || '#'}`;
const indexName = 'eugeneyan.com'; // Get index name for insights
let imageUrl;
// Ensure hit.image is not null, undefined, or an empty/whitespace string before using it.
if (hit.image && typeof hit.image === 'string' && hit.image.trim() !== '') {
imageUrl = `/assets/og_image/${hit.image}`;
} else {
imageUrl = `/assets/og_image/default-v4.jpg`; // Default image
}
let scoreValue = null;
if (typeof hit.score === 'number') scoreValue = hit.score.toFixed(2);
else if (typeof hit._score === 'number') scoreValue = hit._score.toFixed(2);
// else if (typeof hit.your_custom_score_field === 'number') scoreValue = hit.your_custom_score_field.toFixed(2);
const scoreElement = scoreValue ? `<div class="recommendation-score"><svg viewbox="0 0 24 24" class="recommendation-score-icon" xmlns="http://www.w3.org/2000/svg"><path d="M16 6l2.29 2.29-4.88 4.88-4-4L2 16.59 3.41 18l6-6 4 4 6.3-6.29L22 12V6h-6z"></path></svg>${scoreValue}</div>` : '';
const imageAndScoreTag = `
<div class="recommendation-image-container">
<img src="${imageUrl}" alt="${hit.title || 'Recommendation cover image'}">
${scoreElement}
</div>`;
const title = hit.title || 'Untitled Post';
return `
<a href="${itemUrl}"
class="ais-RelatedProducts-item-link-wrapper"
onClick="${() => {
sendEvent('click', hit, 'Related Item Clicked');
}}"
>
${imageAndScoreTag}
<div class="related-product-title">${title}</div>
</a>
`;
},
empty(results, { html }) {
const containerElement = document.getElementById('algolia-recs-container');
const relatedElement = document.getElementById('algolia-related-products');
// Hide Related Products container since there are no results
if (relatedElement) {
relatedElement.style.display = 'none';
}
// Check if the FBT widget also has no items before hiding the main container
const fbtWidgetContainer = document.getElementById('algolia-fbt');
if (containerElement && (!fbtWidgetContainer || !fbtWidgetContainer.hasChildNodes())) {
containerElement.style.display = 'none';
}
// Return an empty string to prevent rendering 'undefined'
return '';
}
}
})
]);
relatedSearch.start();
}
function initFrequentlyBoughtTogether() {
// Ensure container exists
if (!document.getElementById('algolia-fbt')) {
// Container not found, stopping initialization
return;
}
const recSearchClient = algoliasearch(
'2XJCLEABQD',
'b61ec4cb64bd32d62c053466fccbfa43'
);
const fbtSearch = instantsearch({
indexName: 'eugeneyan.com',
searchClient: recSearchClient,
clickAnalytics: true,
insights: true, // Enable insights for click tracking on recommendations
});
fbtSearch.addWidgets([
instantsearch.widgets.frequentlyBoughtTogether({
container: '#algolia-fbt',
objectIDs: ['/writing/what-does-a-data-scientist-really-do/'],
limit: 3,
queryParameters: {
attributesToRetrieve: ['title', 'url', 'image', 'score', '_score'], // Specify only needed attributes
attributesToHighlight: [], // Disable highlighting
attributesToSnippet: [] // Disable snippeting
},
translations: {
title: '', // Custom title is in _layouts/post.html
},
transformItems: function(items) {
const containerElement = document.getElementById('algolia-recs-container');
if (items.length > 0 && containerElement) {
containerElement.style.display = 'block';
} else if (containerElement && !document.getElementById('algolia-related-products').hasChildNodes()) {
containerElement.style.display = 'none';
} else {
}
return items;
},
templates: {
header() {
// Return a PLAIN string for the header - only if we have items to display
// The header should not render if there's no content
return '<h4 class="algolia-recs-section-header">Frequently Read Together (behavioral-based)</h4>';
},
item: function(hit, { html, sendEvent }) {
const itemUrl = `${hit.url || '#'}`;
const indexName = 'eugeneyan.com'; // Get index name for insights
let imageUrl;
// Ensure hit.image is not null, undefined, or an empty/whitespace string before using it.
if (hit.image && typeof hit.image === 'string' && hit.image.trim() !== '') {
imageUrl = `/assets/og_image/${hit.image}`;
} else {
imageUrl = `/assets/og_image/default-v4.jpg`; // Default image
}
let scoreValue = null;
if (typeof hit.score === 'number') scoreValue = hit.score.toFixed(2);
else if (typeof hit._score === 'number') scoreValue = hit._score.toFixed(2);
// else if (typeof hit.your_custom_score_field === 'number') scoreValue = hit.your_custom_score_field.toFixed(2);
const scoreElement = scoreValue ? `<div class="recommendation-score"><svg viewbox="0 0 24 24" class="recommendation-score-icon" xmlns="http://www.w3.org/2000/svg"><path d="M16 6l2.29 2.29-4.88 4.88-4-4L2 16.59 3.41 18l6-6 4 4 6.3-6.29L22 12V6h-6z"></path></svg>${scoreValue}</div>` : '';
const imageAndScoreTag = `
<div class="recommendation-image-container">
<img src="${imageUrl}" alt="${hit.title || 'Recommendation cover image'}">
${scoreElement}
</div>`;
const title = hit.title || 'Untitled Post';
return `
<a href="${itemUrl}"
class="ais-FrequentlyBoughtTogether-item-link-wrapper"
onClick="${() => {
sendEvent('click', hit, 'FBT Item Clicked');
}}"
>
${imageAndScoreTag}
<div class="fbt-product-title">${title}</div>
</a>
`;
},
empty(results, { html }) {
const containerElement = document.getElementById('algolia-recs-container');
const fbtElement = document.getElementById('algolia-fbt');
// Hide FBT container since there are no results
if (fbtElement) {
fbtElement.style.display = 'none';
}
// Check if the Related Products widget also has no items before hiding the main container
const relatedWidgetContainer = document.getElementById('algolia-related-products');
if (containerElement && (!relatedWidgetContainer || !relatedWidgetContainer.hasChildNodes())) {
containerElement.style.display = 'none';
}
// Return an empty string to prevent rendering 'undefined'
return '';
}
}
})
]);
fbtSearch.start();
}
document.addEventListener('DOMContentLoaded', function() {
let recsLoaded = false;
function checkLoad() {
if (recsLoaded) return;
if ((window.scrollY + window.innerHeight) >= document.body.scrollHeight - 500) {
recsLoaded = true;
window.removeEventListener('scroll', checkLoad);
initAlgoliaRecommendations();
}
}
window.addEventListener('scroll', checkLoad, { passive: true });
checkLoad();
});
</script>
</div>
<br>
<!-- <div id="algolia-recs-container" style="display: none;">
<div id="algolia-related-products" style="margin-bottom: 2em;"></div>
<style>
/* Common styles for both recommendation widgets */
.algolia-recs-section-header {
font-family: 'Raleway', Helvetica, sans-serif;
font-size: 1em; /* Adjust as needed, smaller than default h3 */
font-weight: bold;
margin-top: 0; /* Remove or reduce top margin */
margin-bottom: 15px; /* Space between header and recommendation cards */
color: var(--c-text); /* Use theme's text color */
font-style: italic;
}
/* Related Products Widget Styles */
#algolia-related-products .ais-RelatedProducts-list {
display: flex;
flex-direction: row; /* Arrange items horizontally */
flex-wrap: nowrap; /* Prevent wrapping to new lines, if possible */
justify-content: flex-start; /* Align items to the start of the container */
padding-left: 0; /* Remove default list padding */
list-style-type: none; /* Remove list bullets */
margin: 0;
}
#algolia-related-products .ais-RelatedProducts-item {
width: 32%; /* Adjust for 3 items: 32% * 3 items + 2% * 2 margins = 100% */
margin-right: 2%;
box-sizing: border-box; /* Include padding and border in the element's total width */
/* Optional: Basic card styling (uncomment to use) */
border: 1px solid color-mix(in srgb, var(--c-background) 85%, var(--c-text) 15%); /* Theme-aware light grey border */
padding: 0; /* Remove overall card padding, will be handled by elements */
text-align: left; /* Or 'center' if you prefer */
background-color: var(--c-background);
border-radius: 4px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
#algolia-related-products .ais-RelatedProducts-item:last-child {
margin-right: 0; /* No margin for the last item in the row */
}
/* Styling for images within recommendation items */
#algolia-related-products .ais-RelatedProducts-item img {
display: block; /* Can help remove extra space below image */
width: calc(100% - 4px); /* Full width minus 2px L/R margins */
max-width: 100%; /* Ensures image does not exceed container if intrinsically smaller */
/* height: auto; -- Controlled by inline style's max-height and object-fit */
object-fit: cover; /* Ensure image covers the area, also in inline style */
margin: 2px; /* 2px margin on top, left, right. Bottom is overridden by inline style. */
/* margin-bottom: 8px; -- This is set by inline style in JS template */
}
/* Styling for the wrapper link to make the whole card clickable */
#algolia-related-products .ais-RelatedProducts-item a.ais-RelatedProducts-item-link-wrapper {
display: block; /* Make the link fill the list item */
text-decoration: none; /* Remove underline */
color: inherit; /* Use parent's text color */
}
#algolia-related-products .related-product-title {
font-family: 'Raleway', Helvetica, sans-serif;
font-size: 0.75em;
display: -webkit-box;
-webkit-line-clamp: 2; /* Limit to 2 lines for WebKit browsers */
line-clamp: 2; /* Standard property */
-webkit-box-orient: vertical;
overflow: hidden;
text-overflow: ellipsis;
padding: 0 7px 7px 7px; /* 0 top, 7px L/R/B for text area */
line-height: 1.5; /* Adjust for better readability */
height: 3.3em; /* Current height: 3.3em. For 2 lines with 0.75em font & 1.5 line-height, calculated height would be 2.25em. */
color: var(--c-interactive); /* Use theme's interactive color */
}
/* This container will wrap the image and score, taking the original image's layout space. */
#algolia-related-products .ais-RelatedProducts-item .recommendation-image-container {
position: relative; /* For positioning the score absolutely within */
display: block; /* Matches original image display and ensures proper block layout */
width: calc(100% - 4px); /* Adopts width from original image styling */
margin: 2px; /* Adopts margin from original image styling */
margin-bottom: 8px; /* Adopts specific bottom margin from original image's inline style */
line-height: 0; /* Prevents unexpected space if child elements are treated as inline */
}
/* The image itself, now filling the container */
#algolia-related-products .ais-RelatedProducts-item .recommendation-image-container img {
display: block;
width: 100%; /* Fill the container's width */
max-width: 20em; /* Optional: retain original max-width constraint for the image content */
height: auto; /* Maintain aspect ratio by default */
max-height: 12em; /* Constrain image height (adjust as needed) */
object-fit: cover; /* Ensures image covers the allocated space, cropping if necessary */
margin: 0 auto; /* Center image if max-width kicks in and it's narrower than container */
}
/* The score overlay box */
#algolia-related-products .ais-RelatedProducts-item .recommendation-score {
position: absolute;
bottom: 3px; /* Padding from the bottom edge of the container */
right: 3px; /* Padding from the right edge of the container */
background-color: color-mix(in srgb, var(--c-background) 85%, var(--green) 15%); /* Theme-aware light green */
color: var(--green); /* Theme's green color for text */
padding: 3px 6px; /* Slightly adjusted padding */
font-family: 'Raleway', Helvetica, sans-serif;
font-size: 0.75em;
font-weight: bold;
border-radius: 10px; /* More rounded corners like the example */
border: 1px solid var(--green); /* Theme's green color for border */
line-height: 1; /* Critical for small text in a small box */
z-index: 10; /* Ensure it's above the image */
box-shadow: 0 1px 2px rgba(0,0,0,0.15); /* Softer shadow */
display: flex; /* To align icon and text nicely */
align-items: center; /* Vertically center icon and text */
}
/* Styling for the SVG icon within the score box */
#algolia-related-products .ais-RelatedProducts-item .recommendation-score-icon {
width: 0.9em; /* Scale with score's font size */
height: 0.9em;
vertical-align: -0.1em; /* Fine-tune vertical alignment */
margin-right: 4px; /* Space between icon and score number */
fill: var(--green); /* Theme's green color for icon */
}
</style>
<script>
// Function to load a script and return a promise
function loadScript(src) {
return new Promise((resolve, reject) => {
if (document.querySelector(`script[src="${src}"]`)) {
resolve(); // Already loaded
return;
}
const script = document.createElement('script');
script.src = src;
script.onload = () => resolve();
script.onerror = () => reject(new Error(`Script load error for ${src}`));
document.head.appendChild(script);
});
}
function initAlgoliaRecommendations() {
Promise.all([
loadScript('https://cdn.jsdelivr.net/npm/algoliasearch@4/dist/algoliasearch-lite.umd.js'),
loadScript('https://cdn.jsdelivr.net/npm/instantsearch.js@4')
])
.then(() => {
// Initialize the Related Products widget
initRelatedProducts();
})
.catch(error => {
// Handle error silently
});
}
function initRelatedProducts() {
// Ensure container exists
if (!document.getElementById('algolia-related-products')) {
// Container not found, stopping initialization
return;
}
const recSearchClient = algoliasearch(
'2XJCLEABQD',
'b61ec4cb64bd32d62c053466fccbfa43'
);
const relatedSearch = instantsearch({
indexName: 'eugeneyan.com',
searchClient: recSearchClient,
clickAnalytics: true,
insights: true, // Enable insights for click tracking on recommendations
});
relatedSearch.addWidgets([
instantsearch.widgets.relatedProducts({
container: '#algolia-related-products',
objectIDs: ['/writing/what-does-a-data-scientist-really-do/'],
limit: 3,
queryParameters: {
attributesToRetrieve: ['title', 'url', 'image', 'score', '_score'], // Specify only needed attributes
attributesToHighlight: [], // Disable highlighting
attributesToSnippet: [] // Disable snippeting
},
translations: {
title: '', // Custom title is in _layouts/post.html
},
transformItems: function(items) {
const containerElement = document.getElementById('algolia-recs-container');
const relatedElement = document.getElementById('algolia-related-products');
if (items.length === 0) {
if (relatedElement) relatedElement.style.display = 'none';
if (containerElement) containerElement.style.display = 'none';
} else {
if (relatedElement) relatedElement.style.display = 'block';
if (containerElement) containerElement.style.display = 'block';
}
return items;
},
templates: {
header() { // Removed unused results, html parameters
// Return a PLAIN string for the header
return '<h4 class="algolia-recs-section-header">You Might Also Like</h4>';
},
item: function(hit, { html, sendEvent }) { // Added sendEvent to params
const itemUrl = `${hit.url || '#'}`;
const indexName = 'eugeneyan.com'; // Get index name for insights
let imageUrl;
// Ensure hit.image is not null, undefined, or an empty/whitespace string before using it.
if (hit.image && typeof hit.image === 'string' && hit.image.trim() !== '') {
imageUrl = `/assets/og_image/${hit.image}`;
} else {
imageUrl = `/assets/og_image/default-v4.jpg`; // Default image
}
let scoreValue = null;
if (typeof hit.score === 'number') scoreValue = hit.score.toFixed(2);
else if (typeof hit._score === 'number') scoreValue = hit._score.toFixed(2);
// else if (typeof hit.your_custom_score_field === 'number') scoreValue = hit.your_custom_score_field.toFixed(2);
const scoreElement = scoreValue ? `<div class="recommendation-score"><svg viewbox="0 0 24 24" class="recommendation-score-icon" xmlns="http://www.w3.org/2000/svg"><path d="M16 6l2.29 2.29-4.88 4.88-4-4L2 16.59 3.41 18l6-6 4 4 6.3-6.29L22 12V6h-6z"></path></svg>${scoreValue}</div>` : '';
const imageAndScoreTag = `
<div class="recommendation-image-container">
<img src="${imageUrl}" alt="${hit.title || 'Recommendation cover image'}">
${scoreElement}
</div>`;
const title = hit.title || 'Untitled Post';
return `
<a href="${itemUrl}"
class="ais-RelatedProducts-item-link-wrapper"
onClick="${() => {
sendEvent('click', hit, 'Related Item Clicked');
}}"
>
${imageAndScoreTag}
<div class="related-product-title">${title}</div>
</a>
`;
},
empty(results, { html }) {
const containerElement = document.getElementById('algolia-recs-container');
const relatedElement = document.getElementById('algolia-related-products');
if (relatedElement) {
relatedElement.style.display = 'none';
}
// If this 'empty' template is called, it means related products are empty.
// So, the main container should also be hidden.
if (containerElement) {
containerElement.style.display = 'none';
}
// Return an empty string to prevent rendering 'undefined'
return '';
}
}
})
]);
relatedSearch.start();
}
document.addEventListener('DOMContentLoaded', function() {
let recsLoaded = false;
function checkLoad() {
if (recsLoaded) return;
if ((window.scrollY + window.innerHeight) >= document.body.scrollHeight - 500) {
recsLoaded = true;
window.removeEventListener('scroll', checkLoad);
initAlgoliaRecommendations();
}
}
window.addEventListener('scroll', checkLoad, { passive: true });
checkLoad();
});
</script>
</div> -->
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<p>I'm a Member of Technical Staff at Anthropic. I work to bridge the field and the frontier, and help build safe, reliable AI systems that scale. I've led ML/AI teams at Amazon, Alibaba, Lazada, and a Healthtech Series A, and write about LLMs, RecSys, and engineering at <a href="https://eugeneyan.com/" target="_blank">eugeneyan.com</a>.</p>
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