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Online Assessments for the Data Team

Data Science: Exploratory Data Analysis on a Consumer Finance Dataset

Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.

Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.

On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)

Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:

  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?
    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?

Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!

Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.

This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!

Instructions for working with the dataset:

The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.

Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing

Log in with your GApps@Illinois account to access this dataset.

Deliverables

  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.

Grading

We will be looking at your project and grading it under these five criteria:

  1. Code
    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation
    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling
    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness
    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About

Online Assessments for the Data Team

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Readme
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

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7 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Online Assessments for the Data Team
Data Science: Exploratory Data Analysis on a Consumer Finance Dataset
Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.
Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.
On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)
Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:
  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?

    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?
Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!
Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.
This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!
Instructions for working with the dataset:
The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.
Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing
Log in with your GApps@Illinois account to access this dataset.
Deliverables
  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.
Grading
We will be looking at your project and grading it under these five criteria:
  1. Code

    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation

    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling

    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness

    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About
Online Assessments for the Data Team
Resources
Readme
Activity
Custom properties
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7 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Online Assessments for the Data Team
Data Science: Exploratory Data Analysis on a Consumer Finance Dataset
Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.
Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.
On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)
Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:
  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?

    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?
Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!
Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.
This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!
Instructions for working with the dataset:
The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.
Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing
Log in with your GApps@Illinois account to access this dataset.
Deliverables
  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.
Grading
We will be looking at your project and grading it under these five criteria:
  1. Code

    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation

    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling

    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness

    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About
Online Assessments for the Data Team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
7 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Online Assessments for the Data Team
Data Science: Exploratory Data Analysis on a Consumer Finance Dataset
Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.
Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.
On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)
Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:
  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?

    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?
Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!
Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.
This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!
Instructions for working with the dataset:
The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.
Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing
Log in with your GApps@Illinois account to access this dataset.
Deliverables
  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.
Grading
We will be looking at your project and grading it under these five criteria:
  1. Code

    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation

    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling

    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness

    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About
Online Assessments for the Data Team
Resources
Readme
Activity
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Stars
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7 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Online Assessments for the Data Team
Data Science: Exploratory Data Analysis on a Consumer Finance Dataset
Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.
Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.
On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)
Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:
  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?

    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?
Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!
Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.
This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!
Instructions for working with the dataset:
The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.
Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing
Log in with your GApps@Illinois account to access this dataset.
Deliverables
  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.
Grading
We will be looking at your project and grading it under these five criteria:
  1. Code

    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation

    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling

    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness

    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About
Online Assessments for the Data Team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
7 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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data-oa
Online Assessments for the Data Team
Data Science: Exploratory Data Analysis on a Consumer Finance Dataset
Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.
Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.
On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)
Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:
  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?

    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?
Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!
Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.
This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!
Instructions for working with the dataset:
The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.
Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing
Log in with your GApps@Illinois account to access this dataset.
Deliverables
  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.
Grading
We will be looking at your project and grading it under these five criteria:
  1. Code

    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation

    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling

    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness

    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About
Online Assessments for the Data Team
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7 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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data-oa
Online Assessments for the Data Team
Data Science: Exploratory Data Analysis on a Consumer Finance Dataset
Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.
Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.
On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)
Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:
  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?

    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?
Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!
Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.
This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!
Instructions for working with the dataset:
The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.
Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing
Log in with your GApps@Illinois account to access this dataset.
Deliverables
  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.
Grading
We will be looking at your project and grading it under these five criteria:
  1. Code

    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation

    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling

    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness

    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About
Online Assessments for the Data Team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
7 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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data-oa
Online Assessments for the Data Team
Data Science: Exploratory Data Analysis on a Consumer Finance Dataset
Exploratory data analysis plays a huge part in quantitative finance. Often, in a variety of scenarios, consumer interests, expectations and beliefs play a huge role in deciding major moves for organisations.
Imagine the following scenario: We are an up and coming bank with limited resources. We want to start generating alpha as soon as possible and are elated to do so. Unfortunately, we are unable to hire a large legal team that handles disputes effectively, and seek to minimise the number of consumer complaints that we deal with, as well as deal with only general categories whose disputes are easy to resolve.
On the bright side, we have a dataset with a large number of submitted complaints for many credit based products. Based on these complaints, can you generate insights and a summary of credit products that our company can sell safely? Are there optimizations that we can make in our product offering? (from a legal and PR perspective, assume that another team handles actual credit-default risk.)
Your task, then, is to write a Jupyter Notebook detailing an exploration into the data that you have. Here are a few ideas that you can explore:
  • What states are these disputes most prevalent in? (Can you also maybe get in your own dataset about populations of these states to measure percentages?)
  • Could you figure out the most common products and sub-products with these complaints? Could you find out which of the banks were most likely to have which issues?
  • Could you find out what the most common language / keywords that companies used in their personal statements are?
  • Can you build a quick and dirty model to take in a bunch of relevant features and figure out how the compnay is going to approach the issue?

    • You cannot take in the consumer complaint as text itself in most models, that would be unwise, is there any way you can quantify this text using some basic NLP, and use those instead as features.
  • In the same way, taking more relevant features, can you build out a model that takes in relevant features and the company's closure method, and predict if the consumer is going to dispute it or not?
Remember, this is a non comprehensive list, and any new ideas that you have can and should be implemented. If you have the opportunity to use any other public datasets to amplify the insights from this dataset, you're encouraged to import them in!
Your code should be highly modular, customizable, and absent of technical debt. Your exploration must contain lots of your hanging ideas, explanations based on your choices you make with the data. Remember to work with only the essential data, and drop all irrelevant columns, remove outliers, etc.
This criteria is the same in our codebase and will be a good indicator if you can generate data science based insights. Imagine that you're showing the insights from this dataset to an executive, and retain only visual and simple to understand correlations that you find. Good luck!
Instructions for working with the dataset:
The dataset is on Google Drive. Our advice for you is to download the dataset, work on it, and add it to the gitignore in your private forks, so that you don't commit it. Always print out outputs of operations within your notebook, but do not include the dataset itself as part of your submission.
Link to dataset: https://drive.google.com/file/d/1Vnny6HVGBgF_mdDtIo2ddOTtOiGF5jxK/view?usp=sharing
Log in with your GApps@Illinois account to access this dataset.
Deliverables
  1. A Jupyter Notebook with all the code and your thought process. (Remember the thought process is what we're looking for)
  2. A generated, dynamic PDF File with the insights that you have gathered.
  3. A list of future hypotheses that you may not have yet implemented / couldn't figure out how to, but feel that could be interesting. Ideally, this would be structured in LaTex.
Grading
We will be looking at your project and grading it under these five criteria:
  1. Code

    • If it works
    • Modular
    • Follows best practices (ie. OOP)
  2. Documentation

    • Concise and exact.
    • Follows popular conventions.
    • Visuality. (Lots of graphs and statistics)
  3. Styling

    • Human readable
    • Can quickly glance to receive all relevant information
    • Follows Google Style Guide (preferred if it exists) or most popular convention (ie. PEP8)
  4. Robustness

    • Customizable
    • No technical debt (future proof)
    • Handles bad inputs and errors
  5. Git

About
Online Assessments for the Data Team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
7 forks
Report repository
Releases
Packages
Contributors