William's Data Science Blog

Tag: health

  • Data Science in the World Pt. 5: Data Science in high education

    Data Science in the World Pt. 5: Data Science in high education

    This is the fifth post in my “Data Science in the World” series.

    How Data Science is Transforming Higher Education

    When most people think of college or university, they picture lecture halls, libraries, and late-night study sessions. But behind the scenes, a quiet revolution is underway, one powered by data science. Just as data has reshaped industries like healthcare, finance, and transportation, it is now transforming higher education. From improving student success to guiding institutional decisions, data science is becoming a cornerstone of how colleges and universities operate.

    This might sound abstract, but the reality is simple: data science is helping students learn more effectively, helping educators teach more efficiently, and helping institutions make smarter choices. Let’s explore three key areas where data science is making the biggest impact in higher education: student success and retention, personalized learning, and institutional decision-making.

    Student Success and Retention

    One of the most pressing challenges in higher education is ensuring that students not only enroll but also graduate. Dropout rates remain a concern, and every student who leaves represents both a personal setback as well as a loss for the institution. Data science is helping to address this challenge by identifying at-risk students early and providing targeted support.

    Colleges collect a wide range of data about students, like grades, attendance, course engagement, financial aid status, and even participation in extracurricular activities. By analyzing these data points, machine learning models can detect patterns that signal when a student might be struggling.

    For example, a sudden drop in class attendance combined with declining grades might indicate that a student is at risk of dropping out. Predictive analytics can flag this student, allowing advisors or faculty to intervene before it’s too late.

    • Georgia State University has become a leader in using predictive analytics to improve student success. By tracking over 800 risk factors, the university has significantly increased graduation rates, particularly among first-generation and low-income students.
    • Community colleges are also adopting similar systems, using data to provide proactive advising and support services tailored to individual student needs.

    For students, this means more personalized support and a greater chance of completing their degree. For institutions, it means improved retention rates, which not only enhance reputation but also ensure financial stability. For society, it means more graduates entering the workforce with the skills needed to succeed.

    Personalized Learning

    Every student learns differently. Some thrive in large lectures, while others need more hands-on support. Traditional education models often struggle to accommodate these differences. Data science is changing that by enabling personalized learning experiences tailored to each student’s strengths, weaknesses, and preferences.

    Learning management systems (LMS) and online platforms collect detailed data on how students interact with course materials: how long they spend on readings, which quiz questions they miss, and how often they participate in discussions. Data science tools analyze this information to create individualized learning pathways.

    For instance, if a student consistently struggles with a particular math concept, the system can recommend additional practice problems, videos, or tutoring resources. Conversely, if a student masters material quickly, the system can accelerate their progress to keep them engaged.

    • Adaptive learning platforms like ALEKS (for math) or Smart Sparrow (for science) use data-driven algorithms to adjust content in real time, ensuring that students receive the right level of challenge.
    • Massive Open Online Courses (MOOCs) such as Coursera and edX leverage data science to recommend courses and resources based on a learner’s past activity and performance.

    Personalized learning helps students stay motivated and engaged, reducing frustration and boredom. It also allows instructors to focus their attention where it’s needed most, rather than applying a one-size-fits-all approach. Over time, this could lead to more equitable outcomes, as students from diverse backgrounds receive the support they need to succeed.

    Institutional Decision-Making

    Running a college or university is a complex endeavor. Administrators must make decisions about everything from course offerings to campus facilities to budget allocations. Traditionally, these decisions were based on historical trends, intuition, or limited data. Today, data science is providing a more rigorous foundation for institutional decision-making.

    Universities generate enormous amounts of operational data: enrollment numbers, course demand, faculty workloads, financial aid distribution, and more. By applying data science techniques, administrators can uncover insights that guide strategic planning.

    • Course scheduling: Predictive models can forecast which classes will be in high demand, ensuring that enough sections are offered to meet student needs.
    • Resource allocation: Data can reveal which programs are growing and which are declining, helping institutions allocate funding more effectively.
    • Facilities management: Sensors and data analytics can optimize energy use, reduce costs, and create more sustainable campuses.

    Real-World Examples

    • Arizona State University uses data analytics to optimize course scheduling and advising, ensuring that students can access the classes they need to graduate on time.
    • The University of Michigan has applied data science to improve energy efficiency across campus, saving millions of dollars while reducing environmental impact.

    Smarter decision-making benefits everyone. Students get the classes and resources they need, faculty workloads are managed more effectively, and institutions operate more efficiently. In an era of rising tuition costs and financial pressures, data-driven management helps ensure that higher education remains sustainable and accessible.

    Spotlight: The Early Signal Project

    Another example of how data science can support student success is the Early Signal Project, a nonprofit initiative I founded to help educators detect socio-emotional risks in students before they escalate. By combining privacy-compliant surveys with carefully designed data pipelines, the project gives schools actionable insights while protecting student trust. Instead of waiting until problems become visible in grades or attendance, educators receive early, anonymized signals that a student may need support. This proactive approach mirrors the broader promise of data science in higher education: using information ethically and transparently to empower teachers, improve outcomes, and ensure that no student falls through the cracks.

    Conclusion

    Data science is no longer confined to tech companies or research labs. It’s becoming a central part of how higher education functions. By improving student success and retention, enabling personalized learning, and guiding institutional decision-making, data science is helping colleges and universities adapt to the challenges of the 21st century.

    Privacy concerns must be carefully managed, and institutions must ensure that data-driven decisions are fair and transparent. But the potential benefits are enormous. As data science continues to evolve, it promises to make higher education not only more efficient but also more inclusive, personalized, and effective.

    In the end, higher education has always been about unlocking human potential. With the help of data science, that mission is being reimagined for a new era—one where every student has the opportunity to succeed, every instructor has the tools to teach effectively, and every institution has the insights to thrive.

    References

  • Data Science in the World Pt. 4: Data Science in auto industry

    Data Science in the World Pt. 4: Data Science in auto industry

    This is the fourth post in my “Data Science in the World” series.

    Driving the Future: How Data Science is Transforming the Auto Industry

    Cars have always been about more than just getting from point A to point B. They represent freedom, innovation, and progress. Today, they also represent something else: data. Modern vehicles are really computers on wheels, generating and processing vast amounts of information every second. From safety systems to navigation to entertainment, data science is quietly reshaping the way we design, build, and drive cars.

    Data science is already influencing your daily commute, the safety of your family, and even the future of how we think about car ownership. Let’s explore three key areas where data science is making the biggest impact: autonomous driving, predictive maintenance, and connected car services.

    Autonomous Driving: Teaching Cars to Think

    Perhaps the most exciting—and widely discussed—application of data science in the auto industry is autonomous driving. Self-driving cars rely on a combination of sensors, cameras, radar, and lidar to perceive their surroundings. But perception alone isn’t enough. The real magic happens when data science steps in to interpret all that information and make decisions in real time.

    • Data collection: A single autonomous vehicle can generate terabytes of data every day. This includes images from cameras, distance measurements from lidar, and speed or position data from GPS.
    • Machine learning models: These massive datasets are used to train algorithms that can recognize pedestrians, traffic lights, road signs, and other vehicles.
    • Decision-making: Once trained, the system can predict what’s likely to happen next—like whether a pedestrian will step into the crosswalk—and decide how the car should respond.

    Companies like Tesla, Waymo, and traditional automakers are investing billions into this technology. While fully autonomous cars aren’t yet mainstream, features like adaptive cruise control, lane-keeping assistance, and automatic emergency braking are already powered by data science. These are steppingstones toward a future where cars can safely drive themselves.

    Autonomous driving has the potential to reduce accidents caused by human error, which accounts for the vast majority of crashes. It could also make transportation more accessible for people who can’t drive, such as the elderly or disabled. For the average driver, it promises a future where commuting time could be spent reading, working, or simply relaxing.

    Predictive Maintenance: Fixing Problems Before They Happen

    Traditionally, maintenance has been reactive: you wait until something goes wrong, then fix it. Data science is changing that by enabling predictive maintenance, where problems are identified and addressed before they cause a breakdown.

    • Sensors everywhere: Modern cars are equipped with hundreds of sensors monitoring everything from engine temperature to tire pressure.
    • Data analysis: These sensors feed data into machine learning models that can detect patterns indicating wear and tear.
    • Predictive alerts: Instead of a vague “check engine” light, predictive systems can tell you exactly which component is likely to fail and when.

    Fleet operators, like delivery companies or ride-sharing services, are already using predictive maintenance to keep vehicles on the road longer and reduce downtime. For everyday drivers, some automakers now offer apps that notify you when your car needs attention, sometimes even scheduling service appointments automatically.

    Predictive maintenance saves money by preventing costly repairs and extends the life of vehicles. More importantly, it improves safety by reducing the risk of sudden failures on the road. For consumers, it means fewer surprises and more confidence in their cars.

    Connected Car Services: Turning Vehicles into Smart Devices

    Think of your car as a smartphone on wheels. Just as your phone connects you to apps, maps, and services, connected cars use data science to provide a seamless, personalized driving experience.

    • Telematics: Cars transmit data about location, speed, and performance to cloud platforms.
    • Personalization: Data science algorithms analyze your driving habits to suggest routes, adjust climate control, or recommend nearby services.
    • Integration: Connected cars can communicate with other vehicles and infrastructure, creating smarter traffic systems.

    Connected car services make driving more convenient, efficient, and enjoyable. On a larger scale, they also contribute to smarter cities by reducing congestion and emissions. For consumers, it’s about having a car that feels less like a machine and more like a personalized companion.

    Conclusion

    Data science is no longer a behind-the-scenes tool in the auto industry—it’s the driving force behind its most exciting innovations. Autonomous driving is teaching cars to think, predictive maintenance is making them more reliable, and connected services are turning them into smart devices that fit seamlessly into our digital lives.

    For the general public, the takeaway is simple: your car isn’t just powered by gasoline or electricity, it’s powered by data. And as data science continues to evolve, the way we drive, maintain, and experience cars will keep transforming in ways that once belonged only to science fiction.

    References

    • Intel. (2016). Data-Driven Intelligence: The Future of Autonomous Vehicles. Intel Corporation Whitepaper.
    • Waymo LLC. (2020). The Waymo Open Dataset: Autonomous Vehicle Perception Benchmark. Waymo Research Publications.
    • MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). (2019). Planning and Decision-Making in Autonomous Driving. MIT Technical Report.
    • General Motors (GM). (2020). OnStar and Connected Services: Telematics Platform Overview. GM Global Technology.
    • Tesla, Inc. (2021). Vehicle Diagnostics, Over-the-Air Updates, and Predictive Service. Tesla Engineering Documentation.
    • University of Michigan Transportation Research Institute (UMTRI). (2017). Ann Arbor Connected Vehicle Test Environment: Phase 2 Results. UMTRI Technical Report.
  • Data Science in the World Pt. 3: Data Science in Finance

    Data Science in the World Pt. 3: Data Science in Finance

    This is the third post in my “Data Science in the World” series.

    How Data Science is Transforming Finance: Fraud, Credit, and Investments

    When most people think about finance, they picture swiping a card, checking a bank balance, or maybe watching the stock market ticker scroll across a screen. What’s less visible is how much data science is working behind the scenes to make those everyday interactions safer, smarter, and more personalized.

    Financial institutions have always been data-driven, but the explosion of computing power and machine learning has changed the game. Today, banks, lenders, and investment firms can analyze billions of data points in real time, uncovering patterns that humans alone could never detect. The result? Faster fraud detection, fairer lending decisions, and more accessible investment opportunities.

    I will review three of the most important ways data science is reshaping the financial services industry: fraud detection and security, credit scoring and lending, and investment and wealth management.

    Fraud Detection and Security

    Fraud has always been a cat-and-mouse game. As soon as banks develop new defenses, fraudsters find new ways to exploit vulnerabilities. Data science has tilted the balance in favor of the defenders by enabling real-time, adaptive fraud detection.

    Every financial transaction, whether it’s a credit card swipe, an online transfer, or a mobile payment, creates a data trail. Machine learning models are trained on millions of these transactions to learn what “normal” behavior looks like. They consider dozens of factors simultaneously:

    • Location: Is the purchase happening in the same city as the customer’s last transaction?
    • Timing: Does the transaction fit the customer’s usual spending patterns?
    • Device: Is the payment being made from a familiar phone or computer?

    When something looks unusual, like a purchase in another country minutes after a local coffee shop visit, the system can flag it instantly.

    For consumers, this often shows up as a text message or app notification. Behind that alert is a sophisticated model that has already calculated the probability of fraud in milliseconds.

    Banks benefit too. According to industry reports, AI-driven fraud detection has saved billions of dollars annually by reducing false positives (legitimate transactions incorrectly flagged) and catching fraudulent activity earlier.

    Fraud detection powered by data science protects money and trust. In a world where digital payments are the norm, consumers need to feel confident that their financial institutions can keep them safe. Data science makes that possible.

    Credit Scoring and Lending

    For decades, credit decisions were based on a narrow set of factors: payment history, outstanding debt, and length of credit history. While effective to a point, this system left many people without traditional credit histories, locked out of the financial system. Data science is helping to change that.

    Modern credit models can incorporate a much wider range of information:

    • Alternative data: Rent payments, utility bills, and even subscription services can demonstrate reliability.
    • Behavioral data: Spending patterns, savings habits, and cash flow stability provide additional context.
    • Digital footprints: With proper privacy safeguards, online activity can sometimes serve as a proxy for financial responsibility.

    By analyzing these broader datasets, machine learning models can paint a more complete picture of an applicant’s creditworthiness. This benefits consumers and lenders in the following ways:

    • Fairer access: People with limited or no credit history can qualify for loans they might otherwise be denied.
    • Reduced bias: Properly designed models can minimize human subjectivity in lending decisions.
    • Better risk management: Lenders can more accurately predict defaults, reducing losses and keeping interest rates competitive.

    Investment and Wealth Management

    Investing used to be the domain of the wealthy, with personalized advice available only to those who could afford a financial advisor. Data science has democratized investing, making it more accessible, affordable, and tailored to individual needs.

    An example of this democratization is Robo-advisors. Robo-advisors are digital platforms that use algorithms to build and manage investment portfolios. By asking a few simple questions about risk tolerance, time horizon, and goals, the system can recommend a diversified portfolio that automatically rebalances over time. The benefits to everyday are:

    • Lower costs: Automated systems reduce the need for expensive human advisors.
    • Accessibility: Minimum investment amounts are often much lower than traditional wealth management services.
    • Customization: Portfolios can be tailored to individual preferences, such as socially responsible investing.

    At the institutional level, hedge funds and asset managers use machine learning to detect subtle patterns in market data. Some even analyze unconventional sources like satellite imagery (e.g., counting cars in retail parking lots to predict sales) or social media sentiment to gain an edge.

    Data science also helps investors understand and manage risk. Predictive models can simulate how a portfolio might perform under different economic scenarios, giving both professionals and individuals a clearer picture of potential outcomes.

    At the same time, regulators are pushing for “explainable AI” in finance, ensuring that investment recommendations are transparent and understandable rather than black-box predictions.

    Conclusion

    Data science is no longer a buzzword in finance. It’s the backbone of how the industry operates. From protecting consumers against fraud, to opening up credit access, to democratizing investing, the impact is profound and personal.

    For the general public, the takeaway is simple: every time you swipe your card, apply for a loan, or check your investment app, data science is working behind the scenes. It’s making your financial life safer, smarter, and more tailored to your needs.

    As technology continues to evolve, expect these systems to become even more sophisticated. The future of finance isn’t just digital—it’s data-driven.

    References

  • Data Science in the World Pt. 2: Data Science in Drug Research

    Data Science in the World Pt. 2: Data Science in Drug Research

    This is the second post in my “Data Science in the World” series.

    A New Era in Medicine

    Modern medicine is undergoing a quiet revolution, powered by data. Hospital visits, lab tests, and genetic sequences add to a growing collection of medical information. Data science is helping scientists, doctors, and researchers use this information to find new treatments faster, test them more safely, and tailor care to each individual patient.

    From Molecules to Medicines: Data-Driven Discovery

    Traditionally, discovering a new drug could take a decade or more, with researchers testing thousands of chemical compounds by hand. Today, machine learning models can predict how molecules will interact with the human body long before they’re ever created in a lab.

    For example, AI-powered algorithms analyze massive databases of molecular structures and biological data to identify promising compounds that might block a virus, reduce inflammation, or kill cancer cells. These models learn from patterns across millions of examples, helping researchers focus only on the most likely winners.

    This approach reduces the cost and time to develop new medicines. In some cases, machine learning has identified viable drug candidates in months rather than years — a leap forward in the fight against fast-moving diseases.

    Refining and Developing New Treatments

    Once potential compounds are identified, scientists must determine how they behave inside the body. Data science helps here by analyzing complex biological signals, genetic variations, and even past clinical data to predict outcomes.

    For instance, computer simulations (known as in silico trials) can model how a new medicine might interact with different cell types or organs. These models help researchers optimize dosages and anticipate side effects long before human testing begins.

    Pharmaceutical companies also use predictive analytics to identify which drug formulations are most stable and effective, cutting down on costly lab iterations.

    Smarter, Faster Clinical Trials

    Clinical trials are where potential medicines are tested in humans and where most drug candidates fail. Trials are slow, expensive, and difficult to manage. Data science is making them smarter and more efficient.

    Predictive analytics can help identify the right patients for each trial, ensuring diverse participation and faster recruitment. Algorithms analyze medical records and genomic data to match patients who are most likely to benefit, or least likely to experience harm, from an experimental drug.

    Real-time data monitoring during trials also improves safety. Machine learning systems can flag early warning signs or unexpected reactions faster than human observers. These insights can help scientists adjust or even redesign studies on the fly.

    Personalized Medicine: Tailoring Treatments to Individuals

    Perhaps the most exciting result of this data revolution is personalized medicine, customizing treatment plans for individual patients. Instead of a one-size-fits-all approach, doctors can use data to predict which therapies will work best based on a person’s genes, environment, and medical history.

    For example, genomic data can reveal how patients metabolize certain drugs, allowing physicians to prescribe the safest and most effective options. AI systems can even help oncologists choose targeted cancer therapies by comparing tumor DNA against thousands of previous cases.

    This data-driven personalization not only improves outcomes but also reduces side effects, saving lives and healthcare costs alike.

    Challenges and Ethics: The Human Side of Data

    As powerful as data science is, it also raises critical ethical and technical challenges. Patient privacy must be protected. Medical data is among the most sensitive information that exists. Robust security measures and anonymization techniques are essential.

    Bias is another concern. If algorithms are trained on incomplete or unrepresentative datasets, their predictions could unfairly favor or disadvantage certain groups. Transparency, oversight, and diverse data collection are key to ensuring fairness.

    Finally, collaboration between scientists, clinicians, and data experts is crucial. Data alone doesn’t save lives, people using it wisely can.

    Conclusion: The Future of Medicine Is Intelligent

    From discovering new molecules to designing smarter clinical trials and personalizing treatments, data science has become an essential partner in modern medicine. It allows scientists to see patterns invisible to the human eye, accelerate breakthroughs, and deliver safer, more effective therapies.

    As healthcare continues to generate more data than ever, the question is no longer whether data science will shape the future of medicine, but how far it can take us.

    References