Navigating Biostatistics Coding Interviews: What to Expect and How to Prepare

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Your confidence in biostats coding interviews will increase the more you prepare for them.

 

If you’re about to step into a biostatistics coding interview, it’s natural to feel a little nervous. Coding interviews can be a pivotal part of your job search. You’ve likely spent hours mastering the theories and concepts behind biostatistics, but now it’s time to prove your skills in a live coding environment.

The good news?  With the right preparation and a calm mindset, you can handle the coding challenges that come your way. This article will walk you through exactly what you can expect in a biostatistics coding interview and how to best prepare. From common coding challenges to tips on how to handle real-time problem solving, we’ll help you feel more confident and ready to tackle any question thrown at you.

So, let’s break it down – what should you focus on, and how can you make sure you’re ready for anything? Let’s dive in.

Live Coding vs. Take-Home Assignments: The Key Differences

Before diving into what to expect in a biostatistics coding interview, let’s first set the stage by distinguishing between live coding and take-home assignments. Both are common ways to assess candidates, but they are quite different in their approach.

Live coding interviews are designed to see how you think on your feet. You’re given a problem (often a data manipulation task, statistical modeling, or algorithmic challenge) and expected to solve it while explaining your thought process. There’s no opportunity for deep research or revising the solution. You have to work through it in real-time, which can be stressful – but it’s also a chance to show off how you tackle problems under pressure.

Take-home assignments, on the other hand, allow you time to research, explore multiple solutions, and perfect your answer. Employers can assess how you approach complex problems, but with the added benefit of time and more refined solutions.

For this article, though, we’ll focus on live coding interviews and what you can expect in this high-pressure environment.

What You’ll Be Tested On: Core Areas to Focus

When it comes to biostatistics coding interviews, there are a few main areas that will likely be the focus. These are your “bread and butter,” the things you’ll need to have down pat if you want to perform well.

1. Data Wrangling and Transformation

In biostatistics, data is king, and you’ll spend a lot of time transforming, cleaning, and manipulating datasets. Here’s what you’ll need to focus on:

  • Merging and Joining Datasets: Understand how to combine data from multiple sources. Whether you’re using left joins, right joins, or inner joins, you need to be able to explain when and why to use each one. This is not just about knowing the syntax but also understanding how different types of joins can affect your analysis.

  • Handling Missing Data: This is a big one. Real-world data is messy. You’ll likely encounter datasets with missing values, and you’ll need to show how you’d handle that – whether through imputation, dropping rows, or a more sophisticated method.

  • Reshaping Data: Knowing how to pivot or melt datasets is essential. Whether it’s in Python with pandas or R with tidyr, practice transforming data into the right shape for analysis.

    Example:
    Let’s say you have patient data for multiple years and you need to transform it so each patient’s data is in a single row (one record per patient). How would you approach that?

2. Statistical Analysis and Modeling

Next up is the application of biostatistics. Employers want to know that you can apply statistical methods to data to answer real-world questions.

  • Basic Statistical Tests: Be prepared to work with common tests like t-tests, chi-square tests, and ANOVA. You should know when to use these and what their assumptions are.

  • Regression Models: Whether it’s linear regression, logistic regression, or survival analysis, you need to be able to implement these models and understand their underlying assumptions.

  • Interpreting Results: Employers aren’t just testing your ability to run code – they want to know if you understand what the results mean and how to interpret them. For instance, if you fit a logistic regression model, can you interpret the odds ratios? Can you explain why you might choose one model over another?

3. Data Structures and Algorithms

While biostatistics coding interviews tend to focus more on data wrangling and statistics, don’t be surprised if you’re asked about basic algorithms or data structures. It’s not about memorizing algorithms for sorting or searching, but understanding how to use them effectively when working with large datasets.

  • Efficiency: For example, can you handle a large dataset without running into memory issues? Can you optimize your code to run faster? Employers want to know you can solve problems efficiently -both in terms of time complexity and computational resources.

    Example:
    You might be given a large dataset and asked how you’d efficiently calculate the mean of a column without using a built-in function (example). This tests both your algorithmic thinking and your understanding of data structures.

How to Prepare: Steps to Set Yourself Up for Success

Preparing for a coding interview in biostatistics can feel overwhelming, but with a structured approach, you’ll be ready to tackle any problem that comes your way. Here’s how to get started:

1. Master Key Functions and Libraries

You don’t need to be an expert in every library out there, but you should be comfortable with key functions used for data wrangling and statistical modeling. Depending on the language you’re using, this might include:

  • Python:
    • pandas for data wrangling
    • numpy for numerical operations
    • scipy and statsmodels for statistical tests and models
    • matplotlib and seaborn for data visualization
  • R:
    • dplyr, tidyr, and data.table for data manipulation
    • ggplot2 for data visualization
    • lm(), glm(), and survfit() for statistical modeling

Tip: Get comfortable with libraries that handle large datasets and work with them efficiently. Whether it’s dplyr in R or pandas in Python, ensure you understand their key functions for data manipulation and transformation.

2. Practice with Real-World Datasets

The best way to prepare for biostatistics coding interviews is to practice with real-world datasets. You can find datasets on sites like Kaggle or UCI Machine Learning Repository. These datasets are often messy and require the exact kind of data wrangling you’ll encounter in interviews.

  • Work on cleaning datasets: Practice filling in missing values, transforming variables, or merging datasets.
  • Explore statistics: Use the data to run statistical tests or fit models, and then practice explaining your results.

3. Focus on Problem-Solving, Not Just Syntax

Yes, knowing your syntax is important, but during a live coding interview, how you approach the problem matters just as much – if not more. Here’s what you should focus on:

  • Think aloud: Employers want to hear your thought process. Explain why you’re choosing a certain method, and if you’re stuck, describe what you’re considering.
  • Break down problems: Start with a high-level plan. How would you break down the task into smaller pieces? What’s the best way to approach the problem?

Tip: Even if you don’t know the exact solution immediately, showing that you have a logical approach will go a long way. If you make a mistake, own up to it and show how you would fix it.

4. Do Mock Interviews

The more you practice, the more confident you’ll feel. Set up mock coding interviews with peers or mentors who can simulate the pressure of a live interview. Alternatively, test yourself with online exercises under interview conditions.

What Employers Are Really Looking For

Employers want to know that you can do more than just code. They’re also looking for:

  • Problem-solving skills: Can you think critically under pressure and explain your reasoning?
  • Communication skills: Can you break down complex technical concepts into simple explanations?
  • Statistical rigor: Can you apply statistical methods correctly and interpret results thoughtfully?
  • Collaboration: While it might not be explicitly tested in the interview, showing that you can communicate your process and work with a team is important.

Confidence Is Key

Live coding interviews can be intimidating, but with the right preparation, you’ll walk in ready to take on the challenge. Focus on mastering key functions, practicing with real data, and improving your problem-solving skills. Don’t forget to communicate your thought process clearly and stay calm under pressure.

Now, take a deep breath and ask yourself: What’s one area you’ll focus on first? Maybe it’s mastering the intricacies of joins or practicing hypothesis testing. Choose one thing from this guide to work on today, and start building the confidence you need for your next interview. With consistent practice, you’ll be ready to take on anything.

Good luck, and remember: every interview is a learning experience that gets you one step closer to your dream job!

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MS in Biostatistics vs. Health Data Science: Which Degree is Better for Your Career Goals?

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While the role of Health Data Scientist is relatively new compared to Biostatistician, its interest has been rapidly growing in recent years.
 

When considering a graduate degree, the choice between an MS in Biostatistics and an MS in Health Data Science can feel like a tough one. Both degrees can open doors to lucrative and impactful careers, but each offers a slightly different approach to tackling healthcare data. The real question is: Which degree aligns better with your career goals?

The Overlap: How the Degrees Can Be More Similar Than Different

At first glance, these two degrees may seem worlds apart. Biostatistics sounds like it’s focused purely on statistics, while Health Data Science sounds more like a tech-heavy path. But here’s the thing – many programs in both fields will cover similar ground, especially when it comes to statistical analysis, programming, and data interpretation in healthcare settings. The real difference often comes down to the nuances of the curriculum and the emphasis on specific skills.

If you’re deciding between the two, it’s crucial to understand that the syllabus can vary from school to school. Some programs might even combine elements of both fields in their courses. So, it’s not entirely unusual for a student in either program to end up with overlapping knowledge and skills, especially when it comes to core concepts like epidemiology, statistical modeling, and data visualization.

Key Classes for Aspiring Biostatisticians vs. Health Data Scientists

The classes you’ll take can be a significant factor in helping you decide which path to pursue. Here’s a breakdown of the types of classes you can expect depending on your focus:

If You Want to Be a Biostatistician:

Biostatistics focuses more on the statistical methods that are essential for understanding and interpreting data in the health and medical fields. If you’re drawn to the world of research, clinical trials, or public health, you might be a better fit for biostatistics.

  • Statistical Methods for Public Health
  • Biostatistical Methods
  • Survival Analysis
  • Clinical Trials Design and Analysis
  • Epidemiology
  • Data Management for Health Studies

These courses will help you understand how to design studies, analyze data, and make inferences that inform public health policies and clinical decisions.

If You Want to Be a Health Data Scientist:

Health Data Science, while still grounded in statistical methods, often has a broader focus on programming, machine learning, and large-scale data analysis techniques. This field prepares you to work with massive datasets and cutting-edge technologies, including AI and data mining, to solve health-related problems.

  • Machine Learning in Healthcare
  • Data Science for Health Applications
  • Big Data Analytics
  • Health Informatics
  • Data Visualization and Interpretation
  • R and Python for Data Science

These classes will give you the technical skills to manage and analyze complex health datasets, which are key to making data-driven decisions in healthcare.

Now that we’ve looked at the classes that define each degree, let’s explore which career paths are best suited for each and where there’s overlap.

Similar Career Outcomes: Which Path Makes You a Better Fit?

While both MS in Biostatistics and MS in Health Data Science can lead to similar careers, the key differences lie in the specific roles each degree will prepare you for. Here’s a breakdown of what each degree might make you better suited for:

Careers Biostatistics Makes You a Better Fit For:

Biostatistics programs are rooted in traditional statistical theory and methods, with a focus on applying those techniques to public health, clinical trials, and epidemiology. If you’re drawn to roles that focus heavily on study design, statistical modeling, and analyzing the impact of health interventions, this is where Biostatistics shines.

  • Clinical Biostatistician: Work in clinical trials or pharmaceutical companies, analyzing data from experiments to determine the effectiveness of new drugs or treatments.
  • Epidemiologist: Involved in studying patterns and causes of diseases within populations, using statistical tools to analyze trends and develop health recommendations.
  • Public Health Analyst: Focus on using data to shape public health policies, epidemiological studies, or health outcomes research.
  • Research Scientist: Conduct studies in academic, government, or healthcare settings, applying statistical methods to health-related research questions.

Biostatistics also prepares you to work closely with researchers and healthcare professionals in developing evidence-based solutions, especially in controlled environments like clinical trials or governmental health agencies.

Careers Health Data Science Makes You a Better Fit For:

Health Data Science is more tech-forward and focuses on leveraging big data, machine learning, and computational methods to extract insights from massive healthcare datasets. If you’re excited by working with emerging technologies and analyzing large datasets to make data-driven healthcare decisions, this path is a great fit.

  • Health Data Scientist: Analyze large and complex datasets, often involving electronic health records (EHR), patient data, or insurance data, to provide insights that can improve patient care and hospital operations.
  • Machine Learning Engineer in Healthcare: Build predictive models using AI and machine learning to anticipate patient outcomes, disease progression, or optimize healthcare workflows.
  • Health Informatics Specialist: Combine technology, data science, and healthcare expertise to design systems that manage and analyze healthcare data, improving the overall efficiency of healthcare delivery.
  • Real-World Evidence (RWE) Analyst: Work with data derived from real-world sources (e.g., patient records or claims data) to evaluate the effectiveness of treatments outside of clinical trials (you can read more about this promising RWE opportunities here).

If you’re excited about technology-driven healthcare advancements, this path prepares you to dive into projects involving big data analytics, AI, and automation that are transforming the industry.

Overlapping Careers: Where Both Degrees Can Take You

Despite their differences, both Biostatistics and Health Data Science will prepare you for several overlapping career paths. These roles involve analyzing health data to inform decisions and improve patient outcomes, but the tools and methodologies you use may vary.

  • Healthcare Data Analyst: Whether you come from a Biostatistics or Health Data Science background, you could find yourself working as a data analyst, examining healthcare data (like patient outcomes, hospital performance, or disease trends) to inform decisions and strategies.
  • Biostatistician/Data Scientist in Public Health: Both fields contribute to the management and analysis of public health data, whether it’s for government agencies or NGOs.
  • Consultant in Healthcare Data: Working for healthcare organizations or consulting firms, you’ll help healthcare providers optimize data use for better decision-making. You may work on everything from improving patient care to operational efficiency.
  • Health Policy Analyst: Both Biostatistics and Health Data Science graduates can step into roles where they use data to shape health policy, analyzing how different factors influence public health systems, and making recommendations for improvements.

In these overlapping careers, both Biostatistics and Health Data Science graduates may contribute to similar projects, but the approach will differ – Biostatisticians will likely focus more on hypothesis testing and statistical models, whereas Health Data Scientists may use machine learning and big data analysis techniques to uncover trends.

The Choice is Yours

Ultimately, the decision between an MS in Biostatistics and an MS in Health Data Science comes down to your personal career interests and goals. If you love research and traditional statistical methods, Biostatistics might feel like a natural fit, while Health Data Science might speak to you more if you’re excited by machine learning and big data technologies.

Which MS course sounds the best fit for you?

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The Future of Biostatistics: A Smart Career Choice or a Risky Bet?

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Biostatistics is foundational to analyzing complex health data, from clinical trials to epidemiological studies. However, as political and funding challenges shake the scientific landscape, I know you’re probably wondering if biostatistics is still a smart career choice. The truth is – it absolutely is, and here’s why.

1. Enduring Demand in Healthcare and Beyond

Healthcare Needs More Biostatisticians Than Ever Before

As the world continues to grapple with public health challenges – from pandemics to the aging population – biostatisticians remain indispensable. The demand for experts who can translate complex health data into actionable insights is skyrocketing. Hospitals, government agencies, and non-profits need biostatisticians to evaluate medical treatments, design disease prevention programs, and assess healthcare outcomes.

Example: During the COVID-19 pandemic, biostatisticians played a key role in analyzing infection rates, vaccine efficacy, and predicting future trends. Without their work, the global response would have been far less informed. This kind of work has an enduring relevance as long as public health challenges continue to rise (spoiler: they will).

Opportunities Beyond Public Health

It’s not just public health sectors that need biostatisticians. Pharmaceutical companies, private research labs, and even tech companies have increasingly turned to biostatisticians to inform their decisions. From drug development to personalized medicine, biostatistics is a critical part of the research and development process in many industries. With research predicting that nearly 1 in 5 of the adult population will be living with a major illness by 2040, there is clearly a need for pharmaceutical companies to better serve those with these illnesses.

Example: In biotech and pharmaceuticals, biostatisticians work in clinical trials to determine the safety and efficacy of new drugs. With the rise of precision medicine, this offers more job opportunities for biostatisticians who want to work in this area.

2. Job Stability in an Evolving Field

Flexible Career Paths

The versatility of a biostatistics career is often underestimated. While many people think of the role in terms of academic research, government jobs or pharmaceutical companies, biostatisticians can also find opportunities in the private sector, such as in healthcare technology, insurance, and even healthcare analytics. The rise of data science has further expanded these career paths.

Example: If one job market falters due to political changes, others remain robust. For instance, while government funding for academic research might be unpredictable, the demand for healthcare data analysts in private tech companies is only growing, particularly with the integration of AI and machine learning into medical research. Researchers believe the use of AI in healthcare will expand substantially over the next decade, growing from a global marketplace value of almost $27 billion in 2024 to more than $613 billion by 2034.

A Secure Future with Tech Integration

With the increasing integration of technology into healthcare, biostatistics is becoming even more relevant. The rapid rise of big data and AI in medicine creates a constant need for experts who can analyze and interpret this data. This shift provides biostatisticians with new tools, making their work even more impactful – and valuable. I get it – AI is everywhere, and you’re probably wondering if it’s going to replace biostatisticians like yourself. Here’s why you don’t need to worry.

Example: In the world of artificial intelligence, biostatisticians collaborate with data scientists (or become one themselves) to build predictive models for disease outbreaks, patient outcomes, and drug interactions. With the healthcare sector continuing to adopt new technologies, biostatistics will remain at the forefront.

3. The Societal Impact of Biostatistics: A Meaningful Career

Changing the World, One Data Point at a Time

Biostatistics isn’t just about number crunching. It’s about making a real difference in people’s lives. The ability to analyze health data and inform policies that improve public health outcomes gives biostatisticians a unique sense of purpose. Their work directly influences health interventions, government policies, and access to critical healthcare services.

Example: By using statistical models, biostatisticians help design more efficient public health campaigns – be it for vaccination drives, cancer screenings, or mental health awareness programs. This broadens the scope of their societal impact beyond just academic or technical circles.

Contributing to Global Health

The ongoing need for biostatisticians to tackle complex global health challenges ensures that careers in this field remain both rewarding and impactful. Whether it’s controlling the spread of infectious diseases or addressing the health disparities caused by climate change, biostatisticians are crucial players in shaping global health initiatives.

Example: Global organizations like the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) rely heavily on biostatisticians to guide their disease modeling and health policy decisions. As long as global health crises exist, biostatistics will remain a career path with profound impact.

4. The Future of Biostatistics: Embracing Interdisciplinary Opportunities

Biostatistics and Data Science: A Powerful Combination

“Biostatistics and data science are merging more and more. It’s not just about crunching numbers anymore. Today, biostatisticians are learning machine learning, big data, and predictive analytics. This opens up tons of new opportunities.

Example: Biostatisticians skilled in data science can work alongside machine learning engineers in the tech industry, using predictive analytics to develop algorithms for early disease detection or personalized treatment plans. These interdisciplinary opportunities add layers of flexibility to their careers.

Collaboration with Economists and Policy Makers

Biostatisticians also frequently collaborate with economists, public policy experts, and epidemiologists. This multi-disciplinary approach is becoming more common as policymakers need to make data-driven decisions. For biostatisticians, this means access to a wider range of career options that extend beyond the traditional fields.

Example: In health policy, biostatisticians work with economists to evaluate the economic cost of health interventions, using data to guide the most effective allocation of resources.

5. Education and Continuous Learning: A Growing Field

Expanding Education Pathways

The demand for trained biostatisticians means that graduate programs are growing, offering more opportunities for students to specialize in areas like genomics, environmental health, or epidemiology. Furthermore, online courses and certification programs are making it easier for professionals to keep their skills up-to-date without needing to return to full-time school.

Example: Universities now offer more interdisciplinary graduate programs that combine biostatistics with data science, providing students with the skills they need to enter diverse industries.

Lifelong Learning Keeps Biostatistics Relevant

Biostatistics is not a static field. With constant advancements in statistical methods, computational techniques, and data analysis tools, there’s always something new to learn. This commitment to lifelong learning keeps the field dynamic and ensures biostatisticians are always in demand.

Example: Professionals in the field often take part in workshops, webinars, and conferences to stay up-to-date on the latest statistical methods and software tools, ensuring that their expertise remains competitive.

Biostatistics – A Smart Career Choice with a Lasting Impact

Despite the uncertainty surrounding political and funding pressures, biostatistics remains a smart career choice. The field offers stability, job flexibility, societal impact, and the chance to be at the cutting edge of technological advancements in healthcare and data science. So, do you still think biostatistics is too risky of a bet? Or are you ready to dive into a field that’s growing, impactful, and more necessary than ever?

 

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Are You Ready to Become a Senior Biostatistician? Here’s How to Tell

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As you climb the ladder, it’s about more than crunching numbers.

 

Struggling with Senior Biostatistician Expectations

If you’re aiming for a Senior Biostatistician role, you might be asking: Am I ready? While a strong technical foundation is crucial, senior roles demand more than just statistical expertise.

Most companies expect 3-5 years of experience, but getting this role often comes down to more than just the number of years worked.

The real challenge is bridging the gap between your current role and the broader responsibilities at the senior level.

It’s not just about technical skills – it’s about driving results independently, prioritizing critical tasks, and leading teams to success. These are the qualities that define a Senior Biostatistician.

In this article, I’ll explore how you can assess whether you’re ready for the senior role, and the key skills you’ll need to succeed in the next phase of your biostatistics career.

Mastering Key Senior Biostatistics Competencies

If you find yourself uncertain whether you’re ready for a senior biostatistician position, it’s time to take a closer look at how well you perform in five core areas. These skills distinguish senior professionals from entry-level biostatisticians and will help guide you toward readiness:

1. Independently Drive Results

At the senior level, you’ll be expected to lead projects, not just execute tasks. This requires you to take full ownership of your work.

Signs you’re ready:

  • You proactively identify problems and propose solutions before they escalate.
  • You manage timelines effectively and ensure that deliverables are met without constant oversight.
  • You’re comfortable working autonomously and can make data-driven decisions in the absence of direct supervision.

What to do next: If you’re unsure, ask for projects where you can lead initiatives or oversee outcomes. Practice driving results without waiting for instructions – take ownership of projects from start to finish.

2. Apply Critical Thinking with Smart Prioritization

Senior biostatisticians need to be able to quickly assess the importance of various tasks and prioritize effectively. There will be competing demands on your time, and being able to think critically about which task deserves your attention first is crucial.

Signs you’re ready:

  • You can quickly assess situations, identify high-impact tasks, and prioritize them efficiently.
  • You don’t just follow instructions; you ask the right questions to make sure the team is aligned with the bigger picture.
  • When faced with multiple tasks, you effectively balance the need for speed and accuracy, ensuring that each task gets the necessary focus.

What to do next: Hone your prioritization skills by taking on roles or projects where you must balance competing objectives. Whether it’s coordinating between teams or deciding on statistical methods for complex datasets, practice applying critical thinking under pressure.

3. Collaborate and Partner with Others Outside Your Own Silo

A senior biostatistician is not just a number cruncher – they’re a key partner in decision-making. Cross-functional collaboration becomes vital as you work alongside clinicians, regulatory teams, or even external stakeholders.

Signs you’re ready:

  • You engage with people from other departments to understand their needs and provide solutions.
  • You contribute valuable insights to multidisciplinary teams and work to bridge the gap between data and actionable outcomes.
  • You collaborate effectively across various silos, sharing insights and recommendations that lead to better decision-making.

What to do next: Actively seek opportunities to collaborate with other teams. Whether it’s working with clinical teams to understand trial designs or communicating results to non-technical stakeholders, building these relationships will strengthen your readiness.

4. Using Data, Dashboards, and Metrics to Communicate Progress

As a senior biostatistician, you must be able to clearly communicate project progress to both technical and non-technical stakeholders. This often involves managing up by using dashboards and metrics to provide transparent, digestible reports that track key milestones.

Signs you’re ready:

  • You know how to distill complex data into meaningful metrics that communicate progress clearly.
  • You can use dashboards to manage up effectively, providing leadership with timely and relevant updates.
  • You understand the importance of presenting findings in a way that guides decisions and supports ongoing projects.

What to do next: Start using dashboards to present your work to management. Focus on making your data visually accessible, highlighting key milestones, risks, and progress. Practice explaining statistical concepts in a simple way that ensures everyone on the team, including non-statisticians, understands your work.

5. Lead a Team

A senior biostatistician is often called upon to mentor junior statisticians and lead data-driven teams. This requires more than just technical expertise – it demands leadership skills, including managing people, projects, and expectations.

Signs you’re ready:

  • You’ve taken initiative in mentoring junior statisticians, offering guidance, and supporting their development.
  • You can balance team needs with project requirements, ensuring that the team is working efficiently and effectively.
  • You have experience delegating tasks and supporting team members while keeping the overall project on track.

What to do next: Seek out leadership opportunities in your current role, such as guiding interns, taking on management of smaller projects, or mentoring junior staff. Demonstrating leadership in a practical context will build your credibility as a senior candidate.

My Journey to a Senior Biostatistician Role

After three years of experience, I found myself at a crossroads, wondering if I was truly ready for the next step. As I evaluated the skills required for a Senior Biostatistician role, I realized that it wasn’t just about the technical knowledge I had accumulated – it was about developing a broader skill set. In my previous role I had focused on driving results independently, prioritizing tasks smartly, leading projects and teams and mentoring others. 

Developing this skillset along with crafting a strong, personalized resume that highlighted these skills landed me a Senior Biostatistician role and a 37.8% salary increase. The experience taught me that it’s not just about the years you’ve worked; it’s about developing the required skillset and making yourself an asset at the senior level.

How to Move Forward

Becoming a senior biostatistician is about more than just checking off a list of technical skills – it’s about demonstrating your ability to lead, collaborate, and drive results independently. If you’re not yet confident in some of these areas, focus on building your leadership capabilities, practicing critical thinking, and increasing your exposure to cross-functional work.

As you reflect on your own career path, ask yourself: How many of the skills listed in this article do you currently have? Take the time to develop these essential skills and strategically position yourself for that next big opportunity.

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The Power of Networking For Your Biostatistics Career

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Some experts suggest between 70-85% of jobs are filled through networking.

 

Why Networking Matters More Than Ever

Breaking into the world of biostatistics can be incredibly competitive. If you’re a recent graduate, someone looking for your first job in the field or even looking to job-hop, it’s easy to feel lost in a sea of applicants. With numerous other qualified candidates vying for limited positions, traditional job applications alone might not get you the job you want. This is where networking can offer you opportunities in your biostatistics career.

Networking in biostatistics isn’t just about knowing people – it’s about building authentic relationships that give you access to hidden opportunities and insights. Here’s why networking is critical for securing entry-level roles and how you can use it to your advantage.

It’s Not What You Know, It’s Who You Know (And How Well You Know Them)

In biostatistics, networking can be a game-changer. Here’s why:

  1. Hidden Job Opportunities
    Many entry-level positions never make it to job boards or company websites. They are filled through referrals, word of mouth, or internal recommendations. By networking with industry professionals, you gain access to these hidden opportunities before they are made public.

  2. Insider Knowledge
    Networking provides you with valuable insights into company cultures, team dynamics, and industry trends. These details can give you an edge in interviews by allowing you to tailor your responses to what a hiring manager is specifically looking for, based on what you’ve learned through your network.

  3. Mentorship and Guidance
    Networking isn’t just about landing a job; it’s about learning from people who have been where you are. Finding a mentor in the biostatistics field can provide invaluable career guidance, offer advice on career progression, and help you navigate the complexities of the industry.

How Networking Has Helped My Biostatistics Career

Networking has played a pivotal role in my own career. I’ve been offered job opportunities and interviews thanks to connections I’ve made within this industry. Even when other candidates may have had stronger qualifications, the personal referrals I’ve received acted as a seal of approval, helping me stand out.

Once, I got ghosted for a job, but a friend reached out to a contact at the company I applied for, and I received a screening interview within days.

This accountability also motivates me, knowing that the people who referred me have placed their trust in me. Ultimately, networking has opened doors I wouldn’t have found otherwise, and I’ve learned the importance of maintaining genuine, long-term relationships in this field.

How to Network in Biostatistics: The Practical Approach

Now that you understand the importance of networking, let’s break down how you can effectively network in biostatistics:

1. Leverage LinkedIn for Networking

LinkedIn is a goldmine for biostatistics professionals. But simply having a profile isn’t enough. Here’s how to use LinkedIn to its full potential:

  • Optimize Your Profile: Ensure your profile is up-to-date with all relevant skills, coursework, projects, and internships. Use a professional photo and a compelling headline that clearly states your career focus in biostatistics.

  • Engage with Content: Follow thought leaders in biostatistics, public health, and related fields. Like, comment, and share insightful content. Engage in conversations to ask questions, share your experience and establish yourself as an active member of the biostatistics community.

  • Connect with Purpose: Don’t just send random connection requests. Personalize your invitation with a short message explaining why you want to connect. Be specific – mention mutual interests or goals to make the interaction more meaningful.

  • Request Informational Interviews: Reach out to biostatistics professionals, alumni, or hiring managers for short informational interviews. These informal chats help you learn more about the field and can lead to future job opportunities. Remember, people generally enjoy talking about their work and are often willing to offer advice or guidance.

2. Attend Industry Events, Conferences, and Webinars

Networking in person (or virtually) remains one of the most powerful ways to connect with people in your industry. Here’s how to make the most out of such events:

  • Join Professional Organizations: Becoming a member of associations like the American Statistical Association (ASA) or the International Biometric Society (IBS) is an excellent way to gain access to exclusive events and job boards. These organizations often hold conferences, webinars, and meetups that bring together professionals in the field.

  • Speak Up at Events: When attending webinars or conferences, don’t hesitate to ask questions or even introduce yourself. Asking insightful questions can spark conversations and help you stand out.

  • Follow Up: After the event, follow up with the people you met. Send a brief LinkedIn message or email to thank them for their time and mention something specific you discussed. This will solidify the connection and keep you top of mind.

3. Utilize Alumni Networks

Your alumni network is one of the most underutilized resources when it comes to job hunting. Colleges and universities often have dedicated alumni resources that can help you connect with graduates in the field of biostatistics. Here’s how to make the most of it:

  • Reach Out to Your University’s Career Center: Many universities offer networking events or career fairs exclusively for alumni and students. These events are fantastic opportunities to meet professionals and professors who are already established in the field and may be in a position to refer you to open positions.

  • Use Alumni Directories: Some universities have online alumni directories that allow you to search for graduates by industry or job title. You can use these directories to find biostatistics professionals who may be willing to share advice or connect you with job openings.

  • Tap into Social Media: Many alumni groups also exist on platforms like Facebook and LinkedIn. Join these groups and engage with fellow alumni who may be able to offer job leads or professional advice.

Stay Consistent and Authentic

Networking isn’t a one-time task – it’s an ongoing effort. You won’t land a job overnight simply by attending one event or sending one LinkedIn message. Building authentic relationships takes time. Here are some tips for staying consistent and making sure your networking efforts pay off:

  • Follow Up Regularly: If you haven’t heard from a contact in a while, don’t hesitate to check in. Send a brief message to see how they’re doing or share an update on your progress. Keep the conversation going without being pushy.

  • Offer Value: Networking should be reciprocal. If someone offers you advice or a lead, find ways to return the favor. Whether it’s offering your skills, introducing them to one of your contact, sharing a helpful article, or providing insights into something you know well. The more you help others, the more this strengthens your professional relationships and will pay you back in dividends.

  • Be Patient: Building a network and cultivating strong professional relationships takes time. Don’t expect immediate results from every conversation. Stay consistent, and trust that the right opportunities will eventually come your way.

Conclusion: Networking Is Your Pathway to Success

Finding an entry-level biostatistics position can be challenging, but networking gives you the edge you need to stand out in a crowded field. By connecting with professionals, tapping into alumni networks, attending industry events, and engaging on LinkedIn, you’ll unlock opportunities that might otherwise be out of reach.

Remember, networking isn’t just about finding a job – it’s about cultivating relationships that can help you grow as a professional. Network relentlessly, and you’ll discover that the path to your dream job in biostatistics is not as far out of reach as it might seem.

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Why Real-World Evidence is a Promising Career Path for Biostatisticians

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rwe-career-opportunities-biostatistician Most biostatisticians pursue careers in clinical trials or public health, but there is another option: Real-World Evidence (RWE). RWE which offers a promising career for those seeking something different. In this post I’ll explain what RWE is, what you can expect when working as a biostatistician in this field, and why it’s an attractive and rewarding career choice for biostatisticians.

What is Real-world Evidence?

Real-World Evidence refers to clinical evidence of a medicinal outside of a controlled clinical trial setting. RWE is derived from Real-World Data (RWD), which comes from sources like electronic health records (EHR), insurance databases or patient registries. Now that we’ve covered the acronyms, why is RWE so important in clinical research?

The Growing Importance of RWE

RWE is a growing field in clinical research for several reasons:
  1. Real-world outcomes: RWE helps us understand the effectiveness of treatments in real-world conditions, beyond the controlled environments of clinical trials.
  2. Cost-savings: Data is cheap, clinical trials are expensive. Collecting RWD is more affordable than conducting a clinical trial. Researchers can obtain a huge amount of data without the expense of recruiting patients for trials.
  3. Industry Demand: We’re in the age of data. As technologies like AI and machine learning advance, there’s an increasing need to use RWD to inform healthcare decisions, improve patient outcomes, and shape health policies.
So, what does this mean for you as a biostatistician interested in entering the RWE field?

Real-World Evidence: A Great Career Move for Biostatisticians

RWE is an emerging field that offers many opportunities to accelerate your career. By working in RWE, you can learn new skills, transition to new roles, and make a meaningful impact in healthcare.

Skills

Working with RWD means dealing with large, complex and messy datasets. This complexity provides you with the opportunity to expand your skillset, which is excellent for your career.
  • Data Manipulation: You’ll need to master working with with massive datasets. This might involve learning new tools like Spark for data processing. You may need to brush up on your SQL skills for data extraction. There might also be a need to understand the computational burden of the code you’re running.
  • Advanced Statistical Techniques: With large datasets, you can use a wider range of statistical methods. For example, you can explore data science methods like natural language processing (NLP), cluster analysis, and use a wider array of variables in your models that aren’t feasible with smaller datasets.
  • Study Design: You’ll need to understand how to design statistical analyses with RWD. This includes considering potential biases and other important factors when using real-world data.
It may seem overwhelming at first, but mastering these skills will provide many opportunities for career growth and flexibility.

Career Flexibility and Growth

A career as a biostatistician in RWE opens the door to many roles beyond traditional biostatistics. The skills you gain can help you transition into data science, statistical engineering, or even data engineering. In my last role, I worked closely with the data engineering team to improve the accuracy of our RWD. This expanded my career opportunities and has enabled me to help enhance data quality at my current company. Career paths in RWE are more fluid and diverse than those in traditional biostatistics jobs in clinical trials or public health. This flexibility allows you to work in industries like health tech, biotech, pharmaceuticals, insurance, and government.

Impact

Biostatisticians in RWE can directly contribute to improving patient care by analyzing data that reflects real-world scenarios, bridging the gap between clinical trials and everyday healthcare. You’ll have the opportunity to work on projects that influence healthcare policy, population health, and medical decision-making on a large scale.

Project Variety

RWE roles tend to have fewer of the regulatory deadlines and strict controls compared to clinical trials, providing you more flexibility in your job role. These positions usually involve a mix of data analysis, consulting, and working with interdisciplinary teams, offering variety in day-to-day tasks.

Career Security

As the use of data in healthcare continues to evolve, RWE offers an exciting career choice for those passionate about healthcare innovation. With the healthcare industry increasingly focused on patient-centered approaches and real-world outcomes, RWE biostatisticians will play a central role in shaping the future of medicine. With the future outlook of RWE being very bright, this comes with increased career security in your role. Given the growing interest from healthcare organizations and investors in RWD, RWE offers long-term job stability and the chance to work at the forefront of healthcare research and development.

Conclusion

If you’re looking for a career that allows you to learn diverse skills, work on varied and impactful projects, and thrive in a growing field that offers career growth and security, consider a career in RWE. You can check out RWE opportunities on these job boards.

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How To Personalize Your Resume For Biostatistician Jobs

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Generic resumes might seem quicker, but personalized resumes save you time in the long run!

A common mistake when applying to biostatistician jobs is not personalizing your resume to match the specific role you’re applying for.

In this article I’ll highlight why sending a generic resume when applying to biostatistician jobs can hurt your chances and provide practical tips on how to personalize your resume to improve your chances of getting that job.

Why You Shouldn’t Send Your Generic Resume

Sending your generic resume when applying for biostatistician jobs is like trying to hammer in a screw. Sure, it might work in some cases, but it isn’t the best tool for the job. For example let’s say a company is hiring a biostatistician with experience in R, writing SAPs and hypothesis testing. If you apply with a generic resume that highlights your experience in python, data science and NLP, do you think you’re the best fit for the job? I’d say that’s a “no”. If I saw this resume, I’d assume that this applicant wants a data science role but is just applying for this job as a backup, without considering the requirements of this job. Your resume should be selling why you’re a good fit for this job, not listing your skills and experiences that you won’t be using if hired.

Quality Over Quantity

Many biostatisticians believe that applying for more jobs increases their chances of landing one. Sure, you’re increasing your surface area, you’re also spreading your resume too thin over the job market. This is especially problematic when the job market is competitive. When there aren’t many positions available, don’t waste your application. Instead of asking yourself, “How can I apply for more jobs?”, ask yourself, “How can I position myself to be the best match for this job?”. To do this, you need to think about who will be reviewing your resume.

Who’s Reviewing Your Resume?

When you submit your resume, it will likely be reviewed by one or more of the following:
  1. Applicant Tracking System (ATS)
  2. Recruiter
  3. HR
  4. Hiring Manager

Applicant Tracking System

ATS is used by all the most of the generic job websites. is an automated tool that filters out applications to help employers sort through large volumes of resumes. It does this by comparing your resume with the job advert. See the problem here? If you send a generic resume, it’s likely you won’t make it past this initial filter. If your resume doesn’t match the job advert closely enough, you will be automatically rejected before a human even sees it.

Recruiter/HR

If your resume passes the ATS, it will likely next be reviewed by a recruiter or HR. While this gives you a slightly better chance than ATs, the challenge is that the next person reviewing your resume may not have domain knowledge. They’ll typically scan resumes quickly and might use shortcuts like CTRL+F to search for key skills quickly. You essentially have a human form of ATS here. Again, if you haven’t personalized your resume to align with the job advert, you’ll significantly reduce your chances of hearing back.

Hiring Manager

If your resume reaches the hiring manager, you stand a much better chance of hearing back. Hiring managers have the expertise to evaluate whether your experience is a good fit. They may even look beyond a generic resume if they think your overall experience is valuable. However, with the high volume of applicants that each biostatistician job advert gets, the candidate who best matches the job requirements will always win. If you don’t tailor your resume, you’ll never be the best match for the job.

How To Personalize Your Resume For Biostatistician Jobs

Important reminder: Never lie on your resume. It can potentially lead to you losing the job you’ve been offered, can damage your reputation, and hinder future career opportunities. With that said, let’s move on to the key sections of your biostatistics resume that your should personalize.

What Resume Sections To Personalize

To increase your chances of landing biostatistician jobs, you need to tailor the following sections of your resume to the job’s advert:
  • Skills
  • Job titles
  • Work experience
  • Education
  • Projects
  • Publications

Skills

Most biostatistician job descriptions list the skills they expect the candidates to possess. Make sure your resume reflects these skills – if you have them. For example, don’t list experience in SAS if you’ve only ever worked in R. Feel free to include relevant skills that isn’t listed in the job advert. For example, data cleaning skills will always be relevant even if it’s not mentioned directly in the job description. The goal of this section is to make it easy for the reviewer to quickly scan your resume and confirm that you have the necessary skills for the job. This section doesn’t have to be exhaustive.

Job Titles

Your job title on your contract doesn’t have to be the exact one on your resume. Granted, you shouldn’t exaggerate but a slight adjustment can help. In biostatistics, job titles can vary widely. If your duties align with a biostatistician role, but your title is something like “Research Scientist,” consider listing yourself as a biostatistician.

This is especially important because HR or recruiters may not fully understand the nuances of your role. Changing this section is solely to get through the gatekeepers because the hiring manager will most likely understand your skills matter more than your job title.

Note: If you adjust your job title, make sure your employer would support this change if contacted for a reference.

Work Experience

This section is your opportunity to show the employer that you have the relevant experience to back up the skills. Use results-orientated actions that show how your past work aligns with the job description. For example, if the job ad mentions they’re looking for an applicant with “familiarity with mixed models and repeated measures” and you have experience with these methods, make sure to highlight it. Instead of writing: “Ran linear regression models to quantify dementia progression following intervention.” Write something like: “Applied mixed-effects models to assess within-subject variability in systolic blood pressure fluctuations, showing a 21% higher variability in patients with uncontrolled hypertension over a 12-month follow-up.” Personalizing this section is the most effective way to show you’re the ideal candidate for this role.

Education

Include any relevant coursework or classes that align with the job description but might not be covered elsewhere. For example, if the job asks for “experience with time-series data” and you took a relevant course, mention it here – even if you haven’t yet applied it professionally.

Projects

Similar to the education section, this section allows you to highlight any relevant skills or experiences you may not have shown in other sections. For instance, if you’ve done a self-directed project in survival analysis but haven’t had the chance to apply it in a formal job, this is where to showcase it. This is a perfect area to showcase your skills and experience, especially if you’re looking at landing your first biostatistics job.

Publications

If you have multiple publications, only include those that are most relevant to the role. For example, if the job focuses on oncology, prioritize your publications that are related to cancer research.

How Much Time Will This Take?

When you start personalizing your resume, it should take no more than 20-30 minutes per application. This might sound like a lot of time, but the more you do it, the quicker and easier it gets. Eventually, you’ll be able to personalize your resume in 5-10 minutes. With each new job you apply to, you’ll have a growing collection of tailored resumes and templates that will make the process even faster.

And trust me, the effort is worth it considering how much a personalized resume significantly increases your chances of landing the job.

Conclusion

Pick a random biostatistician job posting. Create a copy of your current resume, and spend just 5 minutes personalizing it for the role. Now, compare your generic resume to the tailored one. Which one would you rather hire?

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The Best Websites To Apply For Biostatistics Jobs

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time-spend--by-biostatisticians-finding-biostatistics-jobs

Looking for biostatistics jobs for your next career move? You’re in the right place!

In this article I’ll share with you where most biostatisticians go looking to find biostatistics jobs – and where you should be applying to drastically increase your chances!

Where Most People Search For Biostatistics Jobs

General Job Boards

These are the big job boards that everyone has heard of. We’re talking about Indeed, LinkedIn, Glassdoor et al.

These are ok. They have a lot of jobs available, they’re sometimes linked to the company’s profile, and they are updated fairly regularly.

But there’s a downside: spamming, bots and fake job adverts are all too common.

The big problem is that everyone applies for jobs through these job boards. The more applicants, the less likely your resume will stand out. What this means is that you have a lot more competition in getting a callbacks for these jobs.

You’ve probably seen biostatistician jobs that have been posted on LinkedIn in the last day but already has hundreds of applications – demoralising, right?

But Your Resume Will Still Get Looked At, Right?

I’ve got some bad news for you! You may think that if you submit your application in that you’ll still get your resume reviewed. Unfortunately, that’s not the case!

LinkedIn filters out applications for employers. This means that the more applications the job advert receives, the more that will be discarded by LinkedIn before the employer even gets a chance to review them.

In fact, the last company I worked for refused to review applications after they had received an arbitrary number of applications from these job boards – “Applicant number 101 – sorry you may have the resume, but we’re only reviewing the first 100″.

This means there’s a good chance the application you worked so hard on won’t ever be seen.

This is why I advise against applying through these platforms unless you have to.

So what are your other options?

Industry-Specific Job Boards (For Biostatistics Jobs)

These are catered for those in certain industries – in this case life sciences, pharmaceutical or academia.

These job boards may have fewer listings, but they’ll be highly targeted for biostatistics jobs, making them more relevant to your job search.

You will also find a lot opportunities that don’t want to advertise on the big job boards because of the amount of spam and bots that apply through these platforms.

The biggest benefit of applying through these job boards is that they get a lot fewer applications, so you stand a much better chance at getting your application through to the hiring manager.

Here are some industry-specific job boards:

 

Biostatistics Jobs Recruiters/Agencies

Recruiters and agencies can provide unique biostatistics jobs that aren’t offered elsewhere.

Some companies go through recruiters because they have a person with a very specific skillset in mind for the role. If you’ve got some unique experiences and skills mentioned in the job post, this can work out in your favour.

Companies sometimes prefer to go through this route as the recruiter can pre-qualify the candidate by conducting the initial screening interview. The problem with this is that you can end up in a scenario where you’re being interviewed twice – which isn’t ideal.

Recruiters tend to be quick to reach out and also quick to ghost. This can be frustrating if you’ve put effort into your application. The positive is that you will find out pretty quickly if they’re interested in your resume or not.

Here are some recruiting agencies offering biostatistics jobs:

Where Should I Be Applying?

So, you’ve found a great job you want to apply for on a job board or recruiters website, you should click apply right?

Not so fast!

If you apply through these channels, you will be applying through an intermediary who holds all the control in whether the employer actually receives your application.

So where should you apply?

Company Career Pages

When you apply through company career pages, you stand a much better chance of your application actually being read by a hiring manager.

This is because most people do not apply through this channel.

We as humans tend to be lazy so we go to job boards that aggregate all these jobs – it’s less effort.

But these will filter out your application, reducing your chances of getting the job. I can’t tell you how many times I’ve applied through job boards and recruiters – only to be ghosted by them. But when I applied directly through company websites, I received callbacks for interviews.

There is no harm applying through a job board if it’s a quick apply where you only send your resume in – but don’t expect results.

Applying On The Company Website

So how do you find the job on the company website?

Sometimes the job advert will name the company, sometimes it won’t. Recruiters and agencies often don’t provide the company name because they don’t want you applying directly and losing their commission.

If the job advert contains the company name – it’s simple. You find the company website’s career page and look for the job advert – click apply.

If it doesn’t contain the company name – then googling the introductory paragraph can find the job on the company’s career page in a lot of cases.

But What if the Job Isn’t on Their Company Career Page?

If the advertised biostatistics job isn’t on their company career page, you need to get as close to the source as possible. This means apply in the places where you’re most likely to have your resume read by the hiring manager. I’d order this as follows:

  1. Company’s LinkedIn page – Best chance for visibility.
  2. Industry-specific job boards – More focused and fewer applicants.
  3. General Job boards/Recruiters & agencies – Only if you must.

You can even follow this up with an email/LinkedIn message to the hiring manager if you know who this is.

Conclusion

Take a look at the current biostatistics jobs adverts from some of the above websites. If you find one that interests you – apply through the company career page, and you stand a much better chance of being noticed. Good luck!

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How To Learn R For Biostatistics

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So, you’ve started an MS and want to learn R for biostatistics? Exciting times! But if you’re struggling, you’re not alone.

Many students really struggle to get to grips with R during their Biostatistics MS (about 70% of my masters cohort).

This is a big problem!

A large proportion of Biostatistician jobs will require you to be proficient in R, so if you don’t know the difference between a vector and a DataFrame- you’re going to be limiting your chances in the job market.

In this post I’ll talk about why learning R can feel so difficult and how you can master it effectively.

Why Are You Struggling To Learn R For Biostatistics?

You’re struggling to learn R in your biostatistics MS for two reasons:

a) You’re book-smart, but not as practically inclined.

  • You have strong theoretical background but haven’t had much hands-on coding experience.

b) Your university teaches R like theory course.

  • Endless PDFs, dry lectures and minimal practical application.

But biostatistics is a highly practical field, you need hands-on experience with R is key to build confidence and competence. You won’t get that if you’re learning R the wrong way!

My Experience Learning R For Biostatistics

I made many mistakes when I first tried to learn R. Here are just some of them:

  • Reading textbooks about R instead of coding in R
  • Trying to memorise code instead of understanding how it works
  • Copy and pasting code from others without grasping the logic
  • Jumping into projects before learning the basics

Looking back, I wish I could have saved hours of wasted time by learning R differently.

I can’t go back, but at least I can help you avoid my mistakes and learn R the right way.

How To Learn R For Biostatistics (The Right Way)

In order to learn R for biostatistics applications – you need to learn it in the right order.

The steps you need to take to learn R are as follows:

  1. Learn syntax and basics
  2. Solve basic problems
  3. Follow along with real projects
  4. Start your own project

You might want to rush these steps or skip a step. This will only lead to frustration!

It’s like learning any language—if you try to write complex sentences before you know the basic words, you’ll struggle.

Similarly, in R, if you try to jump straight into complex modeling without understanding the basics of data manipulation, you’ll likely feel lost.

Step 1: Learn Syntax and Basics

Before you jump into advanced techniques, make sure you understand the syntax and basics.

This includes topics such as variables, data structures, for loops and functions.

How to do this:

  • Read or watch a tutorial, but don’t passively consume it – code along in your R console.
  • Focus on understanding, not memorizing. In the real world, no one is expecting you to remember everything.

Once you understand the code, move on to the next lesson.

Here are some free resources for you to follow:

This step may sometimes feel a bit frustrating, but it’s essential.

Before moving to the next step, make sure that you’ve covered all the concepts in one of the above resources.

Step 2: Solve Basic Problems

Now you’ve got to grips with the basics, it’s time for you to apply what you’ve learned.

You’re not quite ready to write your own project yet, but what you can do is solve basic problems.

I recommend you complete a worksheet problems or lab exercises.

Why is this the best way to practice?

  • Completing exercises will give you a chance to put your new found R skills into action.
  • Comparing your code against the solutions will provide you with immediate feedback, which will help you improve.
  • Solving small exercises gives you a sense of accomplishment that you’re actually learning R.

This step can be daunting – you may feel you don’t know enough or you will get stuck.

Don’t worry! There will be exercises where you haven’t covered the background yet. This is by design – this is what real life biostatisticians encounter all the time.

If you get stuck – go back to the resources above and look for answers, google to help you remember syntax or data types.

Remember, this isn’t cheating – this is exactly what many biostatisticians do daily in our jobs to help rejog our memory.

One thing I don’t recommend you doing is to find the answer using google or chatGPT (it can be wrong). You won’t be learning – you will be copying.

You can use these resources to answer questions like “how do I run a for loop in R” but not “A scientist needs to experiment upon 4 conditions, 5 times each. Generate a vector of length 20, representing these conditions in R.”

Notice how the first gives you tools to solve the problem, the second answers the problem for you! Here are a some free exercise worksheets with solutions:

If you’ve managed to answer all the questions in one of the worksheets – feel free to move onto the next step.

Step 3: Follow Along with Real Projects

Now that you’ve built a foundation, it’s time to see how R is used in real biostatistics projects.

Finding industry projects or someone’s PhD code might prove difficult at this step but you can still follow along with real projects.

The best way to find these projects is with kaggle.

Kaggle is a community for biostatisticians and data scientists to share, test, and stay up-to-date on all the newest techniques. There is a huge repository of community-published projects. The best thing is that they are awarded with upvotes if they are useful. This is where you’re going to discover a project to follow along with.

Your goal:

  • Read through someone else’s project code.
  • Try to understand their code – don’t just skim it.
  • As yourself questions – what is the code doing? Why have they used a specific function?

ChatGPT can be used at this step to clarify anything you don’t understand – but try to understand it yourself first.

Here are some of the best biostatistics projects in R to follow along with:

If you’re comfortable with what the code is doing, move on to the next step.

Step 4: Start Your Own Project

Now for the exciting part – to start your own project.

To start, you can try to run a similar analysis from the project in the last section but run it for a different dataset.

Don’t look at the analysis others have done on the dataset you’ve chosen to avoid any preconceptions about what analysis to do or what features the dataset may have.

If you feel confident enough, feel free to design the analysis yourself. Here are some sections you can use to guide you in your analysis:

  • Exploratory Data Analysis (EDA)
  • Data Cleaning (if needed)
  • Data Transformation
  • Model Building
  • Model Evaluation
  • Model Interpretation
  • Model Visualization

If you get stuck – the R and Biostatistics communities are incredibly active and willing to help. Don’t hesitate to ask questions on Stack Overflow and community subreddits (r/biostatistics, r/RLanguage, r/rstats).

Here are some biostatistics datasets you can use:

Conclusion

Follow these steps to learn R for biostatistics! By following these steps, you’ll not only improve your skills for your MS but also enhance your job prospects by building strong technical skills that employers value (remember to add the project to your resume).

There’s more where that came from.

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Are Biostatisticians Safe From The Threat of AI?

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The End of Biostatisticians?

The era of AI is upon us. Naturally, this means a lot of people are questioning whether their jobs will be automated by AI. Biostatisticians are particularly worrisome when it comes to this matter. This probably has something to do with our analytical, overthinking brains that makes us so inclined to this field in the first place. Personally, I don’t think we need to be so worried.

Reasons Why Biostatisticians Are Safe

Here are some 10 reasons why I am not worried about AI taking biostatistician jobs, and you shouldn’t be either.

1. AI would be a threat to most jobs, not just biostatistics – If biostatisticians can be replaced – so can most other jobs, so at least we’ll all be unemployed. Adaptability is key in any field.

2. AI is a tool, not a replacement – I believe biostatisticians who integrate AI will be in demand. It will be used as a tool in your work, but it will not replace your need to use the tool.

3. We haven’t reached sentience (yet) – Human oversight in healthcare and statistics remains crucial. Until we have an iRobot situation, we shouldn’t be worrying so much – you’ll still need to oversee what AI is doing.

4. Healthcare and pharma are highly regulated industries – AI adoption is slower due to ethical, legal, and compliance concerns. The general public has a scepticism to “big pharma” as it is – try telling them that AI is in control.

5. AI-generated results will still require human expertise – AI can automate tasks, but statistical reasoning and validation are human-driven. Ask biostatisticians if chatGPT is even close to doing all their work now.

6. Pharmaceutical companies rely on regulatory approval – AI can’t yet handle FDA/EMA compliance, clinical trial design, or medical ethics alone. Regulatory bodies will not accept AI doing this work.

7. AI bias and errors make human verification essential – AI models can produce misleading results if not checked by experts. We’ve all had chatGPT make a mistake when you’ve asked it a question, bias and errors will happen.

8. Developing AI for biostatistics requires biostatisticians – AI models will depend on biostatisticians to develop, maintain and improve their functionality. Even if AI takes over, you will still be needed to maintain our AI overlords.

9. Public trust in AI-driven healthcare is low – Decision-making in medicine still requires human professionals to ensure accountability. Just like you wouldn’t fully trust chatGPT diagnosing you with a disease – trusting it to design studies and analyze complex data is fraught with doubt.

10. New AI-driven job roles will emerge – Biostatisticians who upskill in AI/ML will have opportunities in adjacent fields. Health data science and medical AI development will likely grow – this allows the savvy biostatistician to have some flexibility in the jobs they apply for.

Not convinced?

Look up recent job postings for biostatistician jobs. Notice that how there are still jobs available. Take a look at how many list AI/ML skills as requirements. This is a great sign that the field is adapting to AI rather than being replaced by it, and it shows that there is still an ongoing demand for biostatisticians.  

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