HRB Clinical Research Facility - University College Cork’s Post

Careers in biostatistics offer exciting opportunities at the intersection of statistics and public health. Biostatisticians play a crucial role in designing research studies, analyzing data, and interpreting results in the context of health and medicine. Here are some common career paths in biostatistics: Biostatistician: Biostatisticians design studies and experiments to address research questions in biology, medicine, and public health. They analyze data using statistical software and interpret the results to make informed decisions. Epidemiologist: Epidemiologists study the patterns, causes, and effects of diseases in populations. They use statistical methods to analyze data and identify risk factors for diseases, as well as to evaluate the effectiveness of interventions. Clinical Trial Statistician: Clinical trial statisticians design, analyze, and report on clinical trials to assess the safety and efficacy of new drugs, treatments, or medical devices. They ensure that trials are conducted ethically and that the data collected is valid and reliable. Public Health Statistician: Public health statisticians work in government agencies, non-profit organizations, or research institutions to analyze public health data and develop strategies to improve health outcomes in communities. Health Services Researcher: Health services researchers study how healthcare is delivered and used, with the goal of improving the quality, cost-effectiveness, and accessibility of healthcare services. They use statistical methods to analyze data on healthcare practices, outcomes, and costs. Biostatistics Faculty/Researcher: Biostatistics faculty members teach and conduct research at universities, contributing to the development of new statistical methods and their application to public health and medical research. Data Scientist (Healthcare): Data scientists in healthcare use statistical and machine learning techniques to analyze large datasets, such as electronic health records, to extract valuable insights that can inform decision-making and improve patient outcomes. To pursue a career in biostatistics, a strong background in statistics, mathematics, and computer science is essential. Advanced degrees, such as a Master's or Ph.D. in biostatistics or a related field, are typically required for most positions in this field. #BiostatisticsCareers #PublicHealth #Statistics #HealthcareResearch #Epidemiology #ClinicalTrials #DataAnalysis #HealthServices #ResearcherLife #DataScience #AcademicResearch #HealthcareAnalytics #HealthData #CareerGoals #STEMCareers Pic credit: www.usc.edu

🌟 Who's eligible for a Clinical SAS course? Let's break it down! 📚 🧬 Life Sciences: Dive into biology, biotechnology, genetics, and more! 💊 Pharmacy: Perfect for those with a passion for drug development and pharmacology. 👩⚕️ Medicine: Doctors, healthcare pros, and researchers, this is for you! 📊 Statistics/Biostatistics: Love numbers? Analyze clinical data like a pro! 💻 Computer Science/IT: Tech-savvy individuals with a knack for programming. 🏥 Public Health/Epidemiology: Help improve population health with data insights. 👩⚕️ Nursing: Nurses, step into the world of clinical research and analytics! 🛠️ Engineering: Biomedical, chemical, or electrical, your skills are valuable in healthcare analytics! Ready to embark on a journey in Clinical SAS? Check the eligibility criteria and start your adventure! Visit: www.emrti.com For more information: +91 8050276200 #ClinicalSAS #HealthcareAnalytics #EducationGoals #LifeSciences #PharmacyEducation #MedEd #Statistics #Biostatistics #TechInHealthcare #PublicHealth #NursingEducation #EngineeringInHealthcare #DataAnalytics #CareerDevelopment #HealthTech #emrti #institute #emrtiinstitute #empowerresearch #ClinicalTrials #MedicalResearch #Healthcare

It appears to me that many people struggle to understand the modern scientific method of investigation. A few weeks ago, I talked to a guy I helped with data analysis, and he told me he doesn't believe any research findings because it makes no sense to study a few people and use the result to generalize people who didn't participate in the study. “How can you study men in America and say the result applies to me? Does that make sense?” he asked. I explained that the generalization isn't made arbitrarily. The study must yield a statistically significant result before it can be used to generalize a broader and wider context. The effectiveness of the medications you buy was tested on a few people during clinical trials. This is because it is practically impossible to study the whole world. But once a few people are studied and it is confirmed that the study yields a statistically significant result, researchers can conclude that the medication will to a certain degree be potent at addressing the illness it is designed to combat. Imagine having to make everyone take the vaccine during clinical trials before its efficacy was confirmed. We would probably still be battling with COVID-19 today. First, it is cripplingly time-consuming. Second, it is financially cumbersome. If scientists and researchers have to get data from everyone to discover new knowledge, we will never know anything about the world. That's why no one ever gives you an accurate figure of the total number of Nigerian citizens. In fact, the word census owes the debt of its lexical provenance to the Latin word “censere,” which means “to estimate.” As a result of this, researchers and scientists study a small group of people and use the result to generalize the main population. This is known as sampling. However, certain conditions must be met before the findings of a study are considered credible and fit for generalization. First, the sample must be random. That is, each member of the population must have an equal chance of being selected. There are two types of sampling: probability sampling and non-probability sampling. Probability sampling is used when the objective of the research is to generalize a wider context. When you want to niche down, you might use a non-probability sample like a snowball or convenience sampling. Another consideration is representativeness. When we talk about a representative sample in research, we mean the participants share similar qualities, traits, features, or characteristics. Consider this: You want to gauge the perception of all Nigerians about an issue and only survey people on Twitter. That's not a representative sample. Another consideration is the sample size. The larger the sample size, the better and the more credible the result. You can't study 50 people and conclude that their opinions or circumstances reflect the entire population. To be continued. ©️ Gbolahan A. Salahudeen

Attention Students, In the healthcare sector, data stands as the cornerstone of valuable information. Effectively managing and analyzing this data to draw valid inferences demands the expertise of a biostatistician. Join us for an exclusive webinar titled "Biostatistics: A Key Component of Healthcare Analytics" where we delve into the pivotal role biostatisticians play in today's data-driven era. Biostatistics holds critical significance across biology, medicine, public health, and related domains, serving as a linchpin for advancing our understanding of biological processes, enhancing healthcare outcomes, and guiding public health decisions. During this webinar, you will gain insights into: * An overview of Biostatistics * Career opportunities * Required skills and qualifications * Trends in the pharmaceutical industry * Its indispensable role in medical research Date: 07 March 2024 Time: 3:00 PM to 4:00 PM Don't miss this invaluable chance to carve your path in one of the fastest-growing sectors. Register now and take the first stride towards a fulfilling career in Healthcare Analytics! Link to Join: https://shorturl.at/ijtxZ

Data is all around us! 👨💻 More data is available than ever before, especially in health science. Data is important, but if you can't analyze/interpret it, it just stays... DATA! It is a crucial skill to analyze data and use it to make informed decisions. If you have a background in statistics, biology, genetics, epidemiology, bioinformatics, computer science or engineering... Consider applying for this opportunity: 🔎 A Master of Science in Computational Health Informatics. 📉 Deadline: 31st of August More information below 👇 ♻ Reshare with your network so that no one misses this great opportunity! #ComputationalHealth #MasterofScience

Data analytics and biostatistics are essential disciplines in the realm of data science, each with distinct focuses and applications. Data analytics encompasses a broad spectrum of techniques and methodologies aimed at extracting insights and value from data across various domains, including business, finance, and marketing. It involves processes such as data mining, predictive modeling, and machine learning to uncover patterns, trends, and correlations that inform decision-making and drive innovation in diverse industries. In contrast, biostatistics specializes in the statistical analysis of data within the field of biology and healthcare. Biostatisticians play a critical role in designing experiments, conducting clinical trials, and analyzing data to understand biological phenomena, evaluate treatment efficacy, and inform public health policies. By applying statistical methods to biological research, biostatistics contributes to advancements in medicine, genetics, epidemiology, and other areas, ultimately improving healthcare outcomes and enhancing our understanding of the natural world.#biostatistics #DataScience

In our brand new collaborator article, Eric J. Daza, DrPH, MPS explores the evolution of his work from causal inference in biostatistics to pioneering methods for observational causal inference using n-of-1 time series data, driven by the promise of personalized digital health insights. 👇Click below to read more https://lnkd.in/eiiMQaWk #biostatistics #statistics #data #science #education #career #blog #causalInference — At biostatistics.ca our mission is to create an information-rich community around biostatistics. Do you have interesting career stories, project successes or technical skills you would like to share with the community? Send us a message and become a certified biostatistics.ca writer!

🎓 Excited to share that I have successfully completed the Introduction to Biostatistics course with NPTEL! 📊 I've spent the last 8 weeks delving into statistical methods and their application in biological sciences, guided by expert instructors. This course has honed my data analysis skills and deepened my grasp of interpreting complex biological data, essential for research in healthcare and life sciences. I am eager to implement these learnings in my current projects and further explore the realm of biostatistics! #Biostatistics #DataAnalysis #LifelongLearning #HealthcareResearch #NPTEL

𝗗𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗼𝗳 𝗮 𝗰𝗮𝗿𝗲𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗺𝗲𝗲𝘁𝘀 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻? 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟲 𝗯𝗶𝗼𝘁𝗲𝗰𝗵 𝗿𝗼𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗮𝗿𝗲 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲—𝗮𝗻𝗱 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗯𝗲 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻! Biotechnology isn’t just a field; it’s a revolution reshaping healthcare, agriculture, and beyond. If you’ve ever wondered how to merge your passion for science with a high-impact career, these top 6 biotech roles are your roadmap to success: Biotech Analyst – Bridge science and economy (Avg. Salary: ₹6.8 LPA) Research Scientist – Uncover groundbreaking discoveries (Avg. Salary: ₹5.4 LPA) Biochemist – Decode life’s chemistry (Avg. Salary: ₹5.2 LPA) Epidemiologist – Fight diseases at the root (Avg. Salary: ₹5 LPA) Bioinformatics Specialist – Where biology meets big data (Avg. Salary: ₹9.8 LPA) Genetic Counselor – Guide lives with genetic insights (Avg. Salary: ₹5.3 LPA) Any questions? Ask them in the comment box! #Biotechnology #LifeScience #Studentjourney #CareerDevelopment #BiotechStudent #Research #Bioinformatics #STEM #Jobs #Epidemiologist #BiotechAnalyst #GeneticCounselor #genetics

Sure, here's a LinkedIn post for you: --- 🚀 Exciting News! 🚀 I'm thrilled to share that I've recently completed a course in Biostatistics! 🎓📊 This journey has significantly enhanced my analytical skills and deepened my understanding of statistical methods in the biomedical field. From mastering data analysis techniques to interpreting complex biological data, this course has been an incredible learning experience. I'm eager to apply these skills to drive data-driven decisions and contribute to impactful research and projects. A big thank you to Alison Online education Institute #Biostatistics #LifelongLearning #DataAnalysis #SkillUpgrading #ProfessionalDevelopment #research #medicalresearch #pharmacology #Pharma