Sachit Ganapathy Biostatistics, Research Methodology, R Studio
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Hello and welcome! I am delighted to introduce myself as an online tutor specializing in Biostatistics. With a strong academic foundation and over eight years of professional experience, I am here to guide students, researchers, and professionals through the intricacies of statistical concepts and applications in the biomedical and healthcare domains.

I hold a PhD in Biostatistics, with my research focused on Bayesian Network Models, a powerful probabilistic tool used for modeling complex relationships in biological systems and medical data. This work has deepened my understanding of predictive modeling, uncertainty quantification, and decision-making processes in healthcare — skills that are crucial for any modern biostatistician.

Over the years, I have accumulated substantial experience in both academic and applied settings. I served as a biostatistics faculty subsidiary member in a reputed medical institute, where I conducted regular classes for postgraduate students, helping them grasp statistical concepts and apply them to their research projects. I also led numerous workshops and training programs for both postgraduate students and faculty members, with a focus on practical, hands-on statistical analysis using tools like R and SPSS.

Currently, I am working in the pharmaceutical industry as a Principal Biostatistician at a leading global biopharmaceutical company. My current role involves working on oncology clinical trials, where I contribute to the design, analysis, and interpretation of complex datasets that are critical for regulatory submissions and treatment evaluations. This industry experience has equipped me with insights into real-world applications of biostatistics, including protocol development, endpoint analysis, and regulatory reporting.

Throughout my career, I have also been actively involved in statistical consultancy services, providing guidance to researchers and clinicians in designing studies, selecting appropriate statistical methods, analyzing data, and interpreting results. My ability to bridge the gap between statistical theory and biomedical practice is one of my core strengths as a tutor.

As a tutor, I am passionate about making biostatistics accessible and engaging. I believe in a student-centered approach where I tailor my teaching style to match the learner’s background, pace, and goals. Whether you are a beginner trying to understand basic concepts like hypothesis testing or an advanced student working with survival analysis or Bayesian methods, I am here to help.

In terms of technical skills, I am moderately proficient in R programming, which I regularly use for data analysis, visualization, and simulation tasks. I often collaborate on interdisciplinary projects that require statistical modeling and data interpretation, and I enjoy sharing these real-world examples with my students to enhance learning.

In summary, I bring together a unique combination of academic rigor, industry experience, and a passion for teaching. My mission as an online tutor is to empower students and professionals to confidently apply biostatistics in their academic, clinical, and research endeavors. I look forward to working with you and supporting your journey in mastering biostatistics.

Subjects

Research Methodology Beginner-Expert
R (Programming) Beginner-Intermediate
Biostatistics Beginner-Expert
Biostatistics and Epidemiology Beginner-Expert

Experience

PhD Scholar (Jun, 2018–Feb, 2024) at Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), India

PhD Scholar working on my Bayesian Network Modeling, statistical consultancies for medical research, teaching Biostatistics and statistical software.

Education

Ph.D. (Jul, 2018–Feb, 2024) from Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), India
MSc (Jun, 2016–Jun, 2018) from Manipal Academy of Higher education
B.Sc (Jul, 2013–Jun, 2016) from Yuvarajas College, Mysuru

Fee details

₹500–5,000/hour (US$5.26–52.63/hour)

Basic Biostatistics - Rs 500 - 1000/-
Intermediate Biostatistics - Rs 1000 - 2500/-
Advanced Biostatistics - Rs 1500 - 5000/-

Courses offered

Sampling
- ₹1500
- Duration: 2 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
When teaching sampling in biostatistics, I focus on helping students understand the importance of selecting representative subsets from larger populations to make valid inferences. I cover key concepts such as population vs. sample, sampling techniques (simple random, stratified, cluster, and systematic sampling), and the implications of sampling bias and sample size. Emphasis is placed on real-world medical and clinical examples to illustrate how proper sampling affects the reliability of study results. I also integrate hands-on exercises using Excel/R to demonstrate random sampling and simulate sampling distributions, helping students build both conceptual understanding and practical skills.
Summarisation of Data
- ₹1500
- Duration: 2 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
In teaching data summarization, I guide students through the foundational step of organizing and interpreting raw data using descriptive statistics. This includes measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation, interquartile range), and data visualization techniques such as histograms, box plots, and bar charts. I emphasize the importance of selecting appropriate summary methods based on data type and distribution. Using real-life medical datasets and tools like R, students learn to summarize and present data effectively, enabling them to extract meaningful insights and prepare for more advanced analysis.
Sample size estimation
- ₹1500
- Duration: 4 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
When teaching sample size estimation, I emphasize its critical role in designing statistically sound studies that are both efficient and ethically responsible. Students learn how to determine the appropriate sample size based on study objectives, expected effect size, variability, power, and significance level. I cover methods for both descriptive and comparative studies, including sample size calculations for means, proportions, and hypothesis testing. Using practical examples from clinical research and R-based tools, I help students understand how to apply formulas and software to estimate sample size accurately, ensuring robust and reliable study results.
Research Methodology
- ₹2000
- Duration: 4 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
In teaching research methodology estimation, I focus on equipping students with the skills to design robust studies and choose appropriate statistical methods for estimating key parameters. This includes understanding study designs (cross-sectional, cohort, case-control, experimental), types of variables, and selecting the correct estimation techniques for means, proportions, rates, and relative risks. I emphasize the connection between research questions and estimation strategies, ensuring students can align objectives with appropriate methods. Through real-world biomedical examples and hands-on practice using R, students gain practical experience in conducting accurate and meaningful statistical estimations within the research process.
Career opportunities in Biostatistics
- ₹1000
- Duration: 1 Hour
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
While teaching career opportunities in biostatistics, I aim to expose students to the wide range of paths available in this dynamic and evolving field. I highlight roles in academia, pharmaceutical industries, clinical research organizations, public health institutions, regulatory agencies, and tech companies. Emphasis is placed on how biostatisticians contribute to clinical trials, epidemiological studies, health policy planning, data science, and real-world evidence generation. I also guide students on essential skills such as statistical software proficiency (e.g., R, SAS), communication, and interdisciplinary collaboration, helping them prepare for competitive and impactful careers in biostatistics.
Evaluation of diagnostic tests
- ₹1000
- Duration: 2 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
When teaching diagnostic test estimation, I focus on helping students understand how to evaluate the performance and accuracy of medical tests. Key concepts include sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios. I emphasize the interpretation of 2x2 contingency tables and the importance of disease prevalence in influencing test outcomes. Through real-world clinical examples and practical exercises using R, students learn to calculate and interpret these measures, enabling them to critically assess diagnostic tools used in medical research and practice.
Tests of significance
- ₹1500
- Duration: 3 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
In teaching test of significance estimation, I guide students through the process of making informed decisions based on sample data. Core topics include formulating hypotheses, understanding p-values, confidence intervals, and performing common tests such as t-tests, chi-square tests, and ANOVA. I emphasize choosing the right test based on data type and study design, and interpreting results in the context of biomedical research. Practical examples and hands-on analysis using R help students apply these concepts confidently, enhancing their ability to draw valid conclusions from statistical evidence.
Regression analysis
- ₹2000
- Duration: 3 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
While teaching regression analysis estimation, I focus on helping students understand how to model relationships between variables and make predictions. Key topics include linear regression, logistic regression, and multiple regression models, along with the interpretation of coefficients, confidence intervals, and goodness-of-fit measures. I emphasize assumptions, model diagnostics, and variable selection techniques. Using real medical datasets and R programming, students gain practical experience in fitting and interpreting regression models, enabling them to apply these methods effectively in clinical and epidemiological research.
Survival analysis
- ₹2000
- Duration: 2 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
In teaching survival analysis estimation, I help students understand how to analyze time-to-event data commonly seen in clinical and epidemiological studies. Key topics include censoring, survival functions, Kaplan-Meier estimation, log-rank tests, and Cox proportional hazards models. I emphasize the interpretation of survival curves and hazard ratios, along with checking model assumptions. Through real-life clinical trial examples and hands-on practice using R, students learn to apply survival analysis techniques to assess treatment outcomes and patient prognoses accurately.
Introduction to Bayesian methods
- ₹1500
- Duration: 2 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
In teaching an introduction to Bayesian methods estimation, I focus on building a strong conceptual foundation in Bayesian thinking, where prior knowledge is updated with data to make inferences. Key topics include Bayes' theorem, prior and posterior distributions, likelihood functions, and credible intervals. I guide students through comparisons with frequentist methods and demonstrate practical applications in clinical research. Using R and Bayesian tools like JAGS or Stan, students gain hands-on experience in performing basic Bayesian analyses, enabling them to apply these methods to real-world biomedical problems with a probabilistic perspective.
Probability and application
- ₹1500
- Duration: 3 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
When teaching probability estimation in biostatistics, I introduce students to the fundamental concepts of randomness, uncertainty, and chance that underpin all statistical methods. Topics include basic probability rules, conditional probability, independence, Bayes' theorem, and probability distributions such as binomial, Poisson, and normal. I emphasize real-life medical and health-related examples to illustrate these concepts, helping students understand how probability is used to model biological variation and assess risks. Interactive sessions and simulations using R help reinforce theoretical understanding through practical application.
Basic epidemiology
- ₹2000
- Duration: 4 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
In teaching epidemiology within the context of biostatistics, I focus on the application of statistical methods to study the distribution and determinants of health-related events in populations. Key topics include study designs (cohort, case-control, cross-sectional), measures of disease frequency (incidence, prevalence), and measures of association (odds ratio, relative risk). I emphasize data interpretation, bias, confounding, and effect modification. Using real epidemiological datasets and R for analysis, students learn to apply statistical techniques to support evidence-based public health and clinical decision-making.
Statistical Inference
- ₹1500
- Duration: 3 Hours
- Delivery mode: Online
- Group size: Individual
- Instruction language: English
- Certificate provided: No
When teaching inference in biostatistics, I focus on helping students draw meaningful conclusions from sample data about a larger population. Core topics include point and interval estimation, hypothesis testing, p-values, confidence intervals, and understanding type I and type II errors. I emphasize the correct interpretation of statistical results in biomedical contexts and the importance of linking statistical findings to clinical relevance. Through practical examples and analysis using R, students gain hands-on experience in making data-driven inferences that support sound scientific decision-making.