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10+ Yrs | R, SPSS, Power BI, Tableau & Excel Tutor
John G.

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About John

Bio

Hi, I'm John. I hold a PhD in Statistics and have spent my career applying statistical methods and data tools to solve real analytical problems, from academic research to industry consulting. Over the years I've worked across the full pipeline of data analysis: designing studies, running the numbers, and translating results into decisions that non-technical stakeholders can actually use. That range is what I bring to tutoring - I've sat on both sides of the table, as the researcher wrestling...

Hi, I'm John. I hold a PhD in Statistics and have spent my career applying statistical methods and data tools to solve real analytical problems, from academic research to industry consulting. Over the years I've worked across the full pipeline of data analysis: designing studies, running the numbers, and translating results into decisions that non-technical stakeholders can actually use. That range is what I bring to tutoring - I've sat on both sides of the table, as the researcher wrestling with a stubborn dataset and as the consultant explaining findings to someone who's never seen a regression table in their life.

I work with students at every stage, from undergraduates taking their first statistics course to graduate students and working professionals tackling a dissertation, capstone, or workplace analytics project. My core areas are statistics (hypothesis testing, regression, ANOVA, multivariate analysis, experimental design), R programming (data wrangling, visualization, statistical modeling, and R Markdown), SPSS (running and interpreting the procedures most common in coursework and thesis work), Power BI (dashboards, DAX, data modeling), Tableau (dashboard design, calculated fields, and data storytelling), and Excel/spreadsheet modeling (forecasting, optimization, and tools like Solver and Analytic Solver for financial and statistical modeling).

My approach is to meet students exactly where they are. Some need to rebuild a shaky foundation from the ground up; others are troubleshooting a project the night before a deadline; still others need someone to walk with them, step by step, through a dissertation's methodology chapter. Whatever the starting point, I always explain the concept in plain language first, then connect it to the software - so students walk away understanding why an analysis works, not just which buttons to click. I'm patient, responsive, and glad to answer follow-up questions between sessions, because good tutoring is ongoing support, not just a weekly hour.

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Education

Northwestern University
Statistics
Loyola University
Masters
University of CA- Irvine
PhD
  • Licensed teacher

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Approved Subjects

Adobe Premiere

Adobe Premiere

Adobe Premiere Pro is the tool I have used most consistently for video editing work, and my experience with it covers the full production pipeline from initial project setup and media organization through multi-track timeline editing, audio mixing, color correction, and final export optimization for different delivery formats and platforms. I have worked with Premiere's more advanced features including nested sequences for managing complex edits, dynamic link integration with After Effects for incorporating motion graphics, and Lumetri Color tools for achieving consistent, professional-grade color grading across longer projects that require visual continuity between scenes. Beyond my own production work, I have guided students and clients through Premiere Pro workflows, teaching them how to structure a project efficiently from the start, work non-destructively so edits can be revised without losing original footage, and troubleshoot the rendering and export issues that tend to frustrate beginners most. My approach to teaching Premiere follows the same principle I apply to other technical software, making sure the underlying logic of how the program handles media, timelines, and effects is clear before diving into specific techniques, because that foundational understanding is what allows students to solve problems they have not encountered before rather than needing to ask for help every time something unexpected happens.
Biostatistics

Biostatistics

My doctoral training in statistics included substantial exposure to biostatistical methods, covering survival analysis, longitudinal data modeling, clinical trial design, and the analysis of correlated outcomes that arise when observations are nested within patients, clinics, or time points, which are methodological areas that sit at the core of biostatistics as a discipline distinct from general applied statistics. I have worked with health-related datasets in consulting contexts, applying methods such as Kaplan-Meier estimation, Cox proportional hazards regression, logistic regression for binary health outcomes, and mixed effects models for repeated measures data collected across multiple time points in observational and experimental study designs. On the tutoring side, I have supported students in nursing, public health, and health professions programs who were working through biostatistics coursework that required both conceptual understanding of study design and hands-on data analysis using software such as SPSS, R, and Jamovi. A consistent challenge I see with biostatistics students is connecting the statistical output back to a clinically meaningful interpretation, and I focus sessions specifically on building that bridge between what the numbers say and what they mean for the health question the study was designed to answer.
Data Analysis

Data Analysis

My data analysis experience was forged in environments where the stakes of getting it wrong were tangible — as a financial analyst building quantitative models that informed investment decisions, and as a consultant producing analytical deliverables for state and federal agencies where findings directly shaped policy outcomes and program funding. These professional contexts demanded a level of analytical discipline that goes well beyond running procedures correctly — it required knowing which method to apply given the structure and limitations of the data, identifying when results were misleading despite being technically accurate, and communicating findings in ways that non-technical stakeholders could act on with confidence. My doctoral training in applied statistics provided the theoretical foundation underlying every analytical decision I made in these contexts, from study design and variable operationalization through assumption validation, model selection, and results interpretation across tools including R, Python, Stata, SPSS, and Excel. In tutoring, I bring this same professional discipline to undergraduate and graduate students working on data analysis projects, helping them develop not just technical proficiency in their chosen software but the analytical judgment to approach data with the rigor, curiosity, and critical thinking that separates meaningful analysis from mechanical output.
Microsoft Excel

Microsoft Excel

My graduate training in advanced data analytics included coursework that was heavily Excel-based, covering spreadsheet modeling for business decisions, scenario and sensitivity analysis, and structured approaches to building workbooks that are both analytically sound and easy for others to audit and maintain, which gave me a level of Excel fluency that goes beyond general familiarity with the software. I have also tutored students specifically through Excel-intensive analytics courses at the graduate level, including work on spreadsheet modeling for business decision analysis where the assignments required building functional, well-structured models from scratch rather than simply entering data into an existing template. At the more advanced end I have worked with students and professionals on Power Query for automating data transformation workflows, dynamic array functions introduced in newer Excel versions, and cross-referencing data across multiple workbooks using structured table references, which are skills that tend to separate intermediate Excel users from those who can handle genuinely complex analytical tasks. My tutoring approach in Excel is always to make sure students understand the logic Excel is applying when it evaluates a formula, because that understanding is what allows them to troubleshoot errors independently and adapt what they know to problems they have not seen before.
Philosophy

Philosophy

My doctoral training required sustained engagement with philosophical reasoning, particularly in areas touching on the philosophy of science, statistical inference, and the epistemological foundations of empirical research. Working at that level meant grappling seriously with questions about how knowledge is justified, how evidence supports conclusions, and how logical structure determines the validity of an argument, which gave me a working fluency in philosophical argumentation that goes well beyond what a casual familiarity with the subject would provide. That foundation has made me comfortable engaging with primary philosophical texts and the kinds of abstract, carefully reasoned arguments that define the discipline at every level from introductory coursework through graduate seminars. On the tutoring side, I have worked with students in philosophy courses covering logic, ethics, critical thinking, and epistemology, helping them move from struggling with dense readings to constructing well-reasoned arguments of their own in writing. A recurring challenge I see is students who can follow a philosophical argument when it is explained to them but freeze when asked to evaluate or build one independently, and closing that gap is something I focus on deliberately in sessions. I find that students who study philosophy alongside quantitative subjects benefit particularly from seeing how formal logical structure underlies both disciplines, and drawing that connection explicitly tends to accelerate understanding on both sides.
Psychology

Psychology

Psychology as a subject covers a remarkably broad range of content areas, and my tutoring experience spans the foundational courses that form the backbone of most psychology programs, including biological bases of behavior, sensation and perception, learning and conditioning, memory and cognition, social psychology, developmental psychology across the lifespan, and personality theory, giving me the content coverage needed to support students across introductory and intermediate coursework without narrowing to a single subfield. I have a particular familiarity with abnormal psychology and psychopathology, including the diagnostic frameworks outlined in the DSM and the theoretical perspectives, biological, cognitive, behavioral, and sociocultural, that psychology uses to explain the etiology, presentation, and treatment of psychological disorders, which tends to be one of the most content-heavy and exam-intensive courses in any psychology curriculum. Working with psychology students over an extended period has also given me genuine insight into the specific academic demands of the discipline beyond content knowledge, including how to read and critically evaluate empirical journal articles, how psychological theories are constructed and challenged through research, and how to write clearly about complex behavioral and mental phenomena in a way that meets the standards psychology instructors expect. That combination of content breadth, familiarity with the discipline's intellectual frameworks, and understanding of what psychology courses actually assess is what I draw on most directly when supporting students through this subject.
R

R

My experience with R spans well beyond standard statistical procedures into areas that are specific to the language itself, including writing custom functions to automate repetitive analytical tasks, managing package dependencies across different R environments, and using version control alongside R projects to maintain clean, reproducible codebases for research that needs to be shared or resubmitted across multiple collaborators. I have worked extensively with domain-specific R packages including lavaan for structural equation modeling, mice for multiple imputation of missing data, survey for complex sampling designs, and caret and tidymodels for supervised machine learning workflows, which represent the kinds of advanced R applications that go well beyond what introductory and intermediate courses typically cover. On the applied side, I have used R Shiny to build interactive data applications that allow non-technical clients and collaborators to explore analytical results without needing to run code themselves, which required developing a working understanding of reactive programming logic that is unique to R and has no direct equivalent in SPSS or Excel. Students I tutor in R range from those writing their first script to those preparing code for peer-reviewed publication, and I focus sessions on helping them write R code that is not just functional but clean, well-commented, and structured in a way that holds up under scrutiny from advisors, collaborators, and reviewers.
SPSS

SPSS

My hands-on experience with SPSS developed specifically through behavioral and social science research contexts, where the software is the industry standard for managing survey-based datasets, handling missing data through multiple imputation, and preparing codebooks that document variable definitions, coding schemes, and recoding decisions in a way that satisfies institutional review and dissertation committee expectations. I have worked extensively with SPSS's syntax editor to write and save command scripts that automate repetitive data cleaning and recoding tasks, which is a workflow skill that most students never learn in coursework but that becomes essential when managing a large primary dataset collected for a thesis or dissertation study. Beyond the technical side, I have developed particular depth in helping students navigate the output interpretation and APA reporting requirements that SPSS-based research demands, including correctly extracting and reporting test statistics, degrees of freedom, p-values, and effect sizes from procedures like MANOVA, hierarchical regression, and discriminant analysis in a format that meets publication and committee standards. Students I work with in SPSS tend to arrive knowing how to click through a procedure but struggling to connect the output back to their research question in writing, and closing that specific gap between running an analysis and defending its results is where I focus most of my attention in sessions.
Statistics

Statistics

Statistics is the discipline in which I hold my highest academic credential, and the depth of that training included advanced study in areas such as stochastic processes, multivariate distribution theory, computational statistics, and experimental design at a level that required not only applying established methods but understanding their mathematical derivations and limitations well enough to evaluate when and why they fail on real data. I also taught statistics for psychology at the undergraduate level as part of my academic experience, which required translating mathematically rigorous concepts into language accessible to students with no calculus background while maintaining the precision that makes statistical reasoning trustworthy and reproducible. That combination of advanced theoretical training and experience teaching statistics across very different audience types, from mathematically sophisticated graduate students to undergraduates encountering the subject for the first time, has given me an unusually wide range as a statistics tutor. I am equally comfortable working through the measure-theoretic foundations of probability with a doctoral student or explaining what a p-value means intuitively to someone in their first statistics course, and I have found that the ability to move fluidly between those levels is what makes the biggest practical difference when a student is stuck and needs the concept explained from a completely different angle than the one their instructor used.
Tableau

Tableau

As a doctoral-level statistician and financial analyst with over eight years of professional and academic experience, I bring a rigorous analytical foundation to every Tableau engagement that goes well beyond visualization mechanics. I have built interactive dashboards and data visualizations for state-level and federal research initiatives, financial analysis projects, and academic research presentations, translating complex quantitative findings into clear, decision-ready formats for both technical and non-technical audiences. My statistical background ensures that every visualization I build and teach is analytically grounded, with the right chart type, scale, and data structure chosen to communicate findings accurately rather than superficially. I can help students and professionals at any skill level build impactful, interactive Tableau dashboards from raw data, covering everything from data source connections and calculated fields through filter logic, parameter controls, and professional dashboard design.
Trigonometry

Trigonometry

Trigonometry sits at the mathematical core of probability distributions, Fourier analysis, and signal processing methods that I worked with extensively during my doctoral training in statistics, which means my familiarity with the subject goes well beyond the standard high school curriculum into how trigonometric functions behave analytically and why they appear so frequently in advanced quantitative work across science and engineering disciplines. The applied statistical models I worked with in my graduate research, particularly those involving circular data, periodic phenomena, and wave-based signal decomposition, required a deep and flexible command of trigonometric identities, inverse functions, and the geometric relationships encoded in the unit circle that most students only encounter at a surface level in their coursework. On the tutoring side, I have worked with students navigating trigonometry as a prerequisite course for calculus and physics, covering topics including radian measure, the graphs and transformations of sine, cosine, and tangent functions, sum and difference identities, double angle formulas, and the application of the law of sines and cosines to non-right triangle problems. What I find most effective with trigonometry students is establishing a rigorous geometric intuition for why the functions are defined the way they are before introducing algebraic manipulation, because students who understand the geometry can reconstruct identities and solve unfamiliar problems rather than relying on memorization that tends to break down under exam pressure.
Video Production

Video Production

My video production experience is built primarily around the Adobe suite, where I have worked extensively in Premiere Pro on multi-track timeline editing, color grading, transition design, and motion title creation for educational and client-facing video projects that required both technical precision and visual clarity. I have also worked in Photoshop and Illustrator to create thumbnails, visual assets, and graphic design elements that were incorporated directly into video projects, which gave me a workflow that moves fluidly between design and editing depending on what the project requires. On the instructional side, I have walked students and clients through video editing and graphic design workflows in Adobe tools, covering everything from project setup and asset organization through export settings and format optimization for different delivery platforms. My tutoring approach with creative software follows the same principle I use with analytical tools, focusing on building a genuine understanding of the workflow logic rather than memorizing button locations, because students who understand why the software is organized the way it is can troubleshoot problems and adapt their skills to new projects far more independently than those who learned by copying steps without context.
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Response time: 2 hours
Hourly Rate: $50
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