Stanford Statistician | Pro R, SPSS, Power BI, Tableau & Excel

Anthony J.

Biostatistics

Biostatistics as a discipline requires a specific set of methodological tools designed around the structure of health and life sciences data, and my experience working in this space includes applying survival analysis techniques such as Kaplan-Meier estimation and Cox proportional hazards regression to time-to-event outcomes, modeling disease incidence and prevalence using Poisson and negative binomial regression for count data, and working through the sample size and statistical power calculations that are required before any clinical or epidemiological study can be ethically and scientifically justified. I have also worked with the specific study design frameworks that biostatistics governs, including randomized controlled trials, observational cohort and case-control designs, and crossover studies, each of which introduces distinct analytical challenges around confounding, selection bias, and the appropriate choice of comparison group that require biostatistical reasoning rather than general statistical intuition. My tutoring experience in biostatistics includes working with students in nursing, public health, epidemiology, and health professions research programs on coursework that emphasizes critical appraisal of clinical literature alongside hands-on data analysis, including interpreting number needed to treat, absolute and relative risk reduction, diagnostic test sensitivity and specificity, and receiver operating characteristic curves that appear frequently in clinical research reporting but that are rarely covered in general statistics courses. That combination of methodological depth and familiarity with the health sciences research context allows me to ground biostatistics sessions in the kinds of applied problems and published literature examples that make the methods feel immediately relevant to students whose careers will involve producing or consuming clinical and population health evidence.

Business

My graduate training in business analytics covered the quantitative and analytical frameworks that form the backbone of modern business decision-making, including optimization modeling using linear and integer programming, Monte Carlo simulation for risk and uncertainty analysis, decision tree analysis for sequential decision problems under uncertainty, and queueing theory for modeling operational bottlenecks in service and manufacturing environments, all of which are topics that appear regularly in MBA and business analytics core curricula but that require a level of mathematical fluency that many business students find challenging to develop without focused support. I have tutored students through business courses covering supply chain analytics, operations management, corporate strategy, and managerial economics, helping them work through quantitative case assignments that require building and interpreting analytical models rather than simply applying conceptual frameworks, which is where many business students struggle most when their programs shift from qualitative to quantitative problem-solving. My consulting background adds a practical dimension to this tutoring experience, having worked directly with organizations on business problems that required translating messy operational data into structured analytical models and communicating findings in the executive-facing language that business contexts demand, which gives me insight into how the methods taught in business courses actually get applied and adapted when real constraints and imperfect data enter the picture.

Data Analysis

My MS in Advanced Data Analytics was structured specifically around the full data analysis lifecycle, covering data acquisition strategies, database querying using SQL, exploratory data analysis, predictive modeling using supervised and unsupervised machine learning methods, text analytics for extracting patterns from unstructured data, and visual analytics for communicating findings to decision-making audiences, which gave me a formal graduate-level grounding in data analysis as a discipline distinct from statistics or software training alone. That program required working through applied capstone projects using real organizational datasets where the deliverable was a complete, stakeholder-ready analysis rather than a course assignment, which developed the kind of end-to-end analytical judgment that classroom instruction alone rarely produces. My consulting work has reinforced and extended that training across industry contexts where data analysis problems rarely arrive pre-cleaned or pre-structured, requiring substantial work in data profiling, anomaly detection, outlier handling, and feature engineering before any modeling or visualization work can begin meaningfully. I have tutored students through data analysis coursework and certification programs including the Google Data Analytics certificate, supporting them across the full analytical workflow from SQL querying and spreadsheet analysis through data visualization and capstone project completion, with a focus on building the kind of transferable analytical thinking that holds up across different tools and datasets rather than becoming dependent on any single platform or procedure.

General Computer

My work across data analytics, consulting, and academic research has required operating fluently across a wide range of computing environments and software ecosystems, including Windows and Mac operating systems, cloud-based platforms such as Google Workspace and Microsoft 365, file management and organization systems, and the kind of foundational computing literacy that underpins everything from basic document creation to more advanced analytical work, giving me a practical understanding of where general computer users most commonly run into confusion and how to address it efficiently. I have helped students and clients who were returning to computing after extended breaks, transitioning from one operating system to another, or simply trying to build enough digital confidence to function effectively in a workplace or academic setting that assumed a level of computer fluency they had not yet developed. On the instructional side, I have supported learners at the most foundational level, covering topics such as file and folder organization, cloud storage and syncing, keyboard shortcuts, printer and peripheral setup, browser navigation, email management, video conferencing tools, and the general troubleshooting mindset that allows someone to work through unfamiliar software independently rather than freezing every time something unexpected happens. My approach with general computing students is always to build transferable digital intuition rather than memorizing steps for specific tasks, because the computing landscape changes quickly and students who understand the underlying logic of how software and operating systems are organized can adapt to new tools far more confidently than those who learned by rote.

GIS

My GIS experience spans both ArcGIS Pro and QGIS environments, where I have worked with spatial analysis tools including buffer analysis, spatial joins, hotspot analysis, and choropleth mapping applied to demographic, health, environmental, and transportation datasets, giving me a broad practical familiarity with the kinds of spatial problems that come up across research, consulting, and public sector analytical contexts. I have worked with census and administrative geographic data to build maps and spatial summaries that communicate population-level patterns to non-technical audiences, which required developing a working understanding of coordinate reference systems, projection choices, and how those decisions affect the accuracy and visual interpretation of spatial outputs. On the applied side, I have integrated GIS workflows with R using packages such as sf and ggmap to build reproducible geospatial analysis pipelines that combine statistical modeling with spatial visualization, which extends the reach of GIS work beyond standalone mapping software into fully scripted analytical environments where spatial and non-spatial data can be joined, modeled, and visualized together. I have tutored students and clients through GIS workflows covering data import and projection management, attribute table operations, spatial queries, and cartographic design principles for producing publication and presentation ready maps that meet the standards of academic and professional reporting.

Macroeconomics

My graduate training in quantitative methods included substantial engagement with macroeconomic theory and modeling, covering the mathematical foundations of national income accounting, aggregate demand and supply dynamics, IS-LM and AD-AS frameworks, and the Solow growth model for analyzing long-run economic growth through capital accumulation, technological progress, and labor force dynamics, all of which require a level of algebraic and graphical fluency that distinguishes intermediate macroeconomics from the intuitive reasoning that introductory courses emphasize. My exposure to macroeconomic policy analysis extended into open economy macroeconomics including the Mundell-Fleming model for analyzing fiscal and monetary policy effectiveness under fixed and floating exchange rate regimes, balance of payments accounting, and the mechanisms through which interest rate differentials drive international capital flows and currency valuation. I have tutored students through macroeconomics coursework at the introductory and intermediate levels, helping them work through the conceptual and mathematical dimensions of topics including central bank policy transmission mechanisms, the quantity theory of money and inflation, Phillips curve tradeoffs between unemployment and inflation, and the competing theoretical perspectives of Keynesian, monetarist, and new classical economics that frame most policy debates students encounter in their coursework. A pattern I see consistently with macroeconomics students is difficulty connecting theoretical models to real-world economic events, and I focus sessions on building that bridge explicitly by grounding abstract frameworks in current and historical policy episodes that make the models feel relevant and interpretable rather than purely academic.

MBA

MBA programs are distinctive in how they combine functional business disciplines with a case-based pedagogical approach that requires students to synthesize financial, operational, and strategic information simultaneously under time pressure, and my tutoring experience with MBA students has developed specifically around that format, including working through Harvard Business School-style case analyses where the task is building a defensible recommendation from incomplete and sometimes contradictory quantitative and qualitative evidence. My coverage of MBA core curriculum includes corporate finance concepts such as discounted cash flow valuation, net present value, internal rate of return, and capital structure theory, managerial accounting including cost-volume-profit analysis and variance analysis, and organizational behavior frameworks covering leadership, team dynamics, and negotiation strategy that form the qualitative backbone of most MBA programs alongside the quantitative coursework. I have also supported MBA students with the written deliverables that programs weight heavily but that receive less tutoring attention than quantitative coursework, including consulting-style analytical reports, strategic case write-ups, and MBA admissions essays where the writing must balance professional accomplishment with authentic personal narrative within tight word limits. That breadth across the quantitative, strategic, and written dimensions of MBA work reflects the reality that MBA students rarely struggle with just one thing in isolation, and being able to support all three within the same tutoring relationship is something I find makes a meaningful difference in how quickly students find their footing in a demanding program.

Microeconomics

Microeconomics formed a core part of my quantitative graduate training, where the mathematical treatment of consumer theory, producer theory, and market equilibrium required facility with optimization techniques including Lagrangian methods for constrained utility and profit maximization, duality theory connecting cost and production functions, and general equilibrium analysis that extends partial equilibrium reasoning to interactions across multiple interconnected markets simultaneously. My exposure to microeconomic theory at the graduate level also included game theory covering Nash equilibrium, sequential games, mechanism design, and information asymmetry models such as adverse selection and moral hazard that underpin a significant portion of modern applied microeconomics in industrial organization, labor economics, and public policy analysis. I have tutored students through microeconomics coursework at both the introductory and intermediate levels, helping them navigate the transition from intuitive supply and demand reasoning to the more rigorous mathematical framework that intermediate microeconomics requires, including working through indifference curve analysis, isoquant and isocost diagrams, cost curve derivations, and the welfare analysis of market interventions such as price controls, taxes, and subsidies. A consistent challenge I see with microeconomics students is bridging the gap between graphical and algebraic representations of the same economic relationships, and developing fluency in moving between those two forms of reasoning is something I focus on deliberately because it is the skill that tends to determine how well students perform across the full range of problems an intermediate microeconomics course presents.

Microsoft Excel

My formal academic training included graduate-level coursework in spreadsheet modeling specifically designed around Excel as an analytical and decision-support environment, covering financial functions including NPV, IRR, and PMT for capital budgeting problems, What-If Analysis tools including Goal Seek, Scenario Manager, and two-variable Data Tables for sensitivity analysis, and linear programming models built directly in Excel using Solver for resource allocation and optimization problems that are central to business analytics and operations management curricula. I have tutored students through Excel-intensive graduate analytics courses, including DESC 607: Analytics for Business Decisions, where assignments required building functional decision models from scratch rather than simply populating existing templates, and where the grading criteria emphasized both analytical correctness and workbook structure, documentation, and reproducibility. That course-specific experience gives me a precise understanding of the Excel competencies that graduate business and analytics programs actually assess, which allows me to focus tutoring sessions on exactly the skills that show up in assignments and exams rather than covering general Excel features that may not be relevant to what a student is being evaluated on.

Psychology

My direct teaching experience at the undergraduate level was specifically in statistics for psychology, which required developing a deep familiarity with how psychological research is designed, conducted, and reported, including the ethical principles governing human subjects research, the logic of experimental and quasi-experimental designs, and the measurement frameworks underlying psychometric instruments used to assess cognitive, behavioral, and emotional constructs in ways that meet standards of reliability and validity that psychology as a discipline takes particularly seriously. That teaching context required engaging with primary psychological literature across multiple subfields, including cognitive, developmental, social, and clinical psychology, well beyond what a purely quantitative background would require. I have tutored psychology students through coursework covering the full breadth of a typical undergraduate and graduate psychology curriculum, including introductory psychology, abnormal psychology and psychopathology using DSM diagnostic frameworks, developmental psychology across the lifespan, social psychology covering attitude formation, conformity, and group behavior, and neuropsychology covering the biological bases of behavior and cognition. What distinguishes my support for psychology students from general academic tutoring is the ability to connect the conceptual and empirical sides of the discipline simultaneously, helping students understand not just what psychological theories claim but how those claims are tested, challenged, and revised through the research process that sits at the core of psychology as a scientific discipline.

R

My use of R in applied consulting and research settings has required working well beyond standard statistical procedures into areas such as text mining and sentiment analysis using the tidytext package, geospatial data visualization using sf and ggmap for mapping and spatial pattern analysis, and building automated reporting pipelines using Quarto that generate publication-ready documents combining narrative, code, and output in a format that updates automatically when the underlying data changes. I have also worked extensively with R's database connectivity tools, including DBI and odbc, to query and pull data directly from SQL databases into R analytical workflows, which is a practical skill that comes up constantly in professional and research environments where data lives in relational databases rather than flat files. On the instructional side, I have taught R specifically in the context of psychology and social science research methods courses, where the curriculum required students to move from raw survey data all the way through cleaning, analysis, and APA-formatted results reporting entirely within R, using packages like psych for reliability and factor analysis and apaTables for generating correctly formatted output tables directly from R objects. That course-specific instructional experience gives me a precise understanding of the R competencies that research methods programs actually assess, which allows me to target sessions around exactly what students need rather than covering general R programming concepts that may not be relevant to their immediate coursework.

SPSS

My SPSS experience includes working specifically within program evaluation and applied social research contexts, where the software was used not only for standard inferential procedures but for more specialized analyses including cluster analysis for identifying subgroup patterns in population data, multidimensional scaling for mapping perceptual and attitudinal relationships among variables, and conjoint analysis for modeling how respondents weight competing attributes in decision-making studies, which are procedures that sit outside the standard introductory SPSS curriculum but that appear regularly in applied research and evaluation work. I have also worked extensively with SPSS's Complex Samples module for analyzing data collected through stratified or clustered sampling designs, where standard significance tests produce incorrect results because they assume simple random sampling, and with the Custom Tables module for generating publication-ready frequency and cross-tabulation summaries directly from SPSS output in formats that match what journals, government agencies, and evaluation reports typically require. On the tutoring side, I have worked with students and researchers designing and analyzing Likert-scale survey instruments in SPSS, covering item analysis, scale construction, internal consistency testing, and the exploratory and confirmatory factor analytic procedures used to validate measurement instruments before they are used in formal research contexts.

Tableau

My Tableau experience includes working with some of the platform's more sophisticated analytical capabilities beyond standard chart building, including table calculations for running totals, period-over-period comparisons, and cohort analysis, fixed and include level of detail expressions for computing aggregations at dimensions that differ from the view level, and set actions combined with parameter-driven calculations to build dashboards that respond dynamically to user selections in ways that go well beyond basic filter interactions. I have built Tableau workbooks connected to live database sources as well as published extract-based dashboards optimized for performance across large datasets, which required understanding how Tableau's data engine processes queries and how workbook design decisions affect load times and interactivity in published environments. On the applied side, I have used Tableau specifically within pipeline reporting and organizational performance tracking contexts, building multi-page dashboard stories that guide a non-technical audience through a sequential analytical narrative rather than presenting all views simultaneously, and designing layouts that meet accessibility and readability standards for stakeholder-facing reporting. In tutoring sessions I cover the full Tableau workflow from data connection and preparation through calculated field logic, visualization design principles, and dashboard publishing, with particular attention to helping students understand why Tableau behaves the way it does when aggregation levels, data source relationships, or filter scope produce unexpected results.

Tax Accounting

As a licensed CPA with experience in public accounting, my tax background spans individual, corporate, partnership, and nonprofit taxation, including preparation and review of federal returns across Form 1040 for individual filers, Form 1120 for corporations, Form 1065 for partnerships, and Form 990 for tax-exempt organizations, giving me a comprehensive technical foundation across the entity types and filing requirements that tax accounting courses and professional practice both address. My professional experience includes tax planning and advisory work alongside compliance, which required applying the Internal Revenue Code and Treasury Regulations to real client situations where the goal was not just accurate reporting but structuring transactions and timing decisions in ways that minimized tax liability within the bounds of the law. On the instructional side, I have tutored students through tax accounting coursework covering federal income taxation, corporate tax, and partnership taxation, helping them work through the conceptual frameworks and computational problems that define these courses, including basis calculations, depreciation methods, entity-level versus shareholder-level taxation, and the treatment of capital gains and losses that students consistently find most challenging. I have also used professional tax software including Drake, ProConnect, and TurboTax in both preparation and instructional contexts, which allows me to support students who need to develop practical software proficiency alongside their conceptual understanding of tax law and accounting principles.

Thesis Writing

Completing a doctoral dissertation is the most sustained and demanding writing project most graduate students will ever undertake, and my experience navigating that process at the doctoral level gives me a firsthand understanding of the structural, rhetorical, and methodological challenges that arise at each stage, from developing a defensible research question and writing a literature review that positions the study within an existing scholarly conversation, through constructing a methodology chapter that justifies every design decision, to writing a results and discussion section that interprets findings honestly within the limits of the study's design. That experience is qualitatively different from general academic writing support because dissertation writing requires not just clear prose but an understanding of how scholarly arguments are built, evaluated, and defended in front of a committee whose job is to find weaknesses in the logic and methodology. I have supported graduate students across psychology, education, business, and social science programs at the thesis and dissertation stage, helping them work through proposal development, chapter organization, literature synthesis, and the revision process that follows committee feedback, which often requires restructuring arguments substantially rather than simply polishing language. A consistent pattern I see with thesis students is that writing blocks almost always trace back to unresolved conceptual or methodological uncertainty rather than to writing ability itself, and identifying and resolving that underlying uncertainty before returning to the draft is the approach I find consistently produces the most meaningful forward progress in the shortest amount of time.