10yrs+ Tutor for R, SPSS, Tableau, Power BI, & Excel Data Analysis

Jose R.

Business

My background sits at a useful intersection for business students, combining a strong quantitative foundation with hands-on experience in the tools and techniques that modern business coursework increasingly relies on. I regularly help students with business statistics, data analysis, financial modeling, and decision-making frameworks, using tools like Excel, R, SPSS, and Tableau to connect theoretical concepts to the kind of applied analysis expected in business programs. This includes topics such as regression analysis, forecasting, hypothesis testing, financial ratios, budgeting, and business valuation, depending on a student's specific course and level. I also support students working through core business coursework beyond the quantitative side, including managerial accounting, microeconomics and macroeconomics as they apply to business decision-making, operations management, and introductory finance. For students working on case studies, capstone projects, or business plans, I help structure the analytical components clearly, ensuring that data, assumptions, and conclusions are presented in a way that's both rigorous and persuasive to an academic or professional audience. My teaching approach is grounded in practical application. I prefer working directly from a student's coursework, case materials, or real datasets, since business concepts tend to stick far better when tied to concrete examples rather than taught in the abstract. I work with undergraduate and MBA students alike, including working professionals pursuing an MBA part-time who need to apply what they're learning directly to their current job.

CPA

I bring professional accounting experience and CPA-level expertise to my tutoring, covering the core technical areas tested on the CPA exam, including financial accounting and reporting (FAR), auditing and attestation (AUD), taxation and regulation (REG), and business environment concepts. I help students build a strong foundation in GAAP, financial statement preparation, auditing standards and procedures, and tax law application, with an emphasis on understanding the reasoning behind accounting treatments rather than memorizing rules in isolation. For students preparing for the CPA exam specifically, I work through practice questions and simulations, focusing on exam strategy, time management, and the areas where candidates most commonly lose points. Beyond exam prep, I also support students working through undergraduate and graduate accounting coursework, including financial accounting, managerial accounting, auditing, and taxation courses, as well as practical skills like preparing journal entries, reconciling accounts, and building financial statements. My teaching approach emphasizes connecting technical procedures to the underlying accounting principles they represent, since students who understand the "why" behind a rule tend to retain it far better and apply it more flexibly on both coursework and exam questions. I work with accounting students, career-changers studying for the CPA exam, and professionals looking to strengthen specific technical skills for their current role.

Data Analysis

I hold a PhD and have spent years applying data analysis skills to both academic research and professional consulting projects, giving me a broad view of how data analysis is actually used across different fields and contexts. A significant part of my experience has involved supporting graduate students and doctoral candidates through the data analysis stages of their dissertations and theses, including cleaning and organizing raw data, selecting statistically appropriate tests for their research questions, running those analyses correctly, and then writing up results in the clear, defensible language their committees expect. I have also worked with data collected through a variety of sources, including structured surveys, platforms like MTurk, and institutional datasets, which has given me particular strength in handling messy, real-world data that requires careful cleaning and quality screening before any meaningful analysis can begin. My experience spans the full analytical workflow rather than a single stage of it. This includes exploratory data analysis and visualization to understand a dataset's structure, hypothesis-driven statistical testing to answer specific research questions, and the communication of findings in a way that is accessible to audiences without a technical background. I am comfortable moving between tools depending on the context, including R, SPSS, Tableau, and Excel, and I tailor my approach to whatever software a client's coursework, workplace, or research program requires, rather than pushing clients toward a single preferred tool. In my teaching approach, I prioritize building conceptual understanding before diving into software mechanics. I have found that students who understand why a particular analysis or test is appropriate, and what its output actually means, are far better equipped to troubleshoot problems, adapt to new datasets, and apply their skills independently once our sessions end, rather than simply following steps without real comprehension. I typically start by liste

Marketing

My strength within marketing lies specifically at the intersection of marketing strategy and data analysis, an area increasingly central to how marketing is taught and practiced. I help students work through marketing analytics topics such as customer segmentation, A/B testing, campaign performance analysis, regression-based marketing models, and market research methodology, using tools like Excel, R, SPSS, and Tableau to turn raw data into clear, actionable insights. This includes interpreting survey data, building dashboards to track key performance indicators, and applying statistical reasoning to questions around pricing, consumer behavior, and campaign effectiveness. I also support students working through core marketing coursework, including the marketing mix, branding and positioning, consumer behavior theory, and marketing plan development, particularly where those concepts intersect with data-driven decision-making or require quantitative support for a proposal or case study. For students working on capstone projects, marketing plans, or case competitions, I help ensure the analytical components are rigorous and clearly presented, since a well-supported argument tends to carry far more weight than an idea without data behind it. My teaching approach is practical and grounded in real coursework. I prefer working directly from a student's syllabus, case studies, or marketing data, since connecting theory to concrete analysis tends to make the material click faster and stick longer, particularly for students more comfortable with numbers than with abstract marketing theory alone.

Microsoft Excel

Excel tends to mean something different for nearly every client I work with, which is part of what makes it such a varied subject to tutor. Some come in needing to master core formulas like VLOOKUP, INDEX-MATCH, SUMIF, and COUNTIF to handle everyday calculations more efficiently, while others are building full financial or business models involving regression analysis, Solver-based optimization, and sensitivity or scenario analysis. I've also worked extensively with accounting-adjacent tasks, including setting up general journals and ledgers with proper debit and credit structures, and with reporting-focused work like pivot tables, conditional formatting, and dashboard-style summary sheets that update automatically as new data comes in. Data cleaning and organization is another area where I spend a good deal of time with clients, since a spreadsheet full of inconsistent formatting, duplicate entries, or improperly structured columns can make even simple formulas unreliable. I walk clients through practical techniques for standardizing data, using functions like TEXT, TRIM, and data validation tools, before building out any analysis on top of it, so the foundation is solid. For more advanced projects, I've also guided clients through Monte Carlo simulation setups, what-if analysis, and building Excel models that support real decision-making rather than just static reporting. My teaching approach centers on practical, task-driven learning. Rather than teaching Excel features in isolation, I prefer to work directly from a client's actual spreadsheet, dataset, coursework assignment, or workplace task, since that keeps every skill immediately useful rather than abstract. This has worked particularly well for professionals who are using Excel more heavily in a new role and need to build fluency quickly, as well as for students working through business statistics or finance coursework that requires Excel-based problem sets.

Psychology

Psychology coursework sits at an interesting intersection of conceptual theory and quantitative reasoning, and I bring experience supporting students on both sides of that divide. A substantial part of my work in this subject has centered on psychological statistics specifically, including descriptive statistics, probability, hypothesis testing, t-tests, ANOVA, correlation, and regression, all applied within the context of psychological research design rather than taught as abstract math disconnected from the field. Students in psych stats courses often need help seeing why a particular test fits a particular research question, and building that connection is where I focus much of my attention. Beyond statistics, I also support students working through core psychology content, including research methods, experimental design, and foundational topics across developmental, cognitive, and social psychology, depending on a student's specific coursework. For students conducting their own research, whether for a class project, honors thesis, or graduate program, I help with the full arc of a quantitative study, from choosing an appropriate statistical test for a hypothesis to running the analysis in SPSS or R and interpreting the output in language appropriate for a results section. My teaching approach prioritizes conceptual understanding before procedural steps. I've found that psychology students sometimes approach statistics as a separate, intimidating subject rather than a natural extension of the research questions they're already interested in, so I work to bridge that gap directly, framing statistical concepts in terms of the psychological phenomena they're meant to explain. This tends to reduce statistics anxiety considerably and helps the material stick well beyond a single exam or assignment.

R

Most of the students I work with in R fall into one of two groups: those who have never written a line of code before and find the syntax itself intimidating, and those who understand statistics reasonably well but struggle to translate that understanding into working R code. Both situations are common, and both are very fixable with the right pacing. For complete beginners, I start well before any statistical content, making sure RStudio itself feels familiar, that basic data types and structures make sense, and that simple operations like loading a dataset or running a function don't feel like guesswork. Only once that foundation is solid do we move into the specific coursework or project at hand. A large part of my work in R involves data wrangling, including filtering, reshaping, joining, and cleaning datasets using tools like dplyr and tidyr, which tends to be where many students in applied programs, from economics to public health to political science, actually spend most of their time before any modeling begins. I also regularly help with statistical modeling in R, covering everything from basic hypothesis testing and linear regression to more advanced techniques like generalized linear models, mixed models, and causal inference methods such as matching and instrumental variables, depending on a client's coursework. Debugging is another area where I spend considerable time, since error messages in R can be cryptic for newer users, and learning to read and interpret them is itself a skill worth building deliberately rather than skipping past. I also work with RMarkdown fairly often, particularly for clients completing reports, portfolios, or dissertation chapters that need to combine code, output, and narrative text into a single polished document. In these cases, I help with both the technical formatting of the document and the underlying analysis it presents, making sure the two are consistent and that the final product reads clearly for whoever is grading or reviewing it. Across all of this, my approach

SPSS

SPSS has been one of the primary tools I've relied on throughout my work with graduate researchers, particularly those navigating the analysis chapters of a thesis or dissertation. A client will often come to me holding a dataset they're unsure how to approach, and from there, the real work begins: figuring out which variables matter, what the data types actually allow for, and which test genuinely answers the research question at hand, rather than defaulting to whatever test feels most familiar. Descriptive statistics, t-tests, ANOVA, correlation, regression, and chi-square tests all come up regularly in this kind of work, and I've guided clients through each of them, from initial setup in SPSS through to writing results in a way that satisfies a committee's expectations. Data cleaning tends to take up more time than people expect, and it's an area I spend real effort on. Missing values, miscoded variables, outliers, and inconsistencies in how survey or MTurk-collected data gets entered can all quietly undermine an analysis if they're not caught early, so I walk clients through screening their data properly before any test gets run. Output interpretation is the other piece that trips people up constantly. SPSS generates dense tables, and knowing which numbers actually matter, what a p-value is and isn't telling you, and how to translate that into a sentence a committee member will accept takes practice that goes beyond just running the procedure correctly. Rather than starting with software commands, I tend to start conversations by asking what a client is actually trying to find out. Once that's clear, the SPSS steps start to make sense as a means to an end instead of a disconnected set of menu clicks to memorize. This has proven especially useful for clients who left SPSS behind after an undergraduate stats course and are now returning to it years later for graduate work, since the software often comes back faster than expected once the underlying logic clicks again.

Tableau

I have extensive hands-on experience building and refining dashboards in Tableau across both academic and professional contexts, which has given me a well-rounded understanding of how the tool is used in practice, not just in theory. My experience covers the full dashboard development process, starting with connecting and blending data from multiple sources, cleaning and shaping that data within Tableau Prep or directly in Tableau Desktop, and then building out calculated fields, table calculations, and parameters to support more sophisticated analysis. From there, I work on the interactive elements that turn a static chart into a usable dashboard, including filters, actions, highlight interactions, and dashboard-level navigation, so that end users can explore the data meaningfully rather than just viewing a fixed report. I have experience both building dashboards from scratch to answer specific business or research questions and working within existing dashboards to enhance them, whether that means improving visual clarity, correcting misleading or cluttered chart choices, optimizing performance for larger datasets, or updating a dashboard to reflect new data sources or stakeholder requirements. This dual experience, building new versus enhancing existing work, is particularly relevant for clients starting new jobs or projects that involve inheriting dashboards built by someone else, since that requires a different skill set than greenfield design. In my teaching approach, I place as much emphasis on data visualization design principles as I do on Tableau's technical features. I have found that many students can learn the mechanical steps of building a chart but still struggle to choose the right visualization type, layout, or color scheme for their specific data and audience, so I make sure clients understand the reasoning behind visualization choices, not just the clicks required to execute them. I also emphasize troubleshooting skills, helping clients learn how to diagnose why a calculated field isn't returni