Python for Statistics: Code That Is Statistically Sound
Python is the dominant language in data science, but writing code that runs is not the same as running analysis that is correct. Many Python learners can fit a model in three lines yet are unsure which test is appropriate, whether its assumptions hold or what the output really says.
As a statistics professor tutoring since 2014, I bridge that gap. I help you write clean, idiomatic Python and pair it with rigorous statistical reasoning.
What I Can Help With in Python
Data Handling and Exploration
- Setting up Python environments, Jupyter notebooks and VS Code
- Loading, cleaning and reshaping data with pandas
- Handling missing values, data types and dates
- Exploratory data analysis and summary statistics
- Visualization with matplotlib and seaborn
Statistical Testing
- Hypothesis testing with SciPy (
scipy.stats): t-tests, chi-square, Mann-Whitney, Kruskal-Wallis - Normality and variance checks (Shapiro-Wilk, Levene)
- ANOVA and post-hoc tests (Tukey HSD) with statsmodels and pingouin
- Multiple-comparison corrections (Bonferroni, Holm, FDR)
- A/B test analysis, power and sample size calculations
Modeling
- Linear and logistic regression with
statsmodelsformula API, including full inferential output - Regression diagnostics: residuals, leverage, heteroscedasticity and multicollinearity (VIF)
- Generalized linear models and mixed-effects models
- Time series forecasting (ARIMA, exponential smoothing)
- Introductory scikit-learn: train/test splits, cross-validation and when prediction differs from inference
Statistics vs. Machine Learning: Knowing the Difference
A common gap in data science training is the difference between predicting and
explaining. I help you understand when you need confidence intervals and p-values
(statsmodels) versus predictive accuracy (scikit-learn), and how to communicate each
honestly to stakeholders.
Who I Help
- Data science and analytics students who need the statistics behind the code
- Researchers building reproducible analysis pipelines in Python
- Engineers and developers moving into data roles
- Business teams upgrading from spreadsheets (Python workshops)
How Python Sessions Work
Live, hands-on sessions via Zoom or Google Meet. You code in your own environment while I coach, and you keep the annotated notebook and a session recording.
Build Statistical Confidence in Python
Book a free 15-minute consultation. Tell me your goal, the libraries you are using and your deadline. Share a notebook or error message if you have one, and you will receive a clear plan and quote within 24 hours.
Prefer email or chat? Write to [email protected] or message me on WhatsApp at +1 (425) 999-3993.