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📝 Medium articles

Grouped by topic below. All posts are also available on Medium.

Statistics & Hypothesis Testing

  1. 5 Most Common Statistical Distributions Normal distribution and other common statistical concepts explained clearly. Published on Jul 6, 2025 · 7 min read

  2. Understanding Odds Ratio, Relative risk and Hit Ratio An odds ratio (OR) is a statistical measure of association between two events, for example, exposure and outcome. OR plays a crucial role in…

  3. Simpson’s Paradox Be aware of this common mistake in data analysis. Published on Jan 7, 2025 · 3 min read

  4. False Discovery Rate (FDR), adjusted p-value, and Q-values When you perform many hypothesis tests simultaneously, the chance of observing a false positive (rejecting a true null hypothesis)… Published on Jan 5, 2025 · 3 min read

  5. Understand p-value and confidence interval Understand p-value and confidence interval in an example. Published on Jan 4, 2025 · 7 min read

  6. Comprehensive Guide to Multiple Testing Corrections Bonferroni, Holm-Bonferroni, FDR, Tukey HSD, and Permutation Tests Explained with Python Examples.

  7. A Comprehensive Guide to A/B Testing in Python Implementing A/B Testing in an Email Campaign Using Python. Published on Aug 5, 2024 · 6 min read

  8. Compute correlation coefficient between variables in Python Guide to correlation measures. Published on Nov 4, 2024 · 9 min read

  9. Residuals in Linear and Mixed-Effects Models A hands-on tutorial on residuals in Python – what they tell you about nonlinearity, heteroskedasticity, and outliers, and how their meaning shifts in mixed-effects models. Published Dec 3, 2025 · Learning Data

  10. Understanding Indirect Effects with Mediation Analysis How mediation analysis decomposes an effect into direct and indirect paths through an intermediate variable. DataDrivenInvestor

Machine Learning

  1. Robust Regression (Against Outliers) Analysis in Python Learn how to use robust estimators like Huber Regressor to handle noisy data and outliers more effectively than standard linear regression.

  2. Understand Decision Function in a Classification Model A beginner’s guide to decision functions using scikit-learn. Published on Jul 5, 2025 · 5 min read

  3. A Brief Introduction to Automated Machine Learning (AutoML) with an Example Quick AutoML demo with a working implementation. Published on Jul 5, 2025 · 4 min read

  4. Regularized or Penalized Regression Models Lasso, Ridge, and ElasticNet explained with Python examples. Published on Jul 4, 2025 · 9 min read

  5. Dimensionality Reduction using R Understanding dimensionality reduction technique with an example dataset. Published on Aug 7, 2024 · 7 min read · In Towards Dev

  6. R-squared and MSE are not the only performance metrics for a regression model Evaluate regression models with Python and Scikit-Learn. Published on Sep 25, 2024 · 7 min read

  7. Understanding Nagelkerke R² (Pseudo R²) Explained with an example in Python – how Nagelkerke’s adaptation of Cox & Snell R² gives a normalized goodness-of-fit measure for logistic regression. Published Mar 2026 · DataDrivenInvestor

  8. Regression analysis in R Guide to regression analysis with R. Published on Sep 13, 2024 · 11 min read

  9. Most Important Hyperparameters for Popular ML Algorithms Tuning your models right. Published on Oct 23, 2024 · 9 min read

  10. Not sure which ML algorithm to implement? Try SuperLearner Aggregated prediction model in Python. Published on Oct 20, 2024 · 4 min read

  11. Implement Bayesian optimization for hyperparameter tuning in Python Tune hyperparameters efficiently. Published on Nov 1, 2024 · 7 min read

  12. How to Make ML Models Explainable Using SHAP Shapley values for model interpretation. Published on Oct 31, 2024 · 11 min read

  13. Understand Convolution Neural Network (CNN) Conceptual explanation of CNN. Published on Oct 30, 2024 · 10 min read

  14. Market Basket Analysis with Python A hands-on tutorial explaining association rule mining using the Apriori algorithm to find itemsets that frequently occur together in transactional data.

  15. Exploring TabPFN: Efficient Machine Learning Model for Small Tabular Datasets A pretrained transformer-based classifier for tabular data that performs Bayesian inference from a learned prior – little to no hyperparameter tuning required. Published Sep 22, 2025

  16. Data leakage: The hidden risk in your predictive model Why a model can look great in validation and fall apart in production – and the common ways information leaks from test to training. Published Oct 28, 2025 · Learning Data

  17. Build an end-to-end Machine Learning project in Python Machine learning has become an integral part of modern software solutions, enabling intelligent features and data-driven decision-making. Published on Jul 31, 2024 · 5 min read · In Towards Dev

  18. Build up an end-to-end predictive model with ML algorithm in Python Prediction of employee attrition. Published on Oct 7, 2024 · 11 min read

  19. An end-to-end machine learning model with FastAPI and Docker Training, evaluating, and serving a model as an API – reproducible runs, saved preprocessing pipelines, input validation, and containerization with Docker. Published Nov 12, 2025

Biostatistics

  1. An Introduction to the Mixed Effect Model in R Beginner’s guide to building mixed effect models using R. Published on Jul 4, 2025 · 6 min read

  2. Measuring Personalized Cancer Therapy Response: The Growth Modulation Index (GMI) Compute GMI using Python for personalized cancer therapy response. Published on Apr 30, 2025 · 4 min read

  3. Survival Analysis: How Long Until Something Happens? An introduction to time-to-event data, censoring, and the core ideas behind survival analysis.

Genetics & Genomics

  1. SNPs Explained in simple terms The small DNA changes that shape our differences – what a single nucleotide polymorphism actually is, in plain language. Published Mar 28, 2026 · 6 min read

  2. Linkage Disequilibrium Explained in Simple Words How genetic variants tend to be inherited together, and why that matters for association studies, polygenic risk scores, and mapping disease genes. Published Mar 7, 2026

  3. Heritability in simple terms How much of the variation in a trait – height, disease risk, anything – comes from genetic differences versus environment. Published Mar 7, 2026 · 3 min read

  4. Polygenicity in simple terms Why most traits are influenced by many genetic variants, each with a small effect, rather than a single dominant gene. Published Apr 2, 2026

  5. Polygenic Risk Scores (PRS): Explained in Simple Terms How PRS combines the effects of thousands of genetic variants from a GWAS into a single number estimating genetic predisposition to a trait. Published Mar 2026

  6. Mendelian Randomization in simple terms Genetics to understand cause and effect – using genetic variants as natural experiments to study causal relationships between traits. Published Mar 24, 2026

  7. A Simple Introduction to Common Genetics Data Formats A plain-language walkthrough of the file formats you’ll run into constantly in genetics work – VCF, PLINK’s bed/bim/fam, and others.

  8. How does LDSC Compute SNP Heritability? How LD Score Regression estimates SNP-based heritability from GWAS summary statistics alone, without needing individual-level genotype data. Published Mar 31, 2026

  9. How to compute Genetic Correlation with LDSC Extending LDSC from a single trait to two – whether traits like depression and anxiety share the same underlying genetic basis. Published Mar 31, 2026

  10. A Practical Guide to Post-GWAS Analysis The most important steps after a GWAS produces its list of SNPs and p-values, with practical intuition and minimal working code. Published Apr 29, 2026

  11. Bayesian Statistics in SBayesR: A Tutorial for Polygenic Score Estimation A hands-on walkthrough of Bayesian polygenic score estimation using SBayesR.

  12. An Introductory GWAS Workflow: From Simulated Data to PRS on macOS A hands-on pipeline for running a GWAS with PLINK 1.9, PLINK 2.0, and Python – starting entirely from a simulated dataset, no cluster or biobank-scale data required. Published Nov 20, 2025

Bioinformatics Tooling & Workflows

  1. Introduction to grep and sed for Command-Line Text Processing The basic ideas behind both tools, then commonly used commands with examples – grep to find, sed to modify or clean. Published Apr 24, 2026

  2. Getting Started with awk: A Hands-On Tutorial Filtering rows, extracting columns, and transforming structured text files directly from the terminal. Published Apr 23, 2026 · 10 min read

  3. Basic R Coding Cheat Sheet for Statistical Geneticists and Bioinformaticians Practical R patterns for working with PLINK, LDSC, GCTA, PRSice, and SBayesR outputs – cleaning phenotypes, merging covariates, and more.

  4. Building Reproducible Pipelines with Snakemake: A Beginner’s Guide Learn how to automate, organize, and scale your data workflows using a simple rule-based system. Published Apr 25, 2026

  5. Getting Started with HPC and SLURM: A Practical Guide for Beginners Login nodes, compute nodes, and job scheduling – the basics of working on a shared HPC system for the first time. Published Apr 27, 2026 · 5 min read

  6. From FASTQ to PLINK: An End-to-End NGS Pipeline on Apple Silicon A full walkthrough from raw sequencing reads to PLINK-ready genotype files, running natively on Apple Silicon.

  7. 20 Essential Terminal Commands for Data Scientists Working with Genomics Data The Linux commands that make cleaning genotype data, running HPC pipelines, and checking GWAS results faster and less painful. Published Oct 14, 2025

AI & LLMs

  1. How Does ChatGPT Actually Work? A beginner’s visual guide to tokens, next-word prediction, and what’s actually happening when you chat with an LLM. Published Oct 9, 2025

  2. Running Powerful AI Models on Your Mac: My Journey into Local LLMs A practical guide to running Llama, Mistral, Phi, and other transformer-based models locally on a Mac – no cloud or API key required. Published Oct 9, 2025

NLP

  1. Topic modeling with Python: An NLP project Explore your text data with Python. Published on Aug 23, 2024 · 15 min read · In Python in Plain English

  2. Build a machine learning model for sentiment analysis in Python An NLP project on IMDb movie reviews. Published on Oct 17, 2024 · 8 min read

Time Series & Finance

  1. Analysing stock data in Python A step-by-step guide to understanding time series and applying forecasting methods on Yahoo finance dataset. Published on Sep 5, 2024 · 15 min read · In Towards Dev

  2. Visualize stock data in Python Key parameters for stock data visualization. Published on Sep 28, 2024 · 11 min read

  3. Timeseries prediction with Liquid State Machine Model in Python Liquid State Machine for prediction/classification tasks. Published on Oct 30, 2024 · 5 min read

Data Engineering & Python Tools

  1. Managing Relational Data in Python with SQLite and Pandas A tutorial on integrating pandas with SQLite to query, manage, and analyze structured relational data within Python workflows.

  2. Scalable Data Processing with Dask Try Dask for large datasets: a flexible Python library for parallel and distributed computing, enabling scalable analysis with pandas-like syntax.

  3. Handling MultiIndex data in Python Multi-indexing with Yahoo Finance dataset. Published on Sep 10, 2024 · 6 min read

  4. 10 Python functions you must know Widely used Python functions. Published on Oct 19, 2024 · 4 min read

Data Visualization

  1. Plotting data in Python A step-by-step guide to exploring data visualization techniques in Python. Published on Aug 9, 2024 · 7 min read · In Python in Plain English

  2. Interactive plotting in Python Visualize data interactively in Python. Published on Sep 17, 2024 · 4 min read

  3. Radar chart – a visualization tool for multivariate data Visualize effects of multiple variables. Published on Dec 6, 2024 · 4 min read

Data Preprocessing

  1. Outlier detection and removal in Python A beginner’s guide to outlier detection in static and time-series dataset. Published on Aug 15, 2024 · 11 min read · In Learning Data

  2. Data normalization in Python Normalize data using 4 different methods. Published on Sep 24, 2024 · 5 min read

  3. Common preprocessing techniques in Python Data preprocessing techniques. Published on Oct 23, 2024 · 6 min read

  4. SMOTE-handle imbalance in Python Generating synthetic minority-class samples to balance a dataset before training a classifier – how it works, and where it falls short. Published Jan 1, 2025

  5. Does Normalization Change Kurtosis or Skewness? A close look at what normalization actually does – and doesn’t do – to the shape of a distribution.

  6. Transforming Skewed Data with Log Transformation and Rank-Based Inverse Normal Transformation (RINT) methods Two ways to reshape a right-skewed distribution so it aligns better with the assumptions of statistical methods. Published May 8, 2026 · Learning Data

Career & Reflections

  1. How I Transitioned from Physics to Data Science: A Journey of Learning and Discovery From a PhD in computational physics to statistical genetics – the path, the late nights of self-study, and what actually made the shift possible. Published Jul 9, 2024 · 3 min read