Tag
23 articles tagged #Data Science.
P.01The direct answer: calculate your required sample size before you start, based on your baseline conversion rate and the smallest effect worth detecting, then run until you hit it. Stopping early on a good-looking p-value is how most A/B test results turn out to be noise.
P.02Data scientist and ML engineer are two different jobs. This guide explains the distinction, what a strong data scientist actually does, and what to screen for in the interview to find someone with real analytical judgment.
P.03Data engineers build the pipelines, warehouses, and transformation layers that make data useful. They are not data scientists, not backend developers, and not analysts. Here is how to hire for the actual role.
P.04DuckDB runs OLAP queries directly in your process, on files on your laptop, without a server. Here's how to use it and when it beats spinning up BigQuery or Redshift.
P.05Polars has hit stable 1.x and is showing up in production data pipelines everywhere. Here's an honest comparison with pandas, where Polars wins, where it doesn't, and a migration walkthrough.
P.06Explore dimensionality reduction techniques — PCA, LDA, t-SNE, and autoencoders — for improving model performance and data visualization.
P.07Discover the key advantages of Random Forest algorithms — high accuracy, resistance to overfitting, feature importance, and handling missing data.
P.08Learn how centroid-based clustering algorithms like K-means partition datasets into meaningful groups based on distance metrics.
P.09A guide to clustering algorithm types — partition-based, hierarchical, density-based, and model-based — with use cases and selection criteria.
P.10Explore time series forecasting methods including ARIMA, exponential smoothing, and seasonal decomposition for real-world prediction tasks.
P.11Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
P.12Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.13Understand unsupervised learning methods including clustering, dimensionality reduction, anomaly detection, and generative models with practical examples.
P.14Explore derivatives, integrals, multivariate calculus, optimization, and differential equations with Python implementations using SymPy and NumPy.
P.15Learn essential statistics concepts — mean, median, mode, variance, standard deviation, percentiles, quartiles, and z-scores with Python implementations.
P.16Understand vectors, matrices, transpose, inverse, determinant, trace, dot product, and eigenvalues with NumPy implementations for data science.
P.17Learn to create compelling data visualizations using Matplotlib and Seaborn — line plots, scatter plots, bar charts, histograms, heatmaps, and more.
P.18Master Pandas for data manipulation — reading data, selecting columns, grouping, merging DataFrames, handling missing values, and working with dates.
P.19Learn NumPy essentials — arrays, shapes, reshaping, slicing, stacking, broadcasting, universal functions, and image processing with practical examples.
P.20EditorPickA structured 100-day data science bootcamp roadmap covering Python, statistics, machine learning, deep learning, and real-world projects.
P.21EditorPickA comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.
P.22Understand the key differences between artificial intelligence, machine learning, and deep learning with clear definitions, examples, and real-world applications.
P.23EditorPickA comprehensive guide to EDA covering visualization techniques, summary statistics, correlation analysis, data cleaning, PCA, anomaly detection, and feature engineering.