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Data Science Guides & Tutorials
Roadmaps, salaries, projects and interview prep for aspiring data scientists — from statistics and machine learning to model deployment.
Data Science articles
Time Series Forecasting: A Beginner-Friendly Introduction
A beginner-friendly introduction to time series forecasting on 36 years of real monthly CO2 measurements: what trend and seasonality are, why simple benchmark methods matter, how Holt-Winters combines both patterns, and how to test forecasts honestly by repeatedly forecasting the next year from past data only.
How Much Math Do You Really Need for Data Science?
The honest answer to how much math data science needs depends on the role: O*NET rates mathematics knowledge far higher for data scientists and statisticians than for BI analysts or data engineers. A worked example on real data shows where linear algebra and calculus appear in the regression you already use.
Best Programming Languages for Data Science in 2026
Which programming languages data science work actually uses and why: SQL and Python as the core pair, R for statistics, Scala and Java around Spark, Julia for numerical work, and JavaScript for interactive charts, with the same analysis written in SQL and Python and a learning order by role.
Data Science Without a Degree: Is It Possible in 2026?
Data science without a degree is possible, but the data says it is rare: only 5% of working data scientists in a large 2025 survey had no degree, against 17% of data analysts. This guide shows what the numbers mean, why the role leans on formal education, and the realistic routes for people without a degree.
How to Read a Machine Learning Research Paper
How to read a machine learning research paper without drowning in it: the three-pass method applied to the Transformer paper, the questions to ask of every results table, the difference between what a paper shows and what it suspects, and a small NumPy re-implementation that checks one of its claims.
Computer Vision Basics: From Pixels to Predictions
Computer vision from the ground up: what an image is to a computer, how a convolution filter finds edges, a small convolutional network trained on handwritten digits against a pixel baseline, the main vision tasks from classification to segmentation, and why transfer learning is the default.