1. Foundations of Data Science
Data science is an interdisciplinary field that combines statistics, programming, and domain expertise to extract meaningful insights from data. A data scientist typically draws on...
Read full chapter →The essential Data Science Essentials cheat sheet: 8 concise chapters you can read in minutes, distilled from the full deck. When you're ready, drill the flashcards or test yourself under exam conditions.
Data science is an interdisciplinary field that combines statistics, programming, and domain expertise to extract meaningful insights from data. A data scientist typically draws on...
Read full chapter →Real datasets arrive messy, and most of a practitioner's time goes into preparing them. Data cleaning is the process of detecting and correcting errors such as missing values, dupl...
Read full chapter →To estimate how a model will perform on unseen data, practitioners split the data into training and testing sets. This simple train-test split protects against overfitting, the fai...
Read full chapter →Linear regression models a continuous target as a linear combination of features, predicting \( \hat{y} = w^\top x + b \). It is interpretable, fast, and a baseline against which m...
Read full chapter →When labels are absent, unsupervised methods extract structure directly from the data. Clustering algorithms group similar points: k-means partitions data into \(k\) clusters by mi...
Read full chapter →Statistics is the language with which claims are tested and uncertainty is communicated. Exploratory analysis generates hypotheses from the data; confirmatory analysis then tests t...
Read full chapter →Python is the dominant language in data science, supported by a layered ecosystem. NumPy provides N-dimensional arrays and fast vectorized operations; pandas offers DataFrames for...
Read full chapter →Several specialized domains deserve their own methods. Natural Language Processing (NLP) analyzes and generates text. Its pipeline begins with tokenization, splitting text into wor...
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