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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
7812 Paths · page 382 / 782
This path equips data scientists with systematic techniques for creating, transforming, selecting, and reducing features to improve model performance. It covers encoding, scaling, feature importance, and dimensionality reduction, grounded in Python and machine learning fundamentals.
A systematic path for data scientists to master core unsupervised learning techniques: clustering (K-means, hierarchical, DBSCAN), dimensionality reduction (PCA, t-SNE, UMAP), anomaly detection, and association rules. Build from foundational Python and data preprocessing skills through theory, implementation, and evaluation.
A structured learning path for data scientists to master core supervised machine learning algorithms using Python and scikit-learn. It covers essential prerequisites, model training, evaluation, and hyperparameter tuning, with a focus on KNN, decision trees, random forests, SVM, and naive Bayes.
A systematic path covering core regression techniques, from simple linear regression to regularization, emphasizing model evaluation for prediction and inference. Designed for data science students with foundational knowledge in statistics and programming.
A structured learning path covering essential methods for drawing conclusions from data, including sampling distributions, confidence intervals, hypothesis testing, t-tests, ANOVA, chi-square tests, power analysis, and A/B testing. Designed for data scientists with basic probability and statistics knowledge.
This learning path teaches data science practitioners how to explore, visualize, and summarize datasets using Python, Pandas, and Matplotlib. It covers data profiling, summary statistics, correlation analysis, distribution visualization, pair plots, heatmaps, and structured EDA frameworks, progressing from foundational data handling to advanced visualization and analysis techniques.
This learning path equips data analysts and scientists with systematic techniques to clean, transform, and prepare messy real-world data for analysis. Starting from Python/R basics, it covers handling missing data, outliers, normalization, string manipulation, date/time processing, and data validation, culminating in a capstone project that integrates all skills.
This learning path guides students through the essentials of R for data science, from basic programming concepts to data manipulation with dplyr and tidyr, visualization with ggplot2, reproducible reporting with RMarkdown, and fundamental statistical functions. It is designed for university students who prefer R and want a systematic introduction.
A structured path for beginners to master Python programming fundamentals and essential data analysis libraries. Starting from basic syntax and data structures, the path progresses through functions, NumPy, Pandas, and visualization tools, all within the context of Jupyter notebooks. This path prepares learners to handle real-world data analysis tasks with confidence.
A beginner-friendly path through the essential mathematics for data science: linear algebra, calculus, probability, statistics, and optimization. Builds from high school algebra to the core concepts used in data analysis and machine learning.