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Guided learning journeys that build knowledge step by step.
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A systematic learning path for data analysts and scientists to master exploratory data analysis (EDA) through visualization. It covers univariate, bivariate, and multivariate techniques, pattern and outlier detection, and distribution analysis, using Python or R. The path emphasizes choosing the right plot for the question and interpreting visualizations to drive data understanding.
This learning path guides data professionals through building interactive visualizations using Plotly in Python. Starting with Python basics and data handling, it progresses through Plotly Express, graph objects, and advanced features like animations and subplots, culminating in creating interactive dashboards with Dash.
This learning path guides analysts from foundational data concepts to creating and publishing comprehensive Power BI reports and dashboards. It covers data acquisition, transformation, modeling, DAX calculations, visualization design, and collaboration via Power BI Service.
This learning path guides business professionals from foundational data concepts to advanced Tableau skills, enabling them to create interactive visualizations and dashboards. It covers the Tableau interface, data connections, chart types, calculations, parameters, filters, dashboards, and stories, with a focus on practical application. The path is designed for systematic learning with a clear progression from basics to advanced topics.
This learning path guides R users through the philosophy and practice of creating elegant visualizations using the Grammar of Graphics, primarily through ggplot2. Starting with R basics and data manipulation, it progresses through core concepts like aesthetics, geoms, facets, scales, and themes, culminating in advanced techniques and extensions. The path emphasizes principled visualization design and practical application.
This learning path guides data scientists through creating statistical visualizations with Seaborn, starting from Python and Pandas fundamentals, through core plotting techniques, to advanced customization and interpretation. It emphasizes practical application for exploratory data analysis.
This learning path guides Python users through creating comprehensive visualizations with Matplotlib. Starting with Python basics and core Matplotlib concepts, it progresses through various plot types, customization, styling, and saving figures, ensuring a systematic understanding of data visualization.
This learning path equips data professionals with the knowledge and skills to choose effective chart types based on data characteristics and analytical goals. It covers fundamental data types, a range of common chart types, and a decision framework for selection.
This learning path teaches visualization designers how to apply color thoughtfully in data visualizations. Starting with basic design principles and color theory, it progresses through color models, palette creation, semantic color use, accessibility considerations including color blindness, cultural implications, and practical tools. The path ensures learners can create visualizations that are both effective and inclusive.
This learning path teaches designers and analysts how to apply fundamental design principles to create clear, effective, and accessible data visualizations. It covers color theory, typography, layout, contrast, hierarchy, balance, white space, accessibility, and visual consistency, culminating in a practical project.