Overview

Raw data alone rarely tells the full story. This Specialization teaches you to select the right chart for any dataset, build decision-ready dashboards in Excel, and create interactive, publication-quality visualizations in Python with Plotly. Across three courses, you'll classify data types and apply histograms, box plots, scatter plots, and multi-dimensional charts to real analytical questions; build pivot tables, formatted charts, and a multi-panel Excel dashboard ready for PowerPoint; and produce interactive Plotly visualizations including maps, sunburst charts, and Sankey diagrams, then deploy them in a live Dash web application. By the end, you'll match any dataset to the right technique and present your findings clearly to any audience.

Syllabus

Course 1: Basics of Data Visualization Analysis
Course 2: Visualizing Data in Excel
Course 3: Data Visualizations with Plotly

Courses

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This course gives you a complete, practical toolkit for visualizing and analyzing data across more than 20 chart types. You'll identify which graph suits any dataset, configure it correctly, and present findings clearly to both specialist and non-specialist audiences. This course covers the full visualization landscape: core data types and chart elements, distributional techniques including histograms, density plots, strip plots, box plots, and violin plots, categorical methods including bar graphs, pie charts, and radar plots, relationship plots including scatter plots, lines of best fit, and line plots, and multi-dimensional graphics including bubble plots, matrix scatter plots, and contour plots. Whether you're new to data visualization or looking to sharpen your analytical eye, you'll finish with a structured framework for choosing the right chart every time. No prior visualization experience is required. If you've ever looked at a dataset and wondered how to make it communicate, this course gives you the tools to do exactly that.
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Data science projects live or die on communication. Without clear, compelling visuals, even the most rigorous analysis gets ignored, misread, or shelved. Plotly gives Python practitioners a direct path from raw numbers to interactive, publication-quality charts that stakeholders can interpret and act on, all without leaving Python. You'll build a full command of Plotly's visualization library: from basic line, bar, and scatter charts to statistical distributions, 3D plots, geographic maps, sunburst diagrams, and Sankey flows. Along the way, you'll configure figures using both Plotly Express and Graph Objects, add interactive controls like sliders and dropdown menus, combine charts into subplots, and deploy a Dash dashboard that brings all your visuals together in one shareable application. By the end of this course, you'll produce a full suite of interactive, presentation-ready data visualizations in Python that communicate your findings with clarity and confidence to any stakeholder audience.
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Raw data buried in rows and columns forces your audience to do mental work before they can act. When numbers sit in plain cells with no visual emphasis, hierarchy, or context, the most important insights get lost in the noise. In this course, you'll move from raw data to polished, decision-ready visuals using only Microsoft Excel. You'll format numbers for immediate impact, build and customize pivot tables and charts, work with text boxes, icons, and images as visual objects, assemble a multi-chart dashboard, set up end-user filtering, and export the finished result to PowerPoint. By the end of this course, you'll be able to produce professional data visualizations in Excel that reduce the mental effort your audience needs to find the insight, draw their attention to what matters most, and present your data in any format from a live spreadsheet to a board-ready PowerPoint slide.