Python Data Visualization: Matplotlib & Seaborn Masterclass
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This is a hands-on, project-based course designed to help you learn two of the most popular Python packages for data visualization and business intelligence: Matplotlib & Seaborn.
We’ll start with a quick introduction to Python data visualization frameworks and best practices, and review essential visuals, common errors, and tips for effective communication and storytelling.
From there we’ll dive into Matplotlib fundamentals, and practice building and customizing line charts, bar charts, pies & donuts, scatterplots, histograms and more. We’ll break down the components of a Matplotlib figure and introduce common chart formatting techniques, then explore advanced customization options like subplots, GridSpec, style sheets and parameters.
Finally we’ll introduce Python’s Seaborn library. We’ll start by building some basic charts, then dive into more advanced visuals like box & violin plots, PairPlots, heat maps, FacetGrids, and more.
Throughout the course you’ll play the role of a Consultant at Maven Consulting Group, a firm that provides strategic advice to companies around the world. You’ll practice applying your skills to a range of real-world projects and case studies, from hotel customer demographics to diamond ratings, coffee prices and automotive sales.
COURSE OUTLINE:
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Intro to Python Data Visualization
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Learn data visualization frameworks and best practices for choosing the right charts, applying effective formatting, and communicating clear, data-driven stories and insights
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Matplotlib Fundamentals
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Explore Python’s Matplotlib library and use it to build and customize several essential chart types, including line charts, bar charts, pie/donut charts, scatterplots and histograms
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PROJECT #1: Analyzing the Global Coffee Market
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Read data into Python from CSV files provided by a major global coffee trader, and use Matplotlib to visualize volume and price data by country
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Advanced Formatting & Customization
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Apply advanced customization techniques in Matplotlib, including multi-chart figures, custom layout and colors, style sheets, gridspec, parameters and more
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PROJECT #2: Visualizing Global Coffee Production
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Continue your analysis of the global coffee market, and leverage advanced data visualization and formatting techniques to build a comprehensive report to communicate key insights
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Data Visualization with Seaborn
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Visualize data with Python’s Seaborn library, and build custom visuals using additional chart types like box plots, violin plots, joint plots, pair plots, heatmaps and more
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PROJECT #3: Analyzing Used Car Sales
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Use Seaborn and Matplotlib to explore, analyze and visualize automotive auction data to help your client identify the best deals on used service vehicles for the business
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Join today and get immediate, lifetime access to the following:
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7.5 hours of high-quality video
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Python Matplotlib & Seaborn PDF ebook (150+ pages)
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Downloadable project files & solutions
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Expert support and Q&A forum
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30-day Udemy satisfaction guarantee
If you’re a data analyst. data scientist, business intelligence professional or data engineer looking to add Matplotlib & Seaborn to your Python data analysis and visualization skill set, this is the course for you!
Happy learning!
-Chris Bruehl (Python Expert & Lead Python Instructor, Maven Analytics)
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Looking for our full business intelligence stack? Search for “Maven Analytics“ to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!
See why our courses are among the TOP-RATED on Udemy:
“Some of the BEST courses I’ve ever taken. I’ve studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I’ve seen!” Russ C.
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14Intro to MatplotlibVídeo Aula
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15Plotting MethodsVídeo Aula
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16Plotting DataFramesVídeo Aula
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17ASSIGNMENT: Plotting DataFramesVídeo Aula
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18SOLUTION: Plotting DataFramesVídeo Aula
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19Anatomy of a Matplotlib FigureVídeo Aula
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20Chart Titles & Font SizesVídeo Aula
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21Chart LegendsVídeo Aula
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22Line StylesVídeo Aula
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23Axis LimitsVídeo Aula
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24Figure SizesVídeo Aula
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25Custom Axis TicksVídeo Aula
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26Vertical LinesVídeo Aula
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27Adding TextVídeo Aula
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28PRO TIP: Text AnnotationsVídeo Aula
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29Removing BordersVídeo Aula
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30ASSIGNMENT: Formatting ChartsVídeo Aula
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31SOLUTION: Formatting ChartsVídeo Aula
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32Line ChartsVídeo Aula
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33Stacked Line ChartsVídeo Aula
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34Dual Axis ChartsVídeo Aula
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35ASSIGNMENT: Dual Axis Line ChartsVídeo Aula
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36SOLUTION: Dual Axis Line ChartsVídeo Aula
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37Bar ChartsVídeo Aula
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38ASSIGNMENT: Bar ChartsVídeo Aula
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39SOLUTION: Bar ChartsVídeo Aula
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40Stacked Bar ChartsVídeo Aula
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41Grouped Bar ChartsVídeo Aula
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42Combo ChartsVídeo Aula
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43ASSIGNMENT: Advanced Bar ChartsVídeo Aula
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44SOLUTION: Advanced Bar ChartsVídeo Aula
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45Pie & Donut ChartsVídeo Aula
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46ASSIGNMENT: Pie & Donut ChartsVídeo Aula
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47SOLUTION: Pie & Donut ChartsVídeo Aula
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48Scatterplots & Bubble ChartsVídeo Aula
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49HistogramsVídeo Aula
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50ASSIGNMENT: Scatterplots & HistogramsVídeo Aula
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51SOLUTION: Scatterplots & HistogramsVídeo Aula
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52Key TakeawaysVídeo Aula
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53QUIZ: Matplotlib FundamentalsQuestionário
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56Intro to Advanced CustomizationVídeo Aula
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57SubplotsVídeo Aula
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58ASSIGNMENT: SubplotsVídeo Aula
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59SOLUTION: SubplotsVídeo Aula
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60GridSpecVídeo Aula
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61ASSIGNMENT: GridSpecVídeo Aula
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62SOLUTION: GridSpecVídeo Aula
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63Color OptionsVídeo Aula
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64Color PalettesVídeo Aula
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65ASSIGNMENT: ColorsVídeo Aula
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66SOLUTION: ColorsVídeo Aula
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67Style SheetsVídeo Aula
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68ASSIGNMENT: Style SheetsVídeo Aula
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69SOLUTION: Style SheetsVídeo Aula
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70rcParametersVídeo Aula
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71Saving Figures & ImagesVídeo Aula
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72Key TakeawaysVídeo Aula
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73QUIZ: Advanced CustomizationQuestionário