Data Science
Beginner
Data Visualization
Choosing the right chart and building it correctly. Line, bar, scatter, histogram and box plots in Matplotlib, statistical plots and heatmaps in Seaborn, plus multi-chart layouts, styling and time-series visualisation.
30 chapters
2h 30m
What you'll learn
- Introduction to Data Visualization
- Installing Python and Visualization Libraries
- Understanding Data and Visualization Principles
- Introduction to Matplotlib
- Line Charts and Trend Visualization
- Bar Charts and Categorical Data
- Pie Charts and Distribution Visualization
- Scatter Plots and Correlation Analysis
Course content
30 chapters · 2h 30m- 1 Introduction to Data Visualization 5 min
- 2 Installing Python and Visualization Libraries 5 min
- 3 Understanding Data and Visualization Principles 5 min
- 4 Introduction to Matplotlib 5 min
- 5 Line Charts and Trend Visualization 5 min
- 6 Bar Charts and Categorical Data 5 min
- 7 Pie Charts and Distribution Visualization 5 min
- 8 Scatter Plots and Correlation Analysis 5 min
- 9 Histograms and Distribution Analysis 5 min
- 10 Box Plots and Statistical Visualization 5 min
- 11 Working with Colors, Themes, and Styles 5 min
- 12 Subplots and Multi-Chart Layouts 5 min
- 13 Introduction to Seaborn 5 min
- 14 Statistical Visualization with Seaborn 5 min
- 15 Heatmaps and Correlation Matrices 5 min
- 16 Time Series Visualization 5 min
- 17 Interactive Visualization with Plotly 5 min
- 18 Dashboard Design Principles 5 min
- 19 Building Dashboards with Plotly Dash 5 min
- 20 Data Storytelling Techniques 5 min
- 21 Geographic and Map Visualizations 5 min
- 22 Real-Time Data Visualization 5 min
- 23 Data Visualization for Business Intelligence 5 min
- 24 Visualization Best Practices 5 min
- 25 Common Visualization Mistakes 5 min
- 26 Data Visualization with Pandas 5 min
- 27 Advanced Visualization Techniques 5 min
- 28 Data Visualization Interview Preparation 5 min
- 29 Performance Optimization for Large Datasets 5 min
- 30 Final Projects and Real-World Applications 5 min