An in-depth advanced course dedicated to mastering data visualization techniques using Python, Matplotlib, and Seaborn. You will get to work with a real-world Titanic dataset and explore critical aspects of data representation and interpretation.
Overview
Syllabus
- Lesson 1: Deep Exploration of the Titanic Dataset: Features and Characteristics
- Exploring the Titanic Dataset with Pandas Functions
- Diving Deeper into Titanic Dataset Rows
- Debugging Titanic Dataset Exploration
- Exploring Unique Entries in Titanic Dataset
- Exploring Unique Values of 'Embarked' in Titanic Data
- Exploring Titanic Dataset: Add Missing Code Pieces
- Exploring the Titanic Dataset from Scratch
- Lesson 2: Diving into Descriptive Statistics of The Titanic Dataset
- Exploring the Titanic Dataset with Descriptive Statistics
- Applying Descriptive Statistics to Different Column
- Debug the Descriptive Statistics Calculation
- Calculating Median and Mode for 'Age' in the Titanic Dataset
- Generating Descriptive Statistics for the Titanic Dataset
- Adding Descriptive Statistics and Calculating the Interquartile Range for Age
- Exploring Age Statistics in the Titanic Dataset
- Lesson 3: Extending Data Visualization: Enhancing Plots and Analyzing with Matplotlib
- Visualizing Gender Distribution in the Titanic Dataset Using Matplotlib
- Exploring Passenger Class Distribution
- Plotting a Course Correction: Fixing Bar Chart in Pandas
- Discovering the Distribution of Titanic's Passenger Classes
- Crafting the 'Sex Distribution' Bar Plot
- Lesson 4: Unleashing Aesthetics in Visualization: An Introduction to Seaborn's Styling Capabilities
- Visualizing the Data with Aesthetics and Styling through Seaborn
- Changing the Variable for the Count Plot
- Debugging Seaborn Countplot Generation
- Craft Your First Seaborn Plot
- Styling and Customizing a Seaborn Countplot from Scratch
- Lesson 5: Visualizing Distributions with Histograms Using Seaborn
- Visualizing Age Distribution with a Histogram in Seaborn
- Refining Age Distribution with Granular Bin Widths
- Fix the Histogram Plotting Code
- Creating a Histogram with KDE for Age Distribution
- Adding Titanic Passengers' Age Distribution to Seaborn Histograms
- Creating a Histogram with KDE for Age Distribution in Titanic Dataset
- Lesson 6: Visualizing Categorical Relations with Bar Plots
- Visualizing Gender and Survival Rates with Bar Plots
- Tweaking the Code: Visualizing Passenger Class and Survival Correlation
- Correcting The Survival Rate Visualization
- Adding and Displaying a Countplot in Seaborn
- Mastering Bar Plots for Categorical Relations
- Lesson 7: Exploring Multivariate Relationships: Scatter Plots and Correlations in Data Visualization
- Visualizing with Scatter Plots: Age vs Fare in Titanic Dataset
- Changing Marker Style in Scatter Plot
- Fixing the Scatter Plot for Titanic Dataset
- Adding Scatter Plot and Correlation Matrix Calculation
- Implementing Scatter Plot and Correlation Functionality
- Scatter Plot with Extra Dimensions and Correlation Calculation
- Lesson 8: Analyzing Relationships between Passenger Class, Fare, and Survival with Box Plots
- Exploring Ticket Fares and Passenger Classes Using Box Plots
- Switching Orientation of Your Box Plot
- Fixing Passenger Class Analysis with Box Plot
- Constructing Seaborn Box Plots with Titanic Dataset Survival Analysis
- Box Plot Creation and Customization
- Unveil the Relationships: Constructing a Box Plot for the Titanic Dataset
- Lesson 9: Unlocking Insights with Heatmaps: Correlation Analysis in Data Visualization
- Discovering Correlations with a Heatmap
- Creating a Heatmap with a Custom Color Map
- Correcting the Heatmap Visualization
- Adding Heatmap for Correlation Matrix
- Visualizing Correlation Matrix using Seaborn Heatmap
- Creating a Heatmap for the Titanic Dataset Correlation Matrix
