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Journey into Data Science with Python
Intro to Time Series Analysis with Airline Data
Intro to Time Series Analysis with Airline Data
This data-heavy course focuses on trends and patterns in air travel history. Using complex visualizations and time series analysis, you will learn about growth trajectories and seasonal fluctuations in the air travel sector.
Lessons and practices
Lesson 1: Exploring the Seaborn Flights Dataset: An Initial Glimpse
Exploring the Flights Dataset: First Impressions
Slightly Shifting our Spaceship: Adjusting our View on Flight Data
Navigating Through the Turbulence: Dataset Entry Count Issue
Navigating the Cosmos: Total Entries and Dimensions of the Airline Dataset
Flight Data Exploration: Final Takeoff
Lesson 2: Guiding Through Air Travel Using Line Plots: An Introduction to Trend Analysis in Python
Visualizing Trends in Air Travel with Line Plots
Plotting July Passenger Counts Over a Decade
Debugging the Flight Passenger Trends Plot
Creating a Line Plot to Visualize Monthly Passenger Counts
Visualizing Yearly Total Passenger Counts with a Line Plot
Lesson 3: Analyzing and Visualizing Seasonal Fluctuations with Python
Visualizing Seasonal Trends in Air Travel Passenger Counts
Analyzing Monthly Trends in Air Travel Passenger Counts
Debugging Monthly Passenger's Count Plot
Customizing a Line Plot for Monthly Passenger Counts Visualization
Visualizing Seasonal Trends in Air Travel From Scratch
Lesson 4: Unveiling Heat Maps for Monthly Data Analysis in Python
Creating and Visualizing a Heatmap of Air Travel Data
Altering Heatmap's Color Scheme
Debugging Heat Map Creation
Enhancing the Heatmap Visualization of Monthly Airline Passenger Counts
Crafting a Detailed Heatmap from Scratch
Lesson 5: Predicting Air Travel Trends with Linear Regression
Predicting and Visualizing Future Flight Traffic with Linear Regression
Predicting Far Beyond: The Passenger Counts of 1975!
Flight to the Future: Debugging Passenger Prediction
Predicting the Sky Traffic: Next Decade's Passenger Counts
Predicting Decade Passenger Counts using Linear Regression
Drawing the Future: Predicting Passenger Trends with Regression
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