Course Overview
Are you ready to unlock the power of time-based data and make accurate predictions?
This hands-on course, "Time Series Analysis and Forecasting with Python," is designed to take you from the basics to advanced forecasting techniques using real-world datasets.
In just a few weeks, you'll learn how to explore, visualize, and model time series data using powerful Python libraries and statistical methods.
Throughout the course, we cover essential tools and techniques including:
Introduction to Time Series Data and Components
Data Visualization with Matplotlib and Seaborn
Working with Pandas for Time Series Manipulation
Smoothing Techniques and Moving Averages
Decomposition of Time Series
Stationarity and Differencing
ARIMA and SARIMA Modeling
Forecasting with Facebook Prophet
Model Evaluation and Error Metrics
Building and Deploying Forecasting Models
What you will learn
Introduction to Time Series concepts and components.
Data preprocessing and handling missing values in time series data.
Visualization techniques for identifying patterns and trends.
Statistical methods like Moving Averages and Exponential Smoothing.
ARIMA and SARIMA models for advanced time series forecasting.
FINAL PROJECT: Build and evaluate a forecasting model on real-world time series data.
Course Curriculum
44
Lessons
(
14 hours
)
Course Review
Gabi
The course on Time Series Analysis and Forecasting with Python was incredibly insightful. I learned how to handle real-world data and make accurate forecasts using various Python libraries. The hands-on exercises were particularly helpful in understanding complex concepts like ARIMA and exponential smoothing.
Liam
This course provided a deep dive into time series forecasting, and the Python tutorials were easy to follow. I appreciated how the course walked me through the theory and the practical implementation of techniques such as trend analysis and seasonality. I now feel confident applying these methods to my own data.
