Dive Deep into Time Series Analysis and Forecasting

Join our Free Udemy Course on Time Series Analysis and Forecasting today!

Welcome to the most comprehensive and practical course on Time Series Analysis and Forecasting! In today’s data-driven world, the ability to predict future trends and patterns is invaluable for business, finance, operations, and beyond. This course empowers you with the knowledge and hands-on skills to confidently analyze time-dependent data and make accurate forecasts using industry-standard techniques and tools.

This course is designed to take you from foundational concepts to advanced forecasting models. You’ll start by understanding the core components of time series data, including trends, seasonality, and cyclic variations. We’ll then dive deep into classic statistical models like ARIMA and SARIMA, mastering their theoretical underpinnings and practical application in Python. But we don’t stop there! You’ll also learn to leverage modern, powerful libraries such as Facebook’s Prophet, renowned for its robustness and ease of use in handling complex real-world data.

Throughout the course, you’ll gain extensive hands-on experience by implementing all concepts using Python, the leading language for data science. We’ll utilize popular libraries like Pandas, NumPy, Statsmodels, and Scikit-learn to clean, prepare, analyze, and model time series data. Each section includes practical exercises and real-world case studies to solidify your understanding and build a portfolio of forecasting projects. By the end, you’ll be equipped to tackle a wide range of forecasting challenges in various industries.

What you will learn:

  • Understand the core components of time series data
  • Master classic statistical models like ARIMA and SARIMA
  • Leverage modern libraries like Facebook’s Prophet for forecasting

Course Content:

  • Sections: 15
  • Lectures: 60
  • Duration: 10 hours

Requirements:

  • Basic understanding of statistics
  • Familiarity with Python programming

Who is it for?

  • Data Analysts looking to expand their skills into predictive analytics.
  • Data Scientists wanting to deepen their understanding of time series models.

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