Transform raw data into powerful forecasts with Python—learn time-series modeling, regression, real-world forecasting.
Sub Category
- Data Science
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Objectives
- Time-series concepts, notations, and use cases. Data loading, preprocessing, and feature engineering using Python.
- Visualizing time-series data for insights. Forecasting using AR, MA, ARIMA, and SARIMA models.
- Handling seasonality and trend components in data. Performing train-test splits and model validation correctly.
- Applying linear regression (simple and multiple) for forecasting. Interpreting regression outputs and evaluating model accuracy.
Pre Requisites
- Basic knowledge of Python (variables, loops, basic functions).
- Familiarity with basic statistics (mean, median, correlation).
- Some exposure to pandas, matplotlib, or NumPy is helpful but not mandatory.
- No prior experience with time-series or regression needed.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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