Forecasting and Time Series with Python
ARIMA, Prophet, and practical forecasting for business.
What you’ll learn
- Decomposing and understanding time series data
- ARIMA and when it is the right choice
- Prophet for business forecasts with seasonality
- Evaluating forecast accuracy honestly
- Building a forecasting pipeline for business demand
About this course
A focused course on time series forecasting for business. We cover the fundamental models, when to use each, and how to evaluate forecasts honestly — not just on aggregate metrics but on the decisions they drive. Includes a real demand forecasting project.
Curriculum
5 modules · 44 lessons · 14h
1Time series fundamentals
2 lessons
Time series fundamentals
2 lessons
Trend, seasonality, and stationarity.
- Decomposing a time seriesPreview16 min
- Stationarity and differencing18 min
2Practical forecasting models
2 lessons
Practical forecasting models
2 lessons
ARIMA, Prophet, and when to use each.
- ARIMA from scratch25 min
- Prophet for business forecasts20 min
Requirements
- Python and Pandas fluency
- Basic statistics (mean, variance, distributions)
Who this course is for
- Analysts building business forecasts
- Data scientists working on demand or revenue prediction
- Anyone who has been asked to “forecast next quarter”
Your tutor
Principal Data Scientist at Swiggy · Bengaluru
ML practitioner focused on recommendation systems and forecasting. Built Swiggy’s delivery-time prediction model and the menu ranking system. Teaches ML with a strong emphasis on production realities — drift, monitoring, and the boring parts that actually matter.
Student reviews
4.8
67 ratings
Tarun Reddy
22 Jul 2024
Solid and practical
Good balance of theory and practice. The Prophet module was particularly useful for our retail demand planning.
Frequently asked questions
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