Training schedule
IN-COMPANY TRAINING PROGRAMS
Contact Gert-Jan Steltenpool, if you want to know more about custom data & AI training for your teams. He’ll be happy to help you!
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What will the future hold?
Learn the steps to create a Time Series forecast (with Codes in Python and R). From inventory to website visitors, resource planning to financial data, time-series data is all around us. But how can you know what the future holds? This two-day course empowers you to go beyond “spotting trends” and make data-driven business forecasts.
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What you'll learn
- How to effectively handle time-series data
- Python utilities that make working with time-series a breeze
- Why model validation with time-series data cannot follow the traditional machine learning methodology
- Time Series Forecasting Models in Python
- How to determine which model best suits particular time series data
- Why feature engineering is fundamental to the success of your modeling
- How to incorporate seasonality into your models
The schedule
The program consists of eleven blocks. Each block consists of a theory component and a hands-on lab.
- Time features encoding and formatting;
- Pandas time series features (smoothing, resampling, re-weighting);
- Sessionization and holiday detection
- Feature Engineering for time series
- Additive vs Multiplicative features
- Error-Trend-Seasonality Decomposition;
- Seasonality estimation;
- Forecast evaluation and model selection;
- Forecasting with Prophet;
- Switch-point Detection;
- Outlier Detection.
learning journey
Data Science Learning Journey
Marysia Winkels
Data ScientistMarysia is a data scientist who is proud to work on any AI application that can provide solutions to real-world problems. She is always eager to learn from, and be inspired by, her peers.