What will the future hold?
Learn the steps to create a Time Series forecast. 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.
Clients we've helped
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 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.
Data Science Learning Journey
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.