Training schedule
IN-COMPANY TRAINING PROGRAMS
Contact Giovanni Lanzani, 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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Was wird die Zukunft bringen?
Lernen Sie, mit welchen Schritten Sie eine Zeitreihenprognose erstellen Vom Inventar bis zu Website-Besuchern, von der Ressourcenplanung bis zu finanziellen Daten sind wir von Zeitreihendaten umgeben. Doch wie können Sie wissen, was die Zukunft bringt? In diesem zweitägigen Kurs erfahren Sie, wie Sie nicht nur einfach Trends erkennen, sondern datengestützte Geschäftsprognosen erstellen.
This training is for you if…
- You work with time series data or are likely to in the future.
- You have (some) experience with data wrangling with Pandas and machine learning with scikit-learn.
- You want to extract insights from your time series data more easily.
- You want to build forecasting models you can trust.
This training is not for you if…
- You have no data wrangling & analysis experience with Pandas. (Check out the Python for Data Analysts course instead.)
- You have never built a model before with scikit-learn. (Check out the Certified Data Science with Python course instead.)
Clients we've helped
What you'll learn
- Effectively deal with timestamps and formatting with Pandas
- Master fundamental time series analysis techniques with aggregations
- Easily identify trends in the data with rolling averages and various smoothing techniques
- Decompose time series data into trends, seasonality, non-cyclical components, and residuals
- Extrapolate current dynamics into the future with various time series models such as ARIMA and LSTMs
- Explicitly model trends, seasonalities, and holiday effects with Prophet
The schedule
Das Programm besteht aus 11 Blöcken Jeder Block umfasst eine Theorie-Komponente und praktische Übungen.
- Time Feature codieren und formatieren;
- Pandas Time Series Features (glätten, resamplen, neu gewichten)
- Sessionierung und Ferienerkennung
- Feature Engineering für Zeitreihen
- Additive vs. multiplikative Faktoren
- Dekomposition einer Fehlertrend-Saisonalität
- Saisonalitätsschätzung;
- Prognoseauswertung und Modellauswahl;
- Prognosen mit Prophet;
- Schaltpunkterkennung;
- Outlier-Erkennung.
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.
