This course focuses on timeseries forecasting. A specific regression problem which includes a time component and tries to predict future e.g. the revenue for the coming six weeks. The course will build upon basic knowledge of python and machine learning. Data Science Fundamentals is needed as a prerequesite.
This course focuses on timeseries forecasting. A specific regression problem that includes a time component and tries to predict the future e.g. the revenue for the coming six weeks. The course will build upon the basic knowledge of Python and machine learning and includes subjects like datetime feature engineering, temporal cross-validation, and the use of statistical and machine learning models for prediction. At the end of this course, participants will have a clear understanding of the difference between regular regression and time series forecasting and will be able to set up a proper forecasting workflow using Python.
The main subjects we will cover in this training are:
|09.00 – 10.00||–||Introduction||–||Theory|
|10.00 – 11.00||–||Timeseries forecasting theory||–||Theory|
|11.00 – 12.00||–||Timeseries EDA with Python||–||Practice|
|12.30 – 15.00||–||Timeseries data preparation||–||Practice|
|15.00 – 16.30||–||Timeseries as a regression||–||Practice|
|09.00 – 10.00||–||Recap day 1||–||Theory|
|10.00 – 14.30||–||Timeseries with Python||Practice|
|14.30 – 17.00||–||Timeseries forecasting project||Practice|