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Data Science

Practical Time Series Analysis & Forecasting - Virtual

This GoDataDriven training offers 2 days of working with time-series data. Are you eager to learn how to create forecasts using data sets? Join us. This Data Science course will empowers you to create a clear vision of the future! In June it will be a virtual class in 4 half-days from 9:00h till 12.30h.

What will the future hold?

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 course empowers you to go beyond “spotting trends” and make data-driven business forecasts.

This training is perfect for

Data Scientists who know Machine Learning and want to expand their skillset by moving from static data sets to dynamic time-dependent data sets. We invite anyone who is familiar with Python and statistics and wants to become more productive and empowered in analyzing and forecasting using time-series data to join us. To ensure you get the most out of these two days, we recommend you have at least one year of work experience with pandas, scikit-learn, and Matplotlib

What will you learn during Practical Time Series Analysis & Forecasting?

You will learn to confidently work with time-series data: cleaning it, removing outliers, and handling missing data. You will also learn how to create forecasts with your data sets and validate your models when using time-series data.

Program

The program consists of nine blocks. Each block consists of a theory component and a hands-on lab.

Day 1:

  • Time features encoding and formatting;
  • Pandas time series features (smoothing, resampling, re-weighting);
  • Sessionization and holiday
  • Feature Engineering for time
  • Additive vs Multiplicative
  • Error-Trend-Seasonality Decomposition;

Day 2:

  • Seasonality estimation;
  • Forecast evaluation and model selection;
  • Forecasting with Prophet;
  • Switch-point Detection;
  • Outlier Detection.

You will 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
  • How to determine which loss functions to use when training models
  • Why feature engineering is fundamental to the success of your modeling
  • How to incorporate seasonality into your models

Data Science Trainers

This Data Science training is brought to you by our trainingspartner GoDataDriven. GoDataDriven works with experts in their field who are always on the lookout for the most innovative ways to get the most out of data. Your trainer is a data guru who enjoys sharing his or her experiences to help you work with the latest tools.

Data Science Learning Journey

Your Data Science Learning Journey starts with a Foundation course, such as the Data Science with Python Foundation, Data Science for Product Owners or Data Science with R. Continue your journey with Advanced Data Science with Python or with the three days of Deep Learning. If you want to proceed to Expert level, register for Data Science with Spark.

Yes, I want to do more with time-series data!

After registering for this training, you will receive a confirmation email with practical information. A week before the training we will ask you about any dietary requirements and share literature if there's a need to prepare. See you soon!

What else should I know?

  • The training requires a laptop. The hands-on labs are run in an online environment, eliminating the need to install software.
  • This course is brought to you by our training partner GoDataDriven.
  • Literature and a nice lunch are included in the price.
  • Travel & accommodation expenses are not included

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