Site icon R-bloggers

Course Launch: High-Performance Time Series Forecasting in 7 Days!

[This article was first published on business-science.io, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

High-Performance Time Series Forecasting Course is an amazing course designed to teach Data Scientists and Business Analysts the business forecasting tools and techniques that win time series competitions and deliver your organization results! We’ve combined an innovative program with a clear-cut path to forecasting. You’ll undergo a complete transformation learning the most in-demand skills that organizations need right now. Time to accelerate your career!


Create a free account to get the launch discount in 7 Days!

Start Here: Before you go any further, create a free account at Business Science University – Our online educational platform. We will send you a discount link in 7-days (at Course Launch).

Sign Up Here



High-Performance Time Series
Improve your forecasting abilities and accelerate your career

Watch our 2-minute course video that describes the innovative, 3-part roadmap for learning high-performance time series forecasting.


Watch Your Journey (2-Minute Video) – A Streamlined Path to High-Performance Forecasting


Learn from Time Series Competitions
Analyze the winning submissions

High-Performance Time Series Forecasting is a state-of-the-art course designed to teach data scientists and business analysts how to apply the latest forecasting techniques to their businesses by learning from the strategies that won 4 Key Time Series Competitions.


A Project-Based Course
Learn By Completing Real Business Projects with Real Business Data

Project-Based Learning – Learn by Solving 2 Real-World Business Forecasting Problems

This is a project-based course, and here’s the scenario. You are forecasting data related to key performance indicators for an e-learning company. Your primary goals are to improve sales demand forecasting by 10% or more and to set up system for forecasting email subscriptions.

To complete these projects, you will need to learn the state-of-the-art time series forecasting techniques along with time-series feature engineering and working with external data sources. You’ll learn 3 key software libraries along the way!


The Curriculum
By Learn An End-To-End Forecasting Process

Learn the most advanced technology: Modeltime, Timetk, and GluonTS

To complete the projects, you will need to learn:


Your 3-Part Journey to High-Performance Forecasting
The innovative system is divided into three parts

The 3-Part Learning Path for High-Performance Forecasting


Part 1 – Feature Engineering

Part 1 – Feature Engineering

You begin by learning the core data science skills needed to process, transform, visualize, and feature engineer time series data in preparation for machine learning.


Part 2 – Forecasting with Machine Learning

Part 2 – Forecasting with Machine Learning

You learn how to use many different forecasting and machine learning algorithms for time series.


Part 3 – Forecasting with Deep Learning

Part 3 – Forecasting with Deep Learning

You learn a state-of-the-art Python library called GluonTS for time series deep learning.


You learn how to perform deep learning for time series:



Next Steps – Create a free account to get the launch discount in 7 Days!

Career Acceleration Begins in 7-Days: I’m super excited to help you apply the most advanced business forecasting techinques in your organization. Create a free account at Business Science University – Our online educational platform.

Sign Up Here!

To leave a comment for the author, please follow the link and comment on their blog: business-science.io.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.