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Designed to help make organizations

Data Driven

Product Leaders

Program managers and product managers that are focused on metrics like growth and revenue, and prioritization decisions

Data Scientists

Data Scientists and Data Science managers who help map strategic decisions to actionable experimental designs and then interpret the results in a trustworthy manner

The motivation and basics of A/B testing (e.g., causality, surprising examples, metrics, interpreting results, trust and pitfalls, Twyman’s law, A/A tests)
Cultural challenges, humbling results (e.g., failing often, pivoting, iterating), experimentation platform, institutional memory and meta-analysis, ethics
Hierarchy of evidence, Expected Value of Information (EVI), complementary techniques, risks in observational causal studies

Engineering Leaders

Engineering managers, directors, VPs, and CTOs who want to make their organizations data-driven with metrics and A/B tests

Safe deployments
Triggering, especially in evaluating machine learning models
The benefits of agile product development

Designed for Data Scientists and Business Analysts

who want to solve business problems by making accurate predictions with modern forecasting techniques

You will:
  • Identify the business problems that can benefit from modern time forecasting techniques
  • Use appropriate forecasting techniques to maximize the effectiveness of your solution
  • Present the results effectively to persuade a non-technical audience 
You should:
  • Have familiarity with machine learning in industry settings (cross-validation, loss functions, deep learning basics, statistics, and linear algebra basics)
  • Have familiarity with programming (e.g., Python and the associated data science stack: Pandas, Matplotlib, NumPy, etc)
  • Be a mid- to senior-level data scientist and business analysts who work on data-driven solutions for solving business problems

Learn live from a world-class

Instructor

Learn live from a world-class

Instructor

Tim Januschowski is the Director of Pricing Platform at Zalando SE, where he leads the organization responsible for setting prices for the Zalando wholesale business. This involves forecasting of demand heavily. Prior to Zalando, Tim led the time series science organization for Amazon Web Services’ AI division. His teams built multiple AI services for AWS such as SageMaker, Forecast, Lookout for Metrics, and DevOps Guru, top-tier scientific publications, patents, and open source. Tim is a director at the International Institute of Forecasters, serves as a reviewer for the major ML venues, lectures at TU Munich, and advises start-ups such as WhyLabs.

Learn live from a world-class

Instructor

Learn live from a world-class

Instructor

Jan Gasthaus is a software engineer at Meta. Before that, he was principal machine learning scientist at Amazon, where he worked on some of the largest time series prediction problems on the planet. As part of AWS AI Labs, he helped create the technology behind AWS services such as Sagemaker, Amazon Forecast, and Amazon DevOps Guru, and co-created the open-source deep learning forecasting library GluonTS. Before building services for AWS, he worked on a wide range of forecasting and time series analysis problems across Amazon’s businesses, including the massive-scale retail demand forecasting problem, AWS capacity planning, workforce planning, price forecasting, and anomaly detection for cloud resources. Jan holds a Ph.D. in Machine Learning from UCL, has co-authored over 30 scientific articles on time series modeling, served as area chair and reviewer for NeurIPS and other major ML conferences, and has given numerous keynotes, lectures, and tutorials on forecasting.

Learn live from world-class

Instructors

Tim Januschowski is the Director of Pricing Platform at Zalando SE, where he leads the organization responsible for setting prices for the Zalando wholesale business. This involves forecasting of demand heavily. Prior to Zalando, Tim led the time series science organization for Amazon Web Services’ AI division. His teams built multiple AI services for AWS such as SageMaker, Forecast, Lookout for Metrics, and DevOps Guru, top-tier scientific publications, patents, and open source. Tim is a director at the International Institute of Forecasters, serves as a reviewer for the major ML venues, lectures at TU Munich, and advises start-ups such as WhyLabs.

Jan Gasthaus is a software engineer at Meta. Before that, he was principal machine learning scientist at Amazon, where he worked on some of the largest time series prediction problems on the planet. As part of AWS AI Labs, he helped create the technology behind AWS services such as Sagemaker, Amazon Forecast, and Amazon DevOps Guru, and co-created the open-source deep learning forecasting library GluonTS. Before building services for AWS, he worked on a wide range of forecasting and time series analysis problems across Amazon’s businesses, including the massive-scale retail demand forecasting problem, AWS capacity planning, workforce planning, price forecasting, and anomaly detection for cloud resources. Jan holds a Ph.D. in Machine Learning from UCL, has co-authored over 30 scientific articles on time series modeling, served as area chair and reviewer for NeurIPS and other major ML conferences, and has given numerous keynotes, lectures, and tutorials on forecasting.

Recommended by

Industry Experts

Rob J. Hyndman
Professor of Statistics @ Monash University, Australia

Jan and Tim are two of the best forecasters I know, especially when dealing with big data forecasting problems. Both have made important contributions to developing new machine-learning tools designed for forecasting and have years of relevant practical experience.

Ralf Herbrich
Professor of Computer Science @ Hasso-Plattner Institute, Berlin

Over the past 10 years, Tim and Jan have tackled some of the hardest forecasting problems at Amazon and beyond. Their solutions advanced state-of-the-art and considered many aspects: from business questions during inception to method development and productization details during roll-out. Their rich and practical experience will benefit students.

Alex Smola
Distinguished Scientist / VP @ Amazon Web Services

I forecast that this course by Tim Januschowski and Jan Gasthaus on time series forecasting will be great.

Expense
the cost

90% of our learners expense the full cost of our courses to their employer. This includes leading startups and enterprises alike.

Check Expense Approval At Your Company

Exclusive Content

to advance your business

Get access to exclusive content through live sessions, meetups and our Student Portal (even after you finish the cohort). Ask questions and get personal feedback directly from your instructors and others taking the course.

Join a diverse and experienced

Community

This cohort gives you access to a rich community of like-minded professionals from some of the best businesses in the world. Even after the course ends, you will continue to learn and build with each other.

About Tim & Jan's

Live Cohort

Learn how to use time series machine learning techniques for predicting future outcomes to optimize business processes. Two industry experts share their experience in tackling some of the world’s largest and most challenging forecasting problems at Amazon and Zalando, explain the different problem types and techniques that can be used to solve them, and illustrate each of them through examples and case studies.

Session 1: Should your business problem be solved with forecasting?

Tuesday, January 17, 2023
8-10 am PST
  • Understand which and how business processes can be optimized by incorporating (probabilistic) predictions of future outcomes
  • Differentiate strategic from operational forecasting problems with examples from Zalando and Amazon
  • Measure and compare the accuracy of different forecasts

Session 2: Forecasting solutions using a small set of time series

Monday, January 23, 2023
8-10 am PST
  • Case Study: Forecasting top-level energy demand and prices
  • Understand the underlying business problem and the challenges of the resulting data-constrained forecasting problem
  • Identify the effects and structural components that make up the data, such as trend(s), seasonality, exogenous shocks, and noise
  • Identify the appropriate method and tool, such as linear regression, ETS, and ARIMAX

Session 3: Forecasting solutions with a large set of time series

Wednesday, January 25, 2023
8-10 am PST
  • Case Study: Retail demand forecasting
  • Build an intuition for the data via visualization of individual time series and aggregate summaries
  • Obtain co-variates/features and process them
  • Use and tune global ML-powered methods such as Gradient Boosted Trees and Neural Network-based methods like DeepAR

Session 4: Forecasting solutions with dependency structures

Monday, January 30, 2023
8-10 am PST
  • Case Study: Forecasting with causal inputs
  • Forecast demand subject to price changes for millions of products
  • Build what-if analysis using simple and advanced approaches
  • Evaluate & improve forecasting in counterfactual situations

Session 5: What best practices help you avoid common pitfalls in production?

Wednesday, February 1, 2023
8-10 am PST
  • Practical tactics for forecasting exemplified by labor planning
  • Productionize forecasting models including retraining schemes
  • Handle missing data and the associated perils
  • Research approaches to outliers/extreme events such as blizzards and pandemics

Still have questions?

We’re here to help!

Do I have to attend all of the sessions live in real-time?

You don’t! We record every live session in the cohort and make each recording and the session slides available on our portal for you to access anytime.

Will I receive a certificate upon completion?

Each learner receives a certificate of completion, which is sent to you upon completion of the cohort (along with access to our Alumni portal!). Additionally, Sphere is listed as a school on LinkedIn so you can display your certificate in the Education section of your profile.

Is there homework?

Throughout the cohort, there may be take-home questions that pertain to subsequent sessions. These are optional, but allow you to engage more with the instructor and other cohort members!

Can I get the course fee reimbursed by my company?

While we cannot guarantee that your company will cover the cost of the cohort, we are accredited by the Continuing Professional Development (CPD) Standards Office, meaning many of our learners are able to expense the course via their company or team’s L&D budget. We even provide an email template you can use to request approval.

I have more questions, how can I get in touch?

Please reach out to us via our Contact Form with any questions. We’re here to help!

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Learning Experience