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Time Series Analytics

This course introduces various time series analysis methods for business and data analysts. You will get to learn how to perform time series analysis on real time scenarios like sales of Halloween related products, performance in sports game, sales of consumer goods, and more. The four weeks of the course will introduce three different class of methods suitable for three different types of time series data, and reinforce concepts through hands-on projects using a no-code tool from Ikigai Labs.

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Devavrat Shah
Professor, MIT & CTO, Ikigai Labs
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Farhat Habib
Senior Director of AI, Ikigai Labs
Real-world projects that teach you industry skills.
Learn alongside a small group of your professional peers
Part-time program with 2 live events per week:
Next Cohort
March 27, 2023
Duration
4 weeks
Price
US$ 400
or included with membership

Course taught by expert instructors

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Devavrat Shah

Professor, MIT & CTO, Ikigai Labs

Devavrat Shah has been a professor of Computer Science and AI at MIT since 2005. He founded MIT's Statistics and Data Science Center and currently serves as Faculty Director of the Deshpande Center for Tech Innovation. His research focuses on statistical inference and stochastic networks, spanning a variety of areas including resource allocation in communications networks, inference and learning on graphical models, and algorithms for social data processing including ranking, recommendations and crowdsourcing.  Currently, he serves as CTO of Ikigai Labs (ikigailabs.io) which he cofounded in 2019.

Instructor Photo
Affiliation logo

Farhat Habib

Senior Director of AI, Ikigai Labs

Dr. Farhat Habib is currently Senior Director of AI at Ikigailabs (ikigailabs.io). He previously served as Director AI at Glance, and CEO and Co-Founder at Aegle Health. Previously he held numerous other Senior Research Scientist roles developing algorithms for efficient vehicle routing and developing models to predict consumer behavior. Dr. Farhat‘s research interests are on developing methods and algorithms for extracting meaningful and actionable information from large scale data, be it from biology, advertising, logistics or elsewhere.

The course

Learn and apply skills with real-world projects.

Who is it for?
  • Cross-functional business users (marketing, HR, operations) who want to build demand forecasting models with the ease of spreadsheets and confidence of a data scientist.

  • Founders & Entrepreneurs seeking to make data-backed decisions for their organizations using time series forecasting models.

  • Engineers and Data Scientists seeking a foundation in time series approaches and introduction to an easy to use, no-code platform.

Prerequisites
  • Enthusiasm to learn about time series analysis (no code required!)

Not ready?

Try these prep courses first

Learn
  • Forecasting future values ( with confidence interval) using periodic, stationary data, and trends
  • Dealing with missing, dirty data
Project
Project info coming soon...
    Learn
    • Forecasting for sports and scenarios attributed to policy changes
    • Removing noise from observed values and determining underlying hidden state in data
    Project
    Project info coming soon...
      Learn
      • Inferring activity from action data
      • Methods of classification
      Project
      Project info coming soon...
        Learn
        • Sports forecasting
        • Demand forecasting
        • Financial forecasting
        Project
        Project info coming soon...

          Real-world projects

          Work on projects that bring your learning to life.
          Made to be directly applicable in your work.

          Live access to experts

          Sessions and Q&As with our expert instructors, along with real-world projects.

          Network & community

          Core reviews a study groups. Share experiences and learn alongside a global network of professionals.

          Support & accountability

          We have a system in place to make sure you complete the course, and to help nudge you along the way.

          Frequently Asked Questions

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