Workshop Flow:

Intro- (20 mins)
- Discuss data problems and tools available to solve them
- Use cases of Ikigai for industry problems

Hands On (2 hours)
- Train housing data on Random Forest and make predictions

                - Processing data

                - Building data pipelines (ETL)

-Dashboarding (Creating an app interface that can be made available to other users)

- Visualisations of actual vs predicted parameters

- creating an input tab for external users to enter new parameters and get predictions (ML Ops)
- Web scraping and alert design (if time permits)

About the Speaker:

Devavrat Shah is Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT, where he has been teaching since 2005. He is currently the faculty director of the Deshpande Center for Tech Innovation. He was the founding director of the Statistics and Data Science Center at MIT between 2016 to 2020. His current research interests include algorithms for causal inference, social data processing, and stochastic networks. He is a distinguished alumnus of his alma mater, IIT Bombay. His research contributions have been recognized with several awards including the George B. Dantzig Dissertation award (2005), the ACM SIGMETRICS Rising Star Award (2008), and the INFORMS Erlang Prize (2010); in addition, his research has been recognized with a 2019 test of time award from ACM SIGMETRICS as well as best paper awards from many conferences and professional societies such as IEEE Infocom, ACM SIGMETRICS, NeurIPS, INFORMS APS, and INFORMS M&SOM. In 2013, he co-founded the machine learning start-up Celect (part of Nike) which helps retailers optimize inventory using accurate demand forecasting. In 2019, he co-founded Ikigai Labs with the mission of building a self-driving organization by empowering data business operators to make data-driven decisions with the ease of spreadsheets. 

Registration link