DataHour: Anomaly detection using NLP and Predictive Modeling

DataHour Recording

About the DataHour: 

Most engineering organizations manage daily operations across multiple divisions through automated scheduling of jobs. Some of these domains might include data engineering, customer targeting & campaign management, order management among others. Often, hundreds of thousands of such jobs (if not more!) are scheduled every day, which makes the job management and exception handling piece difficult, time-consuming, and resource intensive. 

In this DataHour session, Paritosh will cover the fundamentals of anomaly detection and discuss its application in job management and exception handling. Rather than proposing a single approach, this session will identify the nuances that should be considered while designing an anomaly detection solution. The session will be aided by real-life examples that leverage NLP, predictive modeling, and other ensemble methods to identify, correct, and prevent anomalies. 

Prerequisites:  No prerequisites. A preread of this article will enable familiarization with the core topic.

Who is this DataHour for?

• Students & Freshers who want to build a career in the Data-tech domain.
• Working professionals who want to transition to the Data-tech domain.
• Data science professionals who want to accelerate their career growth

Speaker:

Paritosh Sinha

Senior Data Scientist at Uber

Problem solver, quick learner, and an experienced team lead, Paritosh is a tenured data scientist with 10+ years of experience in using machine learning, statistical, and NLP techniques to solve business problems across consulting, services, and product-based organizations. He is currently working as a Senior Data Scientist in the marketing division at Uber.

You can follow him on Linkedin.