Domino Data Science Blog

Manojit Nandi

Manojit is a Senior Data Scientist at Microsoft Research where he developed the FairLearn and work to understand data scientist face when applying fairness techniques to their day-to-day work. Previously, Manojit was at JPMorgan Chase on the Global Technology Infrastructure Applied AI team where he worked on explainability for anomaly detection models. Manojit is interested in using my data science skills to solve problems with high societal impact.

Data Science

Density-Based Clustering

Original content by Manojit Nandi - Updated by Josh Poduska.

By Manojit Nandi28 min read

Data Science

Recommender Systems through Collaborative Filtering

This is a technical deep dive into the collaborative filtering algorithm and how to use it in practice.

By Manojit Nandi15 min read

Data Science

Sampling Based Methods for Class Imbalance in Datasets

Imagine you are a medical professional who is training a classifier to detect whether an individual has an extremely rare disease. You train your classifier, and it yields 99.9% accuracy on your test set. You're overcome with joy by these results, but when you check the labels outputted by the classifier, you see it always outputted "No Disease," regardless of the patient data. What's going on?!

By Manojit Nandi11 min read

Data Science

A/B Testing with Hierarchical Models in Python

In this post, I discuss a method for A/B testing using Beta-Binomial Hierarchical models to correct for a common pitfall when testing multiple hypotheses. I will compare it to the classical method of using Bernoulli models for p-value, and cover other advantages hierarchical models have over the classical model.

By Manojit Nandi23 min read

Data Science

Faster Deep Learning with GPUs and Theano

Domino recently added support for GPU instances. To celebrate this release, I will show you how to:

By Manojit Nandi12 min read

Data Science

Social Network Analysis with NetworkX

Many types of real-world problems involve dependencies between records in the data. For example, sociologist are eager to understand how people influence the behaviors of their peers; biologists wish to learn how proteins regulate the actions of other proteins. Problems involving dependencies can often be modeled as graphs, and scientists have developed methods for answering these questions called network analysis.

By Manojit Nandi7 min read

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