Exploring the frontiers of data science, statistics, and machine learning at the Indian Statistical Institute, Bangalore
The BSDS program at ISI Bangalore is a rigorous undergraduate course designed to cultivate the next generation of data scientists and statisticians. Our student community drives innovation through collaborative projects, research initiatives, and knowledge sharing.
We believe in learning by doing—combining theoretical foundations with practical applications to solve real-world problems using statistical methods and modern data science techniques.
Deep foundations in probability, statistics, and computational methods
Opportunities to work on cutting-edge statistical research
Active peer learning and collaborative problem solving
import numpy as np
import pandas as pd
from sklearn import models
class BSDSStudent:
def __init__(self):
self.skills = [
"statistics",
"machine_learning",
"data_analysis"
]
self.passion = float('inf')
def innovate(self):
return "Building the future"Innovative solutions built by our talented students
India faces significant challenges related to public health nutrition. With a large and diverse population, designing affordable, nutritionally balanced diets is critical. Inspired by this issue, our project seeks to solve the following:
Presents an ODE-first treatment of temperature dynamics and then extends it to machine learning
Using time resets while solving the Nestorov ODE to decrease absolute convergence time in convex minimization
Student-led presentations and expert lectures
By __ • xth Year
By __ • xth Year
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By __ • xth Year
Insights, tutorials, and stories from our community
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I created this site to bring all BSDS course materials into one place. I collected all the resources shared by professors through their websites, emails, and classes, wrote down the class notes, converted it into clean LaTeX, and uploaded all of it to GitHub so students can easily access organized, reliable materials anytime at one place
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Workshops, hackathons, and community gatherings
Master complex queries, window functions, and optimization techniques