Course Overview
About This Bootcamp
This two-week program targets Undergraduate seniors and MS students preparing for coding interviews. Topics span the full structure of a typical coding interview process — data structures (trees, graphs, dictionaries), AI-assisted debugging, machine learning systems, and soft skills including communication strategies and problem breakdowns. The bootcamp features hands-on activities throughout.
Schedule
Lecture Syllabus
01
Foundation: Python & OOP
Basics review, Complexity Analysis, Object-Oriented Programming
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Technical Skills
Python basics review, O notation complexity analysis, Map/Reduce concepts, Object-Oriented Programming (OOP).
Communication & Strategy
Interview formats, recruitment pipelines, and hiring timelines.
WC
Wei Chen
chen2732@purdue.edu
PC
Prateek Chennuri
pchennur@purdue.edu
02
Data & Scalability
Core DSA — Trees, Graphs, Dictionaries, List operations, Memory
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Technical Skills
Core DSA: Trees, Graphs, Dictionaries. Map/Reduce concepts, list slicing, and memory management strategies.
Communication & Strategy
How to talk to recruiters, professional email etiquette, and phone screen best practices.
WC
Wei Chen
chen2732@purdue.edu
PC
Prateek Chennuri
pchennur@purdue.edu
03
Foundation: AI Agents
How Claude works, designing agents, adding agents to systems
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Technical Skills
Learn how Claude works, how to design agents for specific tasks, and how to integrate agents into the overall system architecture.
Communication & Strategy
Problem breakdowns: how to break complex interview problems into small, manageable sub-tasks.
DC
Deming Chu
chu292@purdue.edu
ZP
Zhaoying Pan
pan433@purdue.edu
04
ML: Data Pipelines
Preprocessing, ETL (Extract, Transform, Load), handling data noise
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Technical Skills
Data preprocessing strategies, ETL pipeline design (Extract, Transform, Load), and robust techniques for handling data noise and missing values.
Communication & Strategy
How to ask for hints or clarification effectively during technical interviews.
DC
Deming Chu
chu292@purdue.edu
ZP
Zhaoying Pan
pan433@purdue.edu
05
ML: Model Lifecycle
Training loops, loss functions, evaluation metrics (Precision/Recall)
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Technical Skills
Training loop implementation, loss function design, and evaluation metrics including Precision, Recall, and F1-score for model assessment.
Communication & Strategy
How to make an elevator pitch about your resume and adapt it for specific engineering or ML roles.
GP
Gaurav Patel
pate1332@purdue.edu
SD
Suhas Dara
daras@purdue.edu
06
ML: Optimization & DP
Dynamic Programming for sequence modeling and resource allocation
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Technical Skills
Dynamic Programming (DP) techniques for sequence modeling and resource allocation problems commonly seen in ML system design interviews.
Communication & Strategy
Panel discussion with industry guests and students who have had recent interview success — Q&A and lessons learned.
GP
Gaurav Patel
pate1332@purdue.edu
SD
Suhas Dara
daras@purdue.edu