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Module 1: Introduction to Data Science Interviews
The Data Science Interview Roadmap
Decoding Company Expectations
Module 2: Interview Strategy & Communication
The PREP Framework
Storytelling with Data
What to Do When You Don't Know
Building Your Interview Confidence
Module 3: Python
Python: List vs. Tuple
Pandas: .loc vs .iloc
Pandas: Handling Duplicates & Missing Values
Reverse a String
Valid Palindrome Check
Two Sum
First Non-Repeating Character
Defang an IP Address
Missing Number
Climbing Stairs
FizzBuzz
Euclidean Distance
Merge Overlapping Intervals
Factorial Trailing Zeroes
Coin Change (Fewest Coins)
Find M Largest Numbers
String Permutations
Pandas: Grouping and Aggregation
Sales Analysis with Promotions
Min-Max Scaling (from Scratch)
Modify Array Columns
First to Roll a Six
Email Validation with Regular Expressions
Consecutive Monthly Purchases
Parse Encoded String
Replace Words with Roots (Stemming)
Module 4: SQL
Google User Insights
Apple Product Feedback
Netflix Subscriber Growth
Amazon Product Performance
Microsoft Talent Acquisition
Meta User Engagement
Google Financial Planning
LinkedIn Event RSVP Rate Analysis
Shopify Customer First Purchase Analysis
Twitter (X) Identifying Inactive Users
Module 5: Descriptive Statistics
Prasad Tech Website Load Times
Employee Salaries.
Kalanajali Customer Age Analysis
FiberNet Customer Satisfaction Analysis
ZEE5 User Engagement Analysis
Karachi Bakery Sales Analysis
Heritage Sales Team Performance
Share Chat Telugu User Engagement Analysis
Bolt Production Quality
TSFC Profit Analysis
Purchase Data Analysis
Sankranti Campaign Analysis
Andhra Recipes Website Traffic Analysis
TeluguGaming "Amaravati Warriors" Player Analysis
Employee Data Analysis
Badam Pista Burfi Sales Analysis
Tirumala Laddu Production: Quality Analysis
Tollywood Box Office Analysis
Karimnagar Dairy: Milk Procurement Analysis
Andhra Bank (Union Bank) Loan Approval Analysis
Module 6: Inferential Statistics
Hyderabad Rides: A/B Test Analysis
Andhra Spice: A/A Test Analysis
Pochampally Weaves: Confidence Interval for Average Purchase Value
Impact of Latency on User Engagement.
Andhra Spice: "Order Now" Button A/B/n Test
Hyderabad Homes: Apartment Price Analysis
APSRTC/TSRTC: Passenger Satisfaction Survey
Sri Krishna Pearls: Biryani A/B Test Interpretation
Deccan Chronicle: Employee Satisfaction Survey
Vaidya Ayurvedic Formulations: Tirumala Forest Blend Trial
Sakshi News App: Telugu Cinema Feature Test
Tirumala Milk Products: Milk Quality Modeling
TeluguConnect: Cultural Content Feature A/B Test
Lalitha Jewellery: Dasara Campaign Analysis
Vijaya Industries: Saree Defect Rate Analysis
Wedding Season Recommendation Test
Krishna Waters: Bottling Process Quality Comparison
Module 7: Probability
Bayes' Theorem
PMF vs. PDF
Central Limit Theorem
Independent vs. Mutually Exclusive Events
Type I & Type II Errors, Alpha & Beta
Two Dice - Probability Sum is 7
Two Aces from Deck (No Replacement)
At Least One Five with Two Dice
Ants on Triangle
Two Red Balls from Bag
Two Cards (1-100), One Double the Other
Two Children, At Least One Boy
Seattle Rain & Three Friends
Disease Screening
Unfair Coin Detection
Fair vs. Biased Coin
Conditional Probability with Dice
Rideshare Voucher Expected Cost
Expected Value Properties
Call Center
Ad Clicks
Heights and Bell Curves
Flat Probabilities
Newsfeed Ads: Binomial vs. Poisson
Testing Website Changes: P-values
Zebras on Triangle
Netflix Movie Raters
Module 8: Business & Product Case Studies
Swiggy Stories Feature Analysis
Ola Ride Demand Analytics
BookMyShow Dynamic Pricing
Nerchuko's Telugu App CLV
BigBasket Checkout Upselling
Hotstar Telugu User Re-engagement
Rapido Driver Lifetime & LTV
Aha Subscription Churn
Flipkart Ugadi Promotion ROI
Dunzo Delivery Partner Incentives
Myntra Traditional Wear Demand
Zepto Market Entry & Model Adaptation
Success Metrics for Aha Interactives
KreditBee P2P Lending for Telugu SMEs
Inshorts Content & Engagement Strategy
Duolingo Telugu-English Exchange
AstroTalk Telugu Platform Optimization
PhonePe Rural Telugu Initiative
Meesho Social Commerce Optimization
BookMyShow Telugu Cultural Events
Module 9: ML Breadth
Linear vs. Logistic vs. Decision Trees
Clustering Algorithms: K-Means vs. Hierarchical vs. DBSCAN
Classification Metrics: Precision, Recall, F1-Score & AUC-ROC
Cross-Validation Techniques: A Practical Guide
Feature Selection Methods Explained
Overfitting and Regularization: L1 & L2 Explained
Ensemble Methods: Bagging, Boosting, & Stacking
Neural Network Fundamentals: Forward & Backward Pass
CNN Architecture: Convolution, Pooling & Fully Connected Layers
Recurrent Neural Networks: RNN, LSTM, & GRU
NLP Preprocessing: Tokenization, Stemming & Lemmatization
Text Representation: From Counts to Context
Transfer Learning & Fine-Tuning Explained
The Transformer Architecture & Self-Attention
Dimensionality Reduction: PCA vs. t-SNE vs. UMAP
Time Series Patterns: Trend, Seasonality, & Cyclical
Recommendation Systems Explained
Computer Vision Tasks: Classification, Detection, & Segmentation
Module 10: ML Depth
Logistic Regression: Gradient Descent Derivation
Backpropagation in a 3-Layer Neural Network
Bias-Variance Tradeoff Decomposition
The Attention Mechanism Explained
XGBoost Objective Function & Missing Values
Principal Component Analysis (PCA)
SVM Dual Formulation, KKT & Kernels
Information Theory in ML: Mutual Information, Gain, Gini & Bottleneck
Clustering: K-means vs. Gaussian Mixture Models (GMM)
ARIMA Models for Time Series Forecasting
Adam Optimizer Explained
Feature Scaling & Interactions
Model Selection: AIC, BIC & Cross-Validation
Ensemble Methods: Variance Reduction & Diversity
Module 11: System Design for Data Scientists
Amazon Duplicate Listing Detection
Zepto Demand Forecasting
Instagram Reels Recommendation
Module 12: The Take-Home Challenge
User Behavior Analysis
Customer Transaction Insights
FinTech Customer Churn Prediction
Bike Rental Demand Forecasting
Multilabel Document Categorization
GenAI-Powered Book Management Platform