Data Science Bootcamp
A 16-week, project-led bootcamp that takes you from raw spreadsheets to deployed machine learning models.
Curriculum
SIG-01Python for Data Work4 lessons · 16 h
- Core Python
- NumPy
- Pandas
- Handling messy, inconsistent data
SIG-02Statistics & Probability for Decisions4 lessons · 16 h
- Distributions
- Hypothesis testing
- Confidence intervals
- A/B testing
SIG-03SQL & Data Wrangling at Scale3 lessons · 16 h
- Joins
- Window functions
- Aggregation on large datasets
SIG-04Visualization & Data Storytelling4 lessons · 12 h
- Matplotlib
- Seaborn
- Plotly
- Stakeholder-friendly dashboards
SIG-05Machine Learning Foundations5 lessons · 24 h
- Regression
- Classification
- Decision trees
- Ensembles
- Honest model evaluation
SIG-06Deep Learning Basics3 lessons · 16 h
- Neural networks from first principles
- PyTorch introduction
- When deep learning is needed
SIG-07Model Deployment & MLOps Basics3 lessons · 12 h
- FastAPI model APIs
- Basic monitoring
- Running models in production
SIG-08Capstone Project5 lessons · 16 h
- Problem framing
- Modelling
- Deployment
- Written report
- Weekly mentor review
What's included
Questions
Do I need prior experience?
This course is set at beginner to job-ready level. The first module starts from the basics, so you only need the time and a laptop or phone with a stable connection.
What if I miss a live class?
Every live class is recorded and posted within 24 hours. You keep access to the recordings for 12 months.
When are the classes?
The next cohort starts soon (date to be announced) and runs for 16 weeks, ~8 hrs per week. The full timetable and class link appear in My courses before the first class.
Which language is the course taught in?
English.
How do I pay?
Online through UPI, cards, net banking or wallets, processed by Razorpay. You get a receipt by email and the course appears in My courses right away.
Q01What is overfitting, and how do you detect it?Machine learningBasic
The model learns noise in the training data instead of the pattern, so it scores well on training data and badly on new data.
Detect it by comparing training and validation scores, using cross-validation or learning curves. Fix it with more data, a simpler model, regularisation, early stopping or fewer features.
Q02Explain the bias–variance trade-off.StatisticsBasic
High bias means the model is too simple and underfits. High variance means it is too sensitive to the training data and overfits.
Expected error is roughly bias² + variance + noise that no model can remove. Choose the model complexity that gives the lowest validation error.
Q03What is the difference between precision and recall, and when do you favour each?EvaluationBasic
Precision = TP / (TP + FP): of the cases you flagged, how many were right. Recall = TP / (TP + FN): of the real cases, how many you caught.
Favour recall when missing a case is costly, such as disease screening or fraud. Favour precision when false alarms are costly, such as a spam filter. F1 is their harmonic mean; the decision threshold moves the balance.
Q04Why is accuracy misleading on imbalanced data, and what do you use instead?EvaluationIntermediate
If 99% of rows are negative, a model that always predicts negative scores 99% accuracy while catching nothing.
Use the confusion matrix, precision, recall, F1 and PR-AUC. Handle the imbalance with class weights, resampling and a tuned decision threshold.
Q05What is data leakage, and how does cross-validation help avoid it?ValidationIntermediate
Leakage is when the model sees information during training that it won't have at prediction time: future data, features derived from the target, or scaling fitted on the full dataset.
In k-fold cross-validation, fit all preprocessing inside each fold, for example with a scikit-learn Pipeline. For time series, split by time instead of shuffling.
Q06What is the difference between L1 and L2 regularisation?Machine learningIntermediate
L1 (lasso) adds the sum of absolute weights to the loss and pushes some weights to exactly zero, so it also selects features.
L2 (ridge) adds the sum of squared weights and shrinks all weights smoothly, which handles correlated features well. Elastic net combines both.
Q07How does a random forest differ from gradient boosting?ModelsIntermediate
A random forest trains many deep trees independently on bootstrap samples and random feature subsets, then averages them. It mainly reduces variance and works well with little tuning.
Gradient boosting trains shallow trees one after another, each fitting the errors of the previous ones. It mainly reduces bias, is often more accurate, and needs careful tuning of learning rate and number of trees to avoid overfitting.
Q08How would you design and analyse an A/B test?ExperimentsAdvanced
Define one primary metric and the hypothesis. Calculate the sample size from the baseline rate, the minimum effect worth detecting, power (usually 80%) and significance level (usually 5%).
Randomise by user, run the full planned duration, and check that the split between groups matches the plan. Analyse with a suitable test, such as a two-proportion z-test, and report the effect with a confidence interval. Don't stop early because results look good, unless you planned a sequential test.
Q09A model's accuracy drops a month after deployment. How do you investigate?MLOpsAdvanced
First rule out pipeline bugs: missing features, changed units, schema changes.
Then compare live feature distributions with training data to find data drift, and check whether the relationship between features and labels has changed (concept drift). Break errors down by segment, retrain on recent data if needed, and add monitoring and alerts so you catch it sooner next time.
Also on its own page: Data Science Bootcamp interview questions