Data Science Interview Preparation Guide

Data science lecture on k-means highlights its simple, fast, and explainable clustering, while noting the need to choose k, sensitivity to initialization and outliers, struggles with non-spherical shapes, and standardization.

Dimensionality reduction lowers model variance and overfitting by reducing features, speeds training, and saves storage. It also mitigates multicollinearity, aids visualization in 2D–3D, and combats the curse of dimensionality.

Understand multicollinearity in multiple regression, where highly correlated independent variables count the same feature twice and undermine statistical significance. Use VIF with a threshold of five to remove one variable.

Evaluate algorithms on unbalanced data using metrics like precision, recall, F1, MCC, and ROC AUC to avoid misleading accuracy. Use undersampling, oversampling, SMOTE, or cost-sensitive models to balance classes.

Learn to handle missing data with deletion or imputation, using mean, median, mode, interpolation, and multiple imputation, plus time series considerations and predictive models like logistic regression and ANOVA.

Learn to determine whether offering coupons affects purchase decisions by using one-way ANOVA to compare means across three groups (two coupon types and no coupon) using the F distribution.

Explore the advantages and disadvantages of neural networks and deep learning, including state-of-the-art performance across image, language, translation, speech, and time series tasks, and challenges like interpretability and data needs.

The popularity of deep learning rises from vast data and faster GPUs, enabling the training of deep neural networks for state-of-the-art results in image recognition.

Pooling, including max pooling, drives translation invariance in CNNs by summarizing nearby outputs, so a shifted input yields similar pooled values, while convolution and activation create features used by pooling.

Explore the four main CNN layers: convolutional, ReLU, pooling, and fully connected, and how they build feature learning toward a softmax-classified image output.

Use many small 3x3 kernels to achieve the same receptive field with fewer parameters and computations, while enabling more filters and activation functions for a more discriminative CNN.

Explain how generative adversarial networks, consisting of a generator and a discriminator, train to produce images indistinguishable from real ones by fooling the discriminator through their competing objectives.

Apply the softmax non-linearity as the last network operation to convert a real-valued vector into a probability distribution by exponentiating each element and normalizing by the sum of exponentials.

Explore the relu activation function, noting its computational efficiency and non-linear backpropagation-friendly nature, while recognizing the dying relu drawback where gradients vanish for zero or negative inputs, hindering learning.

Explore how backpropagation trains neural networks by performing a forward pass, computing error, propagating gradients to adjust weights, and updating them across batches and epochs.

Explore four hyperparameter tuning methods for machine learning, including manual tuning, grid search, random search, and Bayesian optimization, and learn how each affects model accuracy.

Explain variational autoencoders, highlighting probabilistic hidden cells, Bayesian inference and reparameterization, and discuss their generative applications in images, faces, music, molecules, and drug design.

Explore Hopfield networks, a fully connected recurrent architecture where every neuron connects to all others, with fixed weights that converge to learned patterns as associative memory for pattern recognition tasks.

Explore echo state networks as a variant of recurrent networks with random, nonlayered connections. Train only observer-to-hidden links, with input priming, for efficient time series forecasting and sequence prediction.