Data Scientist Real Interview Questions & Code Challenge

Master the seven steps of the machine learning lifecycle, from data gathering and processing to feature engineering, algorithm selection, training, evaluation, deployment, and ongoing maintenance.

Dbscan uses density-based clustering with an epsilon radius and a min points threshold to form clusters and automatically identify anomalies or outliers in large data.

Learn how Facebook's Prophet algorithm enables univariate time series forecasting with daily data, tune parameters for accuracy, and apply change point detection for robust modeling.

Learn to build a univariate time series model for sales by sequentially splitting data into training and validation sets to preserve seasonality, then forecast future dates.

Explore how support vector machines perform supervised learning by using a linear decision boundary to classify high and low risk loans based on features like annual pay and credit score.

Explore how the support vector classifier finds the best separating line between red and blue classes by maximizing the margin, using the nearest data points to determine distance.

Explore how support vector machine regression builds a linear model to predict sales from promotion data, using a decision boundary to find the best regression line.

Explore how the k-nearest neighbor classifier predicts cancer data classes by analyzing distances to labeled points, using majority voting and mean-distance tie-breaking, with Euclidean distance as the measure.

Identify the best cluster formation using the elbow plot for k-means clustering. Plot cost against k to locate the elbow and determine the optimum number of clusters.

Explore how convolutional neural networks power image recognition in computer vision by transforming rgb image data through convolution, pooling, and ReLU activations to classify objects.

Build a multiclass Keras image recognition model for apple, banana, and dates detection using transfer learning with MobileNet, image augmentation, and a three-class output.

Explore how deep learning autoencoder models enable dimension reduction with exact reconstruction of data patterns, including applications to audio input.

Explore auto encoders and decoders for dimensionality reduction, compressing thousand features into a few through layered encoders, then reconstructing original data with decoders using a sigmoid activation.

Explore the siamese network architecture with two identical subnetworks that compute a distance between inputs, and use distance-based loss to learn similarities and dissimilarities.

Learn to generate BERT sentence embeddings with transformer attention mechanisms and compare sentences via cosine similarity in Python.

Explore Google's universal sentence encoder with TensorFlow for generating contextual sentence embeddings, use cosine similarity, and apply to text clustering, classification, and multilingual natural language processing tasks.

Understand how a single neuron processes inputs with weights and a bias, applies an activation function, and adjusts weights to minimize loss.

Explore back propagation and the chain rule to compute how small changes in weights affect the loss, derive derivatives through a neural network, and apply gradient descent to reduce error.

Apply gradient descent with adaptive learning rates to minimize loss and optimize weights, stopping at the global minimum via early stopping.

Explore the h2o automated model development framework, import data, prepare features and target, train multiple models with automated ml, compare leaderboards and accuracy, and select the best model.

Explore Flask as a web framework that bridges client and server, enabling asynchronous Ajax requests to endpoints, JSON responses, and integration with databases, models, and file systems.

Build a Flask api that runs on localhost, handles get and post requests, defines endpoints, and returns an age calculation from submitted data.

Learn how to implement a file upload in a web app using an upload page, render_template, and a post endpoint that saves files with secure_filename and returns a 200 response.

deploy machine learning models with flask by creating an api for prediction via get or post requests, loading a trained model, and returning json predictions.

Explore how to access Kaggle cloud platforms for assessment practice, create a Google account, load cancer detection data, open notebooks, and perform initial exploratory data analysis with pandas.