Statistics
Z-test for Means
The z-test is one of the most basic, and commonly used hypothesis tests.
Z-test for Proportions
The z-test is a great asset to use when exploring proportions.
One Sample t-test
The one sample t-test is one of the top topics asked in statistics interviews.
Two Sample t-test
The two sample t-test is helpful whenĀ determiningĀ if two population means are equal.
Machine Learning
Random Forest
Random Forest is one of the most useful pragmatic algorithms for fast, simple, flexible predictive modeling.
L1 and L2 Regularization
Regularization introduces a regularization term to the loss function of a model in order to improve the generalization of a model.
How to Handle Imbalanced Dataset
Imbalanced data is one of the most common machine learning problems you’ll come across in data science interviews.
K-means
K-Means is one of the most popular machine learning algorithms you’ll encounter in data science interviews.
How to Handle Categorical Data
Handling categorical data in machine learning projects is a very common topic in data science interviews.
Ensemble Methods: Boosting, Bagging, and Stacking
Examples of ensemble learning, the advantages of boosting and bagging, how to explain stacking, and more.
Feature Selection
How to use feature selection with over 10,000 features, how to calculate feature importance, and the pros and cons of various selection methods.
Principle Components Analysis (PCA)
What principal component analysis is, how it works, the problems you would use PCA for, and the pros and cons associated with PCA.
Gradient Boosting
What are Gradient Boosting and XGBoost? How to describe the architecture of gradient boosting. What are the pros and cons associated with them?
SQL
Ratio Problems
Two most common ways to compute a ratio, and feature two examples to demonstrate solving the problems.
Product Case
Cracking Product Case Problems
Frameworks to crack product case problems in Data Science Interviews.