Data Science Interview Preparation Guide

Course Curriculum

Chapter 1: Introduction

Lecture 1: List of questions

Lecture 2: Course Introduction

Chapter 2: Machine Learning

Lecture 1: What is the bias-variance tradeoff?

Lecture 2: How is KNN different from k-means?

Lecture 3: How would you implement the k-means algorithm?

Lecture 4: How do you choose the k in k-means clustering?

Lecture 5: What are the pros and cons of the k-means algorithm?

Lecture 6: What does an ROC curve show?

Lecture 7: What is the difference between a type 1 and type 2 error?

Lecture 8: Define precision and recall.

Lecture 9: What is k-fold cross validation?

Lecture 10: Explain what a false positive and a false negative are.

Lecture 11: When would you use random forests Vs SVM and why?

Lecture 12: Why is dimension reduction important?

Lecture 13: What is principal component analysis? Explain the sort of problems is it used 4?

Lecture 14: What are the assumptions required for linear regression?

Lecture 15: What are some of the steps for data wrangling and data cleaning?

Lecture 16: What is multicollinearity and how do we deal with it?

Lecture 17: You are given a dataset on cancer detection. You have built a classification mod

Lecture 18: How would you evaluate an algorithm on unbalanced data?

Lecture 19: You have the 95th percentile of web server response times generated every 2 seco

Lecture 20: What is Bayes’ Theorem? How is it useful in a machine learning context?

Lecture 21: What is ‘Naive’ in a Naive Bayes?

Lecture 22: Explain the difference between L1 and L2 regularization.

Lecture 23: What cross-validation technique would you use on a time series dataset?

Lecture 24: How can a time-series data be declared as stationery? What statistical test woul

Lecture 25: What are the main components of an ARIMA time series forecasting model?

Lecture 26: How do you ensure you’re not overfitting with a model?

Lecture 27: You are given a data set consisting of variables with a lot of missing values. H

Lecture 28: What’s the “kernel trick” and how is it useful?

Lecture 29: When would you use gradient descent (GD) over stochastic gradient descent (SDG),

Lecture 30: Your organization has a website where visitors randomly receive one of two coupo

Lecture 31: What is the differentiate between univariate, bivariate and multivariate analysi

Lecture 32: Explain SVM algorithm.

Lecture 33: Describe in brief any type of Ensemble Learning.

Lecture 34: What is a Box-Cox Transformation?

Lecture 35: What is the Central Limit Theorem?

Lecture 36: What is sampling?

Lecture 37: Give 4 examples of probability-based sampling methods and how they work

Lecture 38: Give 4 examples of non probability-based sampling methods and how they work

Chapter 3: Neural Networks and Deep Learning

Lecture 1: What are the advantages and disadvantages of neural networks?

Lecture 2: What in your opinion is the reason for the popularity of Deep Learning in recent

Lecture 3: Why do we use convolutions for images rather than just FC layers?

Lecture 4: What Is the Difference Between Epoch, Batch size, and Number of iterations?

Lecture 5: What makes CNNs translation invariant?

Lecture 6: What are the 4 main types of layers used to build a CNN?

Lecture 7: What are 3 types of spatial pooling that can be used?

Lecture 8: What is the stride in convolutional layers?

Lecture 9: What are vanishing and exploding gradients?

Lecture 10: What are 4 possible solutions to vanishing and exploding gradients?

Lecture 11: What is dropout for neural networks? What effect does dropout have?

Lecture 12: Why do segmentation CNNs typically have an encoder-decoder style / structure?

Lecture 13: What is batch normalization and why does it work?

Lecture 14: Why would you use many small convolutional kernels such as 3×3 rather than a few

Lecture 15: What is the idea behind GANs?

Lecture 16: Why we generally use Softmax non-linearity function as last operation in-network

Lecture 17: Activation function 1

Lecture 18: Activation function 2

Lecture 19: Activation function 3

Lecture 20: What is backpropagation and how does it work?

Lecture 21: What are the common hyperparameters related to neural network structure?

Lecture 22: What are 4 methods of hyperparameter tuning?

Lecture 23: Network architecture 1

Lecture 24: Network architecture 2

Lecture 25: Network architecture 3

Lecture 26: Network architecture 4

Lecture 27: Network architecture 5

Lecture 28: Network architecture 6

Lecture 29: Network architecture 7

Lecture 30: Network architecture 8

Lecture 31: Network architecture 9

Lecture 32: Network architecture 10

Lecture 33: Network architecture 11

Lecture 34: Network architecture 12

Chapter 4: Statistics

Lecture 1: Item sold by Amazon seller

Lecture 2: Random Coin

Lecture 3: Find the total number of ways 5 people can sit in 5 empty seats.

Lecture 4: Code in a particular order

Lecture 5: To win the lottery, you must select the 5 correct numbers in any order from 1 to

Lecture 6: Cards

Lecture 7: You are at a Casino and have two dices to play with.

Lecture 8: Plane to London

Lecture 9: 40 Cards

Lecture 10: How do you assess the statistical significance of an insight?

Lecture 11: Explain selection bias (with regard to a dataset, not variable selection)

Lecture 12: What is an outlier?

Lecture 13: How do you handle missing data? What imputation techniques do you recommend?

Lecture 14: Give an example where the median is a better measure than the mean

Lecture 15: Given two fair dices, what is the probability of getting scores that sum to 4?8?

Lecture 16: What is the Law of Large Numbers?

Lecture 17: How do you calculate the needed sample size?

Lecture 18: What is A/B testing?

Lecture 19: What is p-value?

Lecture 20: How do you prove that males are on average taller than females?

Lecture 21: Infection rates at a hospital

Lecture 22: You roll a biased coin (p(head)=0.8) five times. What’s the probability of getti