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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

15 Most Common Data Science Interview Questions

Subhadeep Mandal
Subhadeep Mandal Last Updated : 27 Jun, 2022
9 min read
This article was published as a part of the Data Science Blogathon.
Interview Questions
Source – pinterest.com

Introduction

Job interviews are…well, hard! Some interviewers ask hard questions while others ask relatively easy questions. As an interviewee, it is your choice to go prepared. And when it comes to a domain like Machine Learning, preparations might fall short. You have to be prepared for everything.

While preparing, you might have stuck at a point where you wonder what more shall I read. Well, based on almost 15-17 data science interviews that I have attended, here I have put 15,  very commonly asked, as well as important Data Science and Machine Learning related questions that were asked to me in almost all of them and I recommend you must study these thoroughly. This will help you utilize your time efficiently and focus on what is important rather than wandering across the web to search for questions and answers to them.

15 Most Common Interview Questions

1. Explain the Difference between Classification and Regression with at least 1 example.

Ans. As the name suggests, Classification algorithms classify the output variables into two or more classes or categories so that they become discrete values. Whereas, Regression algorithms calculate continuous and real values, unlike Classification algorithms.

Explain this difference using an example. Using classification, a company can predict whether an employee might resign or not in the next 5-10 years, using their data, like their salary, bonus, work experience, their age etc. Using regression, the same company can analyse their product demand, analyse product manufacturing errors etc. using their product purchase history, customer satisfaction, returns, complaints etc.

2. Which is your favourite clustering algorithm? Explain why.

Ans. My favourite clustering algorithm is K-Means Clustering. The simplicity of this algorithm draws my attention. In K-Means all we have to do is these simple 4 steps-

1. Choose k number of clusters.

2. Specify the cluster seeds.

3. Assign each data point to a centroid

4. Adjust the centroids

 By these steps, K-Means can scale according to the dataset and is also very efficient. The results are easy to interpret too.

Moreover, the elbow method is a very interesting and simplistic approach to optimise the K-Means algorithm taking account of the WCSS (Within Cluster Sum of Squares) Vs. the k number of clusters, which always produces a curve that looks like an elbow!

Interview Questions
Source-Datanovia

The elbow point value is the optimal number of clusters for your data! It greatly improves your model performance in a very elegant yet easy way, which I admire!

3. What is the generalized equation of a supervised Machine Learning model and explain it in your own words.

Ans. Almost every supervised Machine Learning model is represented mathematically as y = f(x), where, y is the output variable and x is the dependent input feature. f(x) is a generalized representation of a Machine Learning model, that takes in input features(xi = x1, x2, x3….,xn) and gives out predicted output (ŷ).

4. What is bootstrap sampling? How is it useful in ML? Explain with a use-case.

Ans. The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It is used in applied Machine Learning to estimate the skill of machine learning models when making predictions on data not included in the training data.

In Machine Learning, bootstrap sampling is used in an ensemble algorithm called bootstrap aggregation or Bagging. It helps avoid overfitting and improves the stability of a Machine Learning algorithm.

For example, the Bootstrap Aggregation algorithm helps create multiple different models from a single training dataset. A very popular use case is predicting election results for an area using sample data instead of taking election data of the entire population.

5. What are overfitting and underfitting? Explain in terms of statistic terminologies and suggest measures to reduce both.

Ans. Overfitting is the phenomenon that occurs when we fit a huge amount of data into a Machine Learning model (which might include noisy and inaccurate data) or also when a model fits exactly to its training data points such that it starts giving wrong predictions. The model becomes ‘overconfident’ and treats data points outside its knowledge to be wrong or inaccurate. Statistically, Overfitting occurs due to low bias and high variance. Overfitting can be reduced by using a linear model for linear data, pruning unnecessary nodes in a Decision tree to reduce its complexity, training with more data where necessary etc.

Underfitting occurs when a Machine Learning model neither fits on the training data nor generalizes to the new data to test on. The model fails to recognize the underlying trends of the data. It usually happens when we have very fewer data to build an accurate model, also when we try to build a linear model with fewer non-linear data. Statistically, underfitting occurs due to high bias and low variance. Underfitting can be reduced by using other Machine Learning algorithms, fitting more data, removing noise from data, increasing the number of features by performing feature engineering etc.

6. Define Euclidean, Manhattan and Minkowski distance. In what kind of problems are they used and why?

Ans. Euclidean distance: Euclidean distance between two points is the length of the line that joins the two points. Consider two points A(x1, y1) and B(x2, y2) in the cartesian plane. The Euclidean distance between them is represented as:

Interview Questions

Manhattan distance: Manhattan distance between two points in an n-dimensional space is the sum of the distances in each dimension. Consider two points A(x1, y1) and B(x2, y2) in the cartesian plane. The Manhattan distance between them is represented as:

Manhattan distance formula

Minkowski distance: Minkowski distance is a generalized form of Euclidean and Manhattan distances. For two points A(x1, x2) and B(y1, y2) the Minkowski between them is:

7. Describe what you mean by bias-variance trade-off in ML.

Ans. Bias is the difference between the actual and predicted output. In n-dimensional space, bias can be the distance between the true value points and the predicted value points. A high bias means the model is streamlining the common data points and hence such a simplified approach leads to incorrect prediction and a huge distance gap between true and predicted values. Such bias introduces underfitting.

On the other hand, variance is how varied predictions our model is doing. In a space where data points are widely scattered, the model tries to learn every data point so perfectly that instead of recognizing patterns, it becomes more like a forcefully memorizing them. Memorization will only lead to good performance as long as it is predicting training data’s outputs. As soon as we give new unknown data to predict, it will give an error. Such behaviour occurs when the model tries to fit the noise and fluctuations (variance). It introduces overfitting.

So bias and variance are opposites to each other, and the trade-off is simply computing the balance between bias and variance so that error is minimised. Whenever we have a low bias, variance increases and vice versa. So, we have to model in a way that bias and variance both stay balanced and neither overfitting nor underfitting occurs. Such kind of tuning is called Bias Variance Tradeoff.

8. What is Vectorization? Why is Vectorization important in NLP use cases?

Ans. Machine Learning algorithms cannot work on raw text data. They are to be converted to numerical data. Vectorization is a method of converting text data to numerical vectors so that the text can be easily analyzed or consumed by the Machine Learning algorithm.

In NLP, Vectorization is used to map words or phrases from vocabulary to a corresponding vector of real numbers to enable word predictions, word similarities/semantics and grammar checks.

9. Mention some differences between covariance and correlation.

Ans. a. Correlation coefficients range from -1 to 1 and covariance values range from -∞ to +∞.

b. Correlation measures how two variables are related to each other and by how much. Covariance measures whether a variation in one variable results in a variation in another variable.

c. Changing the scale of variables affects covariance i.e. if the variables are changed using similar or different constants, the calculated covariance between the variables changes. But in the case of correlation, this doesn’t happen. Correlation is not affected by a change of scale.

10. Why is the Naive Bayes algorithm, naive? Mention its advantages and disadvantages.

Ans. A naive Bayes classifier presumes that a particular feature of a class is unrelated to any other feature, given the class variable. Simply, Naive Bayes assumes that the variables are independent of each other. This assumption may or may not be correct as it falls under the probabilistic classifiers in statistics. This is the reason it’s called Naive.

Advantages

1. Extremely easy to implement and follows a simple probabilistic approach (Bayes’ Theorem).

2. Works great in small datasets, and can classify stuff even when no data is there!

3. Massively used for spam-filtering and specially text-classification tasks ( as it works better when attributes/features in data are independent of each other).

Disadvantages

1. Works poorly with numerical data, as numerical data is mostly normally distributed.

2. The ‘naive’ approach to considering attributes independent of each other, doesn’t often work well in complex problems.

11. Explain a decision tree. What do you know about the ID3 algorithm?

Ans. A decision tree is a tree-like structure that contains leaves, nodes and edges. It has a root node, parent nodes and child nodes. Each parent node is split into one or more child nodes. The terminal nodes or leaf nodes represent the output variables.

In the decision tree, ID3 stands for Iterative Dichotomiser 3. Dichotomiser means dividing. The algorithm is used for feature selection in a decision tree by the top-down greedy approach (builds the tree from the top and selects the best feature at each step iteratively using Entropy/Information Gain). Each parent node is divided into one or more child nodes iteratively till no more division is possible.

12. Entropy or Gini Impurity? – define them and decide which is better for selecting the best features in your data.

Ans. Entropy: It is the measure of impurity of a variable. It describes how well a node is split. The higher the entropy of a node, the higher will be its impurity. For the best split, it is always suggested to choose nodes with low entropy. Entropy is calculated by –

Gini Impurity: Like Entropy, Gini impurity also measures the impurity of a variable or how good a node is split. In both, Entropy and Gini impurity, a node that has multiple classes is considered impure and a node having a single class is pure.

Gini Impurity
Source – thatascience

If we compare both the methods then Gini Impurity is more efficient than entropy in terms of computing power. Training time when using the Entropy criterion is much higher because Entropy is calculated using logarithms. Hence, Gini impurity must be preferred over Entropy.

13. What are leaf nodes in the Decision tree?

Ans. Leaf nodes in a Decision tree are the end or terminal or child nodes. These nodes cannot be split any further. Hence, they are the output classes or categories like YES or NO; GOOD, BAD and SATISFACTORY etc.

14. What are the disadvantages of the Decision tree?

Ans. a. Decision tree has a broad chance of getting overfitted. This generally happens when the dataset is huge, has many features or when we try to fit more data into it. An overfitted model will give wrong predictions for unseen data.

b. It tends to become more complex when we add more data to it. Upon adding new data points, the nodes get calculated again and the tree is restructured. This reduces the efficiency of the model. Hence, for time-series analysis, the Decision tree is not used. Random forest is used instead.

c. Decision tree is vulnerable to noisy datasets. It tries to fit all the data including the noisy data, and later tends to give wrong predictions.

15. Explain Information Gain in the Decision tree algorithm with its mathematical formula.

Ans. Information gain tells us how good a split between a parent node and its children nodes is. The higher the Information gain, the better is the split. High entropy indicates that data is uniform and low entropy means data is more scattered or distributed. Hence, with decreasing entropy, Information gain increases. Information gain is mathematically represented as:

Gain in decision tree algorithm | Interview Questions

Here, Eparent is the entropy of the parent node, and Echildren is the entropy of the child nodes.

Tips for Interviews

Some tips to bear in mind before attending/during data science interviews-

  • Always remember to explain your answers with a pen and paper using graphs/formulas as proof of concepts in an interview. It increases your chance of getting selected as data scientists are much preferred to be extremely detailed with whatever they do! Hence, such a storytelling habit must reflect in your interviews.
  • Avoid the use of abbreviations like ML/DL/NLP/CV if you are sitting for an entry-level role interview. Be as much thorough as you can.
  • Try to not put skills or frameworks that you have used barely 1-2 times and only put those you extensively work with.
  • Try to be honest and humble while answering questions. Learn to humbly address that you don’t know the answer to a particular question asked rather than covering it up with irrelevant information. Most of the time candidates get rejected for such arrogant behaviour.
  • Be friendly yet professional. Have confidence and don’t show up with a “stressed” and “anxious” face.

Conclusion on Interview Questions 

Hope this article will help you get a quick and efficient revision before your gruelling data science interviews.

Key takeaways from this article are-

  • You learned about very important concepts like bias-variance tradeoff, classification, regression etc.
  • The pros and cons of one of the most used classifiers Naive Bayes.
  • Important concepts like Entropy and Gini Impurity in Decision trees.
  • Very crucial mathematical and statistical concepts like covariance and correlation, vectorization, and distance metrics.
  • Overfitting Vs Under-fitting which is a MUST to know concept for any machine learning enthusiast.

Cheers and all the best! Hope you ace your data science interviews!

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Subhadeep Mandal
Subhadeep Mandal

A Machine Learning and Deep Learning practitioner with a background in Computer Science Engineering. My work interests include Machine Learning, Deep Learning, Computer Vision and NLP, with expertise in Generative AI and Retrieval Augmented Generation.

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