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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

Data Science Interview Series: Part-1

K
Kavish Last Updated : 12 Jul, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Data science interviews consist of questions from statistics and probability, Linear Algebra, Vector, Calculus, Machine Learning/Deep learning mathematics, Python, OOPs concepts, and Numpy/Tensor operations. Apart from these, an interviewer asks you about your projects and their objective. In short, interviewers focus on basic concepts and projects.

This article is part 1 of the data science interview series and will cover some basic data science interview questions. We will discuss the interview questions with their answers:

What is OLS? Why, and Where do we use it?

OLS (or Ordinary Least Square) is a linear regression technique that helps estimate the unknown parameters that can influence the output. This method relies on minimizing the loss function. The loss function is the sum of squares of residuals between the actual and predicted values. The residual is the difference between the target values and forecasted values. The error or residual is:

Minimize ∑(yi – ŷi)^2

Where ŷi is the predicted value, and yi is the actual value.

We use OLS when we have more than one input. This approach treats the data as a matrix and estimates the optimal coefficients using linear algebra operations.

What is Regularization? Where do we use it?

Regularization is a technique that reduces the overfitting of the trained model. This technique gets used where the model is overfitting the data.

Overfitting occurs when the model performs well with the training set but not with the test set. The model gives minimal error with the training set, but the error is high with the test set.

Hence, the regularization technique penalizes the loss function to acquire the perfect fit model.

What is the Difference between L1 AND L2 Regularization?

L1 Regularization is also known as Lasso(Least Absolute Shrinkage and Selection Operator) Regression. This method penalizes the loss function by adding the absolute value of coefficient magnitude as a penalty term.

Lasso works well when we have a lot of features. This technique works well for model selection since it reduces the features by shrinking the coefficients to zero for less significant variables.

Thus it removes some features that have less importance and selects some significant features.

Data Science Interview

L2 Regularization( or Ridge Regression) penalizes the model as the complexity of the model increases. The regularization parameter (lambda) penalizes all the parameters except intercept so that the model generalizes the data and does not overfit.

Ridge regression adds the squared magnitude of the coefficient as a penalty term to the loss function. When the lambda value is zero, it becomes analogous to OLS. While lambda is very large, the penalty will be too much and lead to under-fitting.

Moreover, Ridge regression pushes the coefficients towards smaller values while maintaining non-zero weights and a non-sparse solution. Since the square term in the loss function blows up the outliers residues that make the L2 sensitive to outliers, the penalty term endeavors to rectify it by penalizing the weights.

Ridge regression performs better when all the input features influence the output with weights roughly equal in size. Besides, Ridge regression can also learn complex data patterns.

What is R Square?

R Square is a statistical measure that shows the closeness of the data points to the fitted regression line. It calculates the percentage of the predicted variable variation calculated by a linear model.

Data Science Interview

The value of R-Square lies between 0% and 100%, where 0 means the model can not explain the variation of the predicted values around its mean. Besides, 100% indicates that the model can explain the whole variability of the output data around its mean.

In short, the higher the R-Square value, the better the model fits the data.

Adjusted R-Squared

The R-square measure has some drawbacks that we will address here too.

The problem is if we add junk independent variables or significant independent variables, or impactful independent variables to our model, the R-Squared value will always increase. It will never decrease with a newly independent variable addition, whether it could be an impactful, non-impactful, or insignificant variable. Hence we need another way to measure equivalent RSquare, which penalizes our model with any junk independent variable.

So, we calculate the Adjusted R-Square with a better adjustment in the formula of generic R-square.

Adjusted R square

What is Mean Square Error?

Mean square error tells us the closeness of the regression line to a set of data points. It calculates the distances from data points to the regression line and squares those distances. These distances are the errors of the model for predicted and actual values.

Data Science Interview | Mean Square error

The line equation is given as y = MX+C

M is the slope, and C is the intercept coefficient. The objective is to find the values for M and C to best fit the data and minimize the error.

Data Science Interview

Why Support Vector Regression? Difference between SVR and a simple regression model?

The objective of the simple regression model is to minimize the error rate while SVR tries to fit the error into a certain threshold.

Main Concepts:

  • Boundary
  • Kernel
  • Support Vector
  • Hyper-plane

The best fit line is the line that has a maximum number of points on it. The SVR attempts to calculate a decision boundary at the distance of ‘e’ from the base hyper-plane such that data points are nearest to that hyper-plane and support vectors are within that boundary line.

 

Conclusion

We have covered a few basic data science interview questions around Linear regression. You may encounter any of these questions in an interview for an entry-level job. Some key takeaways from this article are as follows:

  • The ordinary least squares technique estimates the unknown coefficients and relies on minimizing the residue.
  • L1 and L2 Regularization penalizes the loss function with absolute value and square of the value of the coefficient, respectively.
  • The R-square value indicates the variation of response around its mean.
  • R-square has some drawbacks, and to overcome these drawbacks, we use adjusted R-Square.
  • Mean square error calculates the distance between points on the regression line to the data points.
  • SVR fits the error within a certain threshold instead of minimizing it.

However, some interviewers might dive deeper into any of the questions. If you want to dive deep into the mathematics of any of these concepts, feel free to comment or reach me here. I will try to explain that in any of the further data science interview question articles.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

K
Kavish

I am a freelance Data Science professional with immense passion to solve Data driven problem. I have almost one year of industry experience. I love to write, explore new use cases of data science, and travel. I am open to a collaboration to learn, teach and grow more.

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