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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

Frequently Asked Data Science Interview Questions

Karpuram
Karpuram Last Updated : 30 Aug, 2022
5 min read

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

Introduction

This article will discuss some data science interview questions and their answers to help you fare well in job interviews. These are data science interview questions and are based on data science topics. Though some of the questions may sound basic, these are frequently asked in interviews. Most candidates overlook them and won’t focus on the basics, and they face rejection in job interviews. It is important to start learning the basics to nail the data science job interviews. The following data science interview questions are your guide to performing well in data science job interviews.

interview questions on data science

Source: SEEK

Frequently Asked Data Science Interview Questions

Q1: Elaborate on the differences between Data Science and Data Analytics.

Firstly data analytics is a part of data science. Multiple things come under data science, like data mining, data analytics, data visualization, and many more.

There are also differences in the programming languages used to implement. Data analytics uses only basic programming languages like python or R. To deal with databases, it uses SQL(structured query language). Data science uses advanced programming languages like java, Julia, Scala, C, C++, Java Script, Python, and R.

The job of a data analyst is to find the solution to the current problem. At the same time, the job of a data scientist is to find the solution to the present problem and predict the future by taking inputs from the past.

Q2: Explain the Confusion matrix.

A confusion matrix is used to know how well the model is performed.

confusion matrix

Source: medium

If the predicted value is positive and the actual result is also positive, then the model will perform well (True positive).

If the predicted value is positive and the actual result is negative, then the model is not performing well (False positive)- Type I error.

If the predicted value is negative and the actual result is also negative, then the model will perform well (True negative).

If the predicted value is negative and the actual result is positive, then the model is not performing well (False negative)- Type II error.

The formula knows the accuracy of the model

(True Positive + True Negative) / Total Observations
let us say true positive observations are 5 and true negative observations are 4, out of total observations 10, then the accuracy of the model is (5+4)/10=9/10=0.9

It is 90% accurate.

Q3:Differentiate between the terms error and residual.

The difference between the observed value and the theoretical value gives the error. The difference between the observed and predicted values gives the residual value.

Error = Observed value - Theoretical value

Residual = Observed value - Predicted value

Q4:What are the precautions taken to avoid overfitting our model?

Suppose the model is performing well on the data sets we are using for our training and testing and not on some other data set, then such a model is said to be overfitting.

avoiding overfitting model

Source: kdnuggets

Precautions-

  • Can be avoided by having our model as simple as possible
  • Using cross-validation techniques
  • Using regularization techniques
  • Using feature engineering

Q5: Differentiate between data science and traditional application programming.

In traditional application programming, we need to analyze the input first. To get the expected output, certain code needs to be written on our own, which could be challenging as it is a manual process.

However, in Data Science, the process is entirely different. We need to have data first and divide it into two sets. One is called the testing data, and the other set is called the training data. With the help of training data and data science algorithms, rules are created to map an input to an output. These rules are tested using the testing data set. If the rule succeeds, it is said to be the model.

Q6:Explain bias in Data Science.

Categorically, a model is based on the ability of an algorithm to understand, design, etc. If the algorithm is less effective and cannot conclude from the given data, it leads to a less efficient model, leading to an error. This error is also called bias in data science. Usually, bias occurs when simple algorithms like linear and logistic regression are deployed.

Q7: What do you know about dimensionality reduction?

Some Datasets would have more fields than required. Even after removing some fields, the functionality would remain the same. The process of reducing such fields or dimensions while taking care of the functionality is known as dimensionality reduction.

Q8: Mention the popular libraries used in Data Science.

Some of the popular libraries used in data science are:

  • TensorFlow
  • SciPy
  • Pandas
  • Matplotlib
  • PyTorch

Q9: Explain the working of a recommendation system.

A recommendation system filters all the videos or movies based on our preferences and watches history. It offers recommendations to the users based on the above two inputs and behaviour models.

A recommendation system is either a program or an algorithm based on watch and search history inputs. It analyses the genre, cast, director, and more to recommend movies to the viewers. That is how the recommendation system works for a product-selling platform like Amazon, Myntra, or Flipkart, or an OTT platform like Netflix, Amazon prime Video, Aha, and so on.

Generally, there are three types of recommendation systems.

1. Demographic filtering

2. Content-based filtering

3. Collaboration-based filtering

Demographic filtering: In this, the recommendations are the same for every user regardless of their interests. For example, let’s take the top trending movies column in OTT platforms. These are the same for every user because of the demographic filtering system.

Content-based filtering: Filterings are based on movie metadata. Metadata contains details like movies, songs, genres, cast stories, etc. Based on this data, the system recommends movies related to that data for user consumption.

Collaboration-based filtering: Here, the system will group users with similar interests and recommend movies to them.

Question 10: Explain the benefit of dimensionality reduction.

Some Datasets would have more fields than required, meaning that even after removing some fields, the functionality would remain the same. The process of reducing such fields or dimensions by taking care of functionality is known as dimensionality reduction.

After reducing fields, it requires less time to process the data and to train the model, and speed is increased compared to data with more dimensions. Also, the accuracy of the model is increased.

Conclusion

Data science is a lucrative career option with ample opportunities across diverse sectors. There is a surge in the number of companies based on Data science like machine learning and artificial intelligence and the career options they offer. This write-up helps us cover some frequently asked and basic data science interview questions and their answers.

Overall in this article, we have seen,

  • Some basics of data science and data analytics
  • Bias, Error, and Overfitting related topics.
  • Questions related to data science.
The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.
Karpuram
Karpuram

Hello Everyone,
This is Srivani. I had completed my B.Tech in the computer science department. I am interested in Data Science and programming. Thanks for reading my articles and hope you get knowledge from them.

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