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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

10 Important Questions for Cracking a Data Science Interview

Yamini
Yamini Last Updated : 08 Nov, 2022
5 min read

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

Introduction

You may be asked questions on various topics in a data science interview. These include statistics, machine learning, probability, data visualization, data analysis, and behavioral questions. Besides these, your coding abilities are also tested by asking you to solve a problem, and you might be asked to solve a data science case study. While the skills required for coding and solving case studies can be acquired by practicing, theoretical concepts can be clarified by keeping your foundations strong.

Data Science Interview

Data Science interviews are conducted for various roles like Data Scientist, Data Analyst, Machine Learning Engineer, Data Visualization Engineer, etc. Though the roles are different, the core skills required to crack an interview for any of these roles are the same.

In this article, we are going to look at a few important questions for a data science interview.

Data Science Interview Questions

1. Describe the workflow of a data science case study.

The following steps are involved while solving a data science case study.

  • Problem description and understanding
  • Data collection
  • Exploratory data analysis (involves data exploration,
    feature engineering, outlier detection and treatment, and, handling missing
    data).
  • Model building
  • Model evaluation
  • Model deployment
Data Science Interview
Source: Author

2. What are the assumptions of linear regression?

The assumptions of Linear Regression are:

  • The data should have a linear relationship.
  • There should be multi-variate normality.
  • No or little multi-collinearity.

3. What are outliers and how do they affect the end result?

Data points that differ significantly from other observations are known as outliers in a data set. Outliers are the data points that differ from the rest of the data and do not follow the common data pattern. An outlier can reduce a model’s accuracy and efficiency, depending on its reason. They can cause adverse data analysis problems and manipulate the entire process, thereby causing us to end up with a biased result. Therefore, it is important to detect any outliers present in the data and treat them.

Data Outliers can be identified by visualizing the data set using scatter plots, box plots, etc.,.

4. What is the p-value in hypothesis testing?

In hypothesis testing, p-value refers to the likelihood of a data point occurring with random choice. If a p-value is 0.5 and is less than alpha, we can conclude that there is a probability of 5% that the experiment results occurred by chance, or you can say, 5% of the time, we can observe these results by chance.

5. What is normal distribution?

A normal distribution is a probability function that describes how the value of a variable is distributed over all the possible values.

Data distribution can be identified by plotting the values using histograms or scatter plots. If the histogram representing the distribution has a central peak, it is normally distributed. The mean and standard deviation are two main measures that help identify the data’s normal distribution.

normal distribution

Source:https://www.scribbr.com/statistics/standard-normal-distribution/

In the above picture, the red line represents a normal distribution.

6. How to calculate the standard deviation of data in Python?

A standard deviation measures the deviation of data points from the central point of the data. It represents how far a particular data point is from the main data distribution. Since it measures the deviation of the data concerning the mean, it shows the spread of data around the mean.

It is calculated by using the Numpy method ‘std’.

Example:

a = np.array([[1, 2], [3, 4]])
np.std(a)
>>> 1.1180339887498949

Using the np.std method, we can also calculate the standard deviation across a specific axis also. The stdev() method present in the statistics module of Python can also be used.

7. What are the different kinds of plots in data visualization?

Plots can be divided into the following categories based on their purpose:

  • Correlation plots: they give us information about how one data point changes with respect to another. Scatter plots, count plots, marginal boxplots, heatmaps, and pairwise plots are some examples of correlation plots.
  • Deviation plots: Such plots help to identify the amount of variation in the data. Examples of deviation plots include diverging plots and diverging dot plots.
  • Ranking plots: Sometimes, we may need to know the ranking of the data, like maximum, minimum, mode, etc. In such situations, ranking plots such as ordered bar charts or dot plots come in handy.
  • Distribution plots: Distribution plots help in knowing the distribution of the data. Histograms for continuous and discrete data, density plots, and box plots are some examples of distribution plots.
  • Composition plots: These graphs tell us the composition of the data. They help in understanding how much of the entire data set a certain data point constitutes.
  • Change plots: They are used to identify the changes within the data variable. Time series plots are a prominent example of change plots.
Data Science Interview
Source: Author

 

8. What is bias and variance? How do they affect the model’s performance? What is the bias-variance tradeoff?

Bias refers to the difference between the actual and predicted values. High bias causes underfitting problems; hence low bias is preferred.

Variance can be defined as the model’s response to the fluctuations in the data. High variance causes over-fitting issues and hence is undesirable.

Both bias and variance affect the model’s ability to generalize the data when present in high values. In turn, this affects the accuracy of the model. Hence, it is important to maintain a balance between the bias and variance of a model. The correct trade-off point must be chosen to improve the model’s performance while ensuring that the model’s capability to generalize is not compromised.

9. Define regularization?

Regularization is a technique used to deal with the problem of overfitting in machine learning models. Here, we keep the number of features and reduce the feature size. It reduces the size of the coefficients towards zero, thus avoiding the overfitting problem. Regularization reduces the magnitude of the features while allowing us to maintain all the featured.

It is of two types: Ridge regression and Lasso regression.

regularization
Source: https://www.simplilearn.com/tutorials/machine-learning-tutorial/regularization-in-machine-learning

10. What role does machine learning play in data science?

Data science is a field that requires input from other disciplines, such as machine learning, deep learning, and artificial intelligence, to analyze data, build solutions, and predict outcomes. In particular, machine learning is used to build models that use currently available data to predict outcomes. In most cases, data science case study solutions are built using machine learning concepts and methods. Supervised and unsupervised learning, exploratory data analysis, feature engineering, model evaluation, hyperparameter tuning, epoch, etc., are some machine learning concepts widely used in data science.

Conclusion

Let’s recap what we learned in this article. We looked into

  1. The different steps involved in solving a data science case study
  2. Types of plots used for representing data
  3. How regularization helps reduce overfitting
  4. We learned about the impact of outliers on the end results
  5. We also have a quick glance at many Machine learning concepts like bias, variance, standard deviation, linear regression p- value, etc.,.

Here are a few important questions you must know before attending a data science interview.

Besides these, questions on projects you worked on, your work experience, solving case studies, SQL queries, coding questions, fundamental concepts of Machine learning, and Data Science are quite common. Hence, you must hold a strong grip over all the above-stated concepts and subjects to crack any data science interview.

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

Yamini
Yamini

Data science enthusiast and storyteller. Sharing my learnings and findings from the world of NLP, data science and machine learning through my articles. Let's explore the world of data together! Spreading knowledge, one post at a time.

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