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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

Crack the Data Science Interview Case study!

usha bhanu
usha bhanu Last Updated : 28 Jun, 2022
5 min read

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

Data Science Interview

Image 1

Introduction to Data Science Interview Case Study

When asked about a business case challenge at an interview for a Machine learning engineer, Data scientist, or other comparable position, it is typical to become nervous. Top firms like FAANG like to integrate business case problems in their screening process these days. This approach is followed by a few other leading companies, like Uber and Twitter. Most case studies are open-minded and technical. They are specific to the company you are interviewing for.

 

What is a business case problem in a data science interview?

In basic terms, a case study is any real-world project you may work on in your company. The type of case study question you’ll be asked is determined by the position you’re interviewing for. In most interviews, you will be given around 45 minutes to absorb the problem description and walk through your thoughts and potential solutions.

“If you cannot measure it, you cannot improve it.”

                                                                                   Peter Drucker

Getting the right metrics for any case study is important. It can be difficult due to different reasons like being a fresher, or having no knowledge of the industry.

Note: A business case problem does not have a single correct answer or a simple solution.

Mainly case studies can be categorized into three types. They are Product based case studies, prediction-based case studies (Machine learning), and business case studies.

Example of a Data Science Interview Case Study

Pretend you’re interviewing for a position on Twitter’s engagement team. Twitter does have a news feed, as we all know. It provides the content to the user based on their interests. With the assistance of a news feed ranking algorithm, it can do this task. As a part of the engagement team, you are assigned a task to evaluate the algorithm’s success.

Data Science Interview                                                                                                                                                      Image 2

Framework

When you’re given a problem, never begin by firing your techniques; instead, start by clarifying the case study. Make absolutely sure the interviewer and you are on the same page. Because the questions tend to be confusing and indefinite! Asking questions will help you get answers or extra information. Show you are curious about it. As the case studies are open-minded avoid words like “correct approach is/ would be” because there might be multiple solutions to that problem statement.

Begin with a phrase like “Before going into the problem, I’d like to double-check that I understood the problem here…….”. Keeping track of your approach will be much easier if you take notes. It’s absolutely OK to pause for a while to consider and plan before proceeding with the solutions.

Then, to demonstrate that you understood the problem, concentrate on what actually is the problem; for example, in our study, the problem is focused on Twitter’s news feed and user engagement. As a result, the algorithm’s success is evaluated.

The first step is to identify relevant indicators to assess, such as shares, comments, and click-through rates (CTR). The strategy you choose to address the problem is the next step. Questions like these might be included “I’m also thinking that these measurements don’t always follow a trend. Some may be rising, while others may be falling “. In such circumstances, you may pick your metrics based on your approach’s perspective, such as whether you want to focus on business or user experience. Consider ads from a business standpoint. The CTR is a powerful statistic. Because not all users share the content, a powerful metric for user experience is comments or reactions (for example, text processing of comments).

Take a step back and ask if the interviewer has any questions for you or if you can proceed. When you’ve completed the key phases of the process, be sure to cover the following:

1. Clarifying

2. Brainstorming and

3. Strategy

4. Conclusion

Make certain that your approach to the case study is complete. Also, discuss the results of your method in regard to the problem statement.

Additional Points to keep in Consideration

1. Response should be in a structured manner.

2. Conclude the case study with end to end solution.

3. A sensible Strategy.

4. Know your target audience.

You are stuck?

Don’t panic, or say irrelevant points, instead tell your interviewer that you are stuck. Let them know What you’re thinking and why you could not proceed with those approaches. In a few cases, the interviewer might give clues to you.

Wrong point?

When you are sure that you are leading towards a solution that might not work! or lead to a dead end. Don’t hesitate to mention that this might lead to a situation. Instead, concentrate on how would you correct and continue your strategy for the problem you have encountered. In real-life projects, you are expected to identify those problems and continue with an appropriate approach. You could add how your new approach to the problem will be avoiding the problem that you were stuck with.

Not sure about the Strategy?

Let the interviewer know that you are not sure about the approach and think out loud to get on the track. It is always better than bluffing.

Practice with yourself!

Ask yourself a few case studies! Record yourself on camera to observe and correct yourself. It helps you with gaining confidence before practicing with peers. With each practice, you will get better. Making notes of all your practice studies will help to refer in the future.

A resource I found useful to practice case study: https://www.interviewquery.com/blog-data-science-case-study-interview

Conclusion

Voila! Follow these four steps for a successful case study response ( Clarify, Plan, Strategy, and Conclusion).In other words, one can memorize the approach to a case study as “CAPER”.      Where C- Clarify, A- Assume, P- Plan, E- Execute, R- Review.

Make the interview as participatory as possible. Taking notes will benefit you in structuring your response. Mention the pitfalls to let them know both pros and cons of your chosen approach. Make sure you know all there is to know about the company and its products. Practice with peers and do mock interviews. Don’t worry if your first few efforts are a mess! Take the feedback seriously. I hope you liked my article on data science interview case study. Please share your feedback in the comments section below.

Image1:https://mockinterview.co/index.php/2018/02/26/four-sample-case-studies-for-data-scientists-analytics-positions/

Image2:https://www.searchenginejournal.com/increase-google-search-visibility-twitter/367324/

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

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usha bhanu
usha bhanu

Hi, I’m Usha!

As a Data Science professional with a Master's from Indiana University and a Bachelor's in Computer Science & Engineering, I excel in transforming complex data into actionable insights. I specialize in advanced data visualization, AI solutions, and regulatory reporting, using tools like Python, R, and SQL.

I have a proven track record of enhancing productivity and reducing errors through innovative AI solutions and data analysis. My strong analytical skills are complemented by my ability to communicate complex concepts clearly and work effectively in collaborative environments.

Passionate about driving innovation and making data-driven decisions, I am eager to leverage my expertise in digital media and other dynamic industries. If you're looking for a skilled data scientist who can turn data into strategic assets, let's connect!

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Responses From Readers

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Sri
Sri

Very helpful article. I m about to have an interview like mentioned in the article and it cleared my thoughts on how to prepare for the interview. Tysm.

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