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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

How freshers can ace interviews for Business Analytics roles?

Kunal Jain
Kunal Jain Last Updated : 27 Aug, 2021
6 min read

Campus interviews can be very competitive, especially so, if you want to secure a job with the best companies. Further, if you are a fresher, the experience of giving interviews can be unnerving at times. The combination of these factors can make even the best analysts falter when being top of the game matters the most.

How do you ensure that you excel in campus interviews?

Thankfully, you can train yourself to make sure that you present your best when it matters the most. The purpose of this post is to provide tips, using which you can blaze through any analytics interview:

analytics interview

[stextbox id=”section”]First and foremost, how are analytics interview different from other interviews?[/stextbox]

Interviews for analytics positions could be quite different compared to interviews for Sales or Operations. This is because the skills required for success in analytics are different from the ones in other functions. Analytics interviews for freshers are typically meant to test some (if not all) of the following skills:

  • Structured thinking
  • Attention to details
  • Numerical skills
  • Problem solving capabilities
  • Communication skills

Hence, they differ in design and assessment compared to other interviews. In order to test these skills, companies rely on case studies, strategic / analytical role play, puzzles and aptitude tests.

 

[stextbox id=”section”]Lay the ground work and train yourself[/stextbox]

The pressure to get your first job could be immense and hence it makes sense to be fully prepared for what you are likely to face. Your preparations should begin at least 3 – 4 months before your placement season starts. You need to prepare and train just like an athlete does for his / her competition.

Following are list of activities you should complete before even applying to a company:

  • Prepare your CV: Your CV is your window of opportunity to get shortlisted. While preparing a good CV is not the focus of this post, I would want to highlight 2 points for making your CV better:
    • CV should ideally be 1 pager, maximum 2.
    • Highlight the impact of your projects, internships and contributions through numbers. How much profit did the company make because of your contribution? What was the impact of your internship?

Once completed, get the CV reviewed by some of your Seniors to make it flawless.

  • Identify and prioritize your target companies: Identify the companies you would want to join before hand. Check if these companies are coming to your campus. If not, you should reach out to the right people in these companies and find out how you can apply? Once identified and prioritized, go through the website of these companies, past annual reports and recent press clippings (if any)
  • Revise MBA concepts and frameworks: If you are passing out of a B-school, this should not take any extra effort. However, if you are a non-MBA graduate, go through and understand some of the common business frameworks. Some of the famous ones are 4Ps, 3Cs and Porter 5 forces. Keep special focus on frameworks which can have numerical part (e.g. Profit = Revenues – Cost). While there is no need to memorize these frameworks, you need to apply these frameworks to solve business problems.
  • Solve Case studies: If you are a non-MBA graduate, get some of the B-school case studies and apply the frameworks mentioned above to solve these case studies. I would suggest smaller case studies (which can be completed in an hour or so) rather than elaborate ones. Also, don’t ignore the numerical part of these case studies, it is as important as strategic thinking. Spread these case studies over days rather than doing all of them in a go.
  • Form an interview group: A lot of these activities are best done in groups. You can ask your partners to take a case interview, perform a role play, pool puzzles and case interviews etc. Needless to emphasize the importance of right group here. Try and get people from diverse background but common aspirations.
  • Pool interview puzzles and solve them: Arrange as many interview puzzles as possible and then solve them with your group. Discuss them and see if you can find a shorter way. What could you have done better to reduce time?
  • Solve aptitude tests: Taking up aptitude tests which score you on logical reasoning and numerical skills is the best way to brush up your numerical skills. They not only simulate a possible round of interview, but also create an environment of pressure. Finding and taking mock CAT and GMAT exams could be a good way to do these tests.
  • Read about analytics: While this may or may not have a direct impact on any of the rounds you face, it will provide you discussion points after the interview. They can also be used to answer some of the questions like “Why do you want to enter Analytics?” or “Why does Analytics excite you?”
  • Finally, get into the routine: Your mind needs to be on top of its productivity during the interviews. If the interviews are expected in evenings or over the weekend, solve your case studies and perform role plays during same time. Try and simulate similar environment so that you are at home on the final day.
[stextbox id=”section”]On the day of interview[/stextbox]
Following are some important tips to observe for your day of interview:
  • Take good sleep the night before: On the night before, take a sound sleep. If you have not read or prepared some thing yet, there is no point of preparing it now. Your mind should be in its best productive state during interview, so a night out or late night coffee would not help.
  • Dress up comfortably: Wear the dress you are comfortable in. If the shirt is tight, change it (but keep it ready in time).
  • Reach the venue before time: There is no point in creating hassle in your mind by getting late or rushing at the last minute. Plan your time for commute, keep buffer for traffic jams and reach the venue with a relaxed mind.

 

[stextbox id=”section”]During the interview[/stextbox]

  • Enjoy the process: You should judge the interviewer as much as they are judging you. Enjoy this period of interaction as much as you can. Anticipate
  • Focus on framework & interpretation of numbers: For case studies and role play, focus on the approach and framework. The interviewer would be interested in your framework and strategic thinking. Numerical accuracy is important, but not the most important thing. Also, try and interpret the numbers thrown at you. Is the cost higher than revenues? Is the company making operation loss or profit?
  • Be honest and call out assumptions: One attribute every manager wants in his analyst is honesty and integrity. No one expects you to know solution to every problem in the world. So, if you don’t know the answer, say so. If you are making assumptions, say so. There is no point in bragging about something you don’t know.
  • Think out Loud: In evaluation of case studies and role plays, your problem solving approach has higher weightage compared to the actual answer. Hence, it is important that you always keep interviewer up to date with your thought process. As a good practice, layout the approach in front before actually proceeding with the case study (or a part of). So, even if the answer is wrong, interviewer understands that the approach was right.
  • Take notes / solve the case by laying out things neatly on paper: One of the best ways to make sure you follow a framework is to lay out things neatly on paper. For example, if the question is to see how a company can become profitable, I would say that the company has 2 options: Reduce cost or increase revenues. Then I’ll divide the paper in 2 half and list down various levers for each part accordingly. You can then discuss each lever. This not only ensures that a framework is followed, but also that you look at the holistic picture.
  • Ask questions whenever you can: If you are not clear about any thing, ask! There are times when questions are kept vague deliberately. It is to test how do you deal with ambiguous situation. Towards the end of the interview, ask questions which can help you understand the role better. What is the role? How does a typical day or week look like? What is the profile of people in team are some good questions you should ask if you don’t know the answer to.
 
Do these preparation sound over-whelming? They probably are. But, I can assure you that if you follow these in disciplined manner, you will ace through the interviews and reach your dream companies.
The more you practice these, the better you will become in clearing interviews with flying colors.
All the best
As usual, if you have any other tips, feel free to add them in comments below.

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Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

AB
AB

Thanks for this Article. It is very informative. Will u please suggest some good websites/web links from where we can practice small case studies, the kind of case studies u were referring to? It will be great help for us. Thanks :)

123

Kunal Jain
Kunal Jain

AB, Website of Capital One has one case study. There will be multiple case studies if you search for "Ace the Case". These will typically be for Management consultancies and not Analytics roles. You should look at guess-estimates in this collection. Following article might also be help for you: 1. Tips for cracking guess-estimate Also, would recommend that you subscribe to our emails as we have a few articles in pipeline on this topic. So...stay tuned! Kunal

123
Rajarshi Chakraborti
Rajarshi Chakraborti

As a fresher, I am applying to a lot of analytics startup. There are a lot of guides on the internet, which are confusing. Some say a fresher, we need stats and puzzles to be done. Some say to complete Case studies. R or SAS is also recommended, to know? If a 2014 graduate is looking for a start, which roadmap will help. Thanks much

123

Kunal Jain
Kunal Jain

I think you are mixing 2 problems - one is how to crack an interview, and the other is what is required in your job. Most of the big companies would not require a pre-knowledge of tools and would judge you using Case studies and puzzles. However, startups tend to ask a few technical questions as part of the interview as they may not have the resources to train the new hires. If you are interested in only start-ups, gaining skills on R might help as well (but do that once you have solved enough case studies.) Regards, Kunal

123
Rajarshi Chakraborti
Rajarshi Chakraborti

Hi Team, As advised, I completed the course in EDX named The Analytics EDGE. As I am applying to startups for analytics interviews, where should I prepare for them and what should I prepare, especially when I have done some course on R and analytics. What I need to revise and read further. Thanks/

123

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Tips for freshers to crack campus interviews for analytics / data science companies
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