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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

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

Tavish Srivastava
Tavish Srivastava Last Updated : 26 Feb, 2019
6 min read

Introduction

I’m blessed that my memories last for long. 1st week of December 2011 was a critical week for me. This was the first time when I had to appear for job interviews at IIT Madras. I trusted myself completely. Yet, I was unsure of my performance in interviews with analytics companies.

I had great difficulty convincing recruiters that I’m an ideal person for the job. They considered me unfit for analytics jobs. Actually, the trouble with me was, I did Mechanical Engineering. And, later I discovered my love for business analytics. I’m sure there would be many other like me finding themselves stuck in similar situations.

The wheel of time has brought me to the other side of the table today. I now have experience of both the souls, a recruiter and a candidate. Once again, December is approaching fast. I can sense the anxiety and trepidation of candidate who would be appearing for campus interviews this season.

I will help you now!

Having interviewed many candidates, I’ve analyzed the essential patterns for cracking campus interviews. In fact, these patterns can help you to clear any type of analytics interview.

In this article, I’ve shared these insights along with some useful interview tips. I’d recommend you to follow these tips while preparing for job interviews. I’ve seen lots of candidates taking these tips for granted, and ending up disappointed.

You should also check out the ‘Ace Data Science Interviews‘ course. It is the ultimate course, with plenty of videos and resources, to help you land your first data science role. Give yourself the gift of cracking data science interviews!

tips for interviews analytics data science

 

Let’s start with breaking few Myths

  1. Will a low academic score impact my chances of selection in non-core companies : Of course it would. Academic performance is the only indicator of your excellent performance at a given task in past. The extent to which it can impact your selection might be slightly less compared to core companies. But, try to keep your academic record flawless. These are questions which you can kill even before facing them. In case, you’ve already completed your education, make sure you have it covered with useful tools which can increase your credibility.
  2. I can not show any interest in non-core fields before getting a job in such company : You are wrong my friend. With free online courses spreading across the world, you can always take up courses on Coursera , EdX etc. You also have platforms to compete like Kaggle and Analytics Vidhya. These are strong indicators of your interest in the field.
  3. I only expect puzzles to be asked in such non-core interviews : Puzzles are a main component of these interviews, however they do not give a complete picture. You need to find things in your resume which can catch interviewer’s interest. Here are a few interesting puzzles you can test yourself on : Puzzle 1 and Puzzle 2.
  4. These interviews are generally interviewer driven : You don’t know that! If interviewer fails to find something to talk to you about, you have a very slim chance to get through. Make sure you give enough talking reasons to the interviewer.

 

Here are few good things to do thing before interviews

  1. Make a 1 page resume : Never apply in non-core companies with longer than one page resume. These companies are interested in whether you can concisely present your complete candidature in an impressive way.  This is one of the very few things they can test you on.  Here is the best way to prepare CV for analytics roles.resume
  2. Practice few frequently asked questions : The first question in most interview is ” Tell us about yourself” or “walk me through your resume”. You’ll have to nail such questions. Make sure you have an effective 2-3 minutes answer prepared. Don’t get into too much details but make sure you give him enough pick up points.FAQ
  3. Do a complete research on the company: This shows your admiration, passion to join the company. You will need to show your research in your resume and in your interview. This probably can be one of your strongest point to gather recruiter’s attention. I will always prefer a person high on company research and medium on all other skills against otherwise.
  4. Get your questions to be asked ready : In general the last question is “Do you have any question for us”. Note that, the interview does not end until unless you leave the room. So you need to kill this question as well. Show your research here. Talk about something new which the company has got into. Try understanding the role better. Try touching up on a few popular debates in the field.
  5. Never volunteer to ask for compensation until  the interviewer wants you to : In general, compensation is fixed for all chosen candidates when companies come for campus selection. Do not look excessively money minded person and someone who can leave them for a minimal hike in compensation offered.
  6. Try to get your name first on the list of interviews : Not sure if you have control on this factor. However, you should try to get your interview done as early as possible. With time, the expectation of a good candidate rises and hence its always better to get finished with your interview as soon as possible.
  7. Show your fit to company culture : Some companies are very particular about the culture fit into their community. In such companies, make sure you show your humorous side/ sports engagement/ community engagement.
  8. Prepare well for guess estimate questions: Recruiters are keen to explore the aspect of structured thinking in candidate. They don’t look for any correct or incorrect answer. This technique of evaluation helps recruiters to filter candidates on the basis of quick decision making, comfort with elementary maths and analytics skills. Get some tips to crack guess estimates – Useful Tips.

 

What are interviewers primarily looking for in candidates

  1. Good communication skills : I believe this is the most important skill which interviewers use to distinguish the best from the rest. Make sure you are confident and clear in what you speak. No blabbering. Do not use jargons which you don’t know. Try to be simple and fluent in your vocabulary.2-pp-healthcare-communication-skills-11-638
  2. Interest in the company : Interviewers want candidates to come well researched. There might not be a direct question about this but interviewer always gets a sense of how much you know about the company.
  3. Good Analytical skills : Practice lot of puzzles! I know that puzzles are probably not the best way to choose candidates but you have to accept the fact that they are heavily used in elimination rounds. Make sure you practice variety of puzzles. Even if you are unable to solve a puzzle at interview, it is very important to show that your approach was structured. That’s all recruiters look for!
  4. Relevant Resume : Make your resume specific to the company you are applying for. Do not use the same resume everywhere. For instance, in an analytics company, you can mention a few big data/ machine learning / operation research experience ( internship or full time). Try bringing up quantifiable numbers. Do not use technical jargons and make your resume difficult for recruiter to comprehend.

 

End Notes

Last but not least, dress well for your interview. There is a lot you cannot control in this recruitment process, but there is a lot which you probably can do very well. The list is not exhaustive but some very basic pointers which can help you prepare well. I will urge other recruiters to add what they look for in a candidate in interviews. Here is another awesome resource to prepare yourself.

Best of luck to all candidates!

Did you find this article helpful? Please share your opinions / thoughts in the comments section below.

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Analyticsanalytics interviewscampus interviewsdata sciencedata science interviewsData Scientist Interviewinterviews
Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

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Tejkumar naik
Tejkumar naik

I have completed B E in mechanical engineering and I want to do SAS (statistical analysis system) course. whether I can do that course. whether it is helpfull to me in future? ?

123

palvi
palvi

Brilliant tips! I appreciate it will be very useful for freshers. I really enjoyed this post. Thanks for sharing.

123

Top engineering college in noida
Top engineering college in noida

Thank for this valuable information.

123

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