Interview Questions to prepare for Analytics Interview

1. This document has been created to provide you some guidance for quick preparation. The questions listed here are not exhaustive by any
means and are not unique/ specific to the mentioned companies. Also, companies are also indicative (not at all exhaustive) – they are used
to provide you with better context for preparation. Use your own judgment to prepare exhaustively.
Do not circulate further – we have worked very hard to create this!
Quick Tips toprepare for Analytics
Likely Companies where this role exists:
• UHG Group
• Decimal Point Analytics
• Deloitte
• Flipkart
• MU Sigma
• Latent View
Questions about industry/sector:
These questions are important to prepare as a business graduate. Recruiters expect you to have holistic
view of the problem/role.
• How does the US Healthcare Insurance industry spanned out - # of top players and their market
shares
• How are UHG’s competitors spanned out in all areas of healthcare insurance – Payer , Provider
• How does UHG integrate to Pharma industry – Which are the major pharma players in US it works
with?
• What was Covid’s impact on overall US healthcare insurance industry – percentage increase in cost
burden in healthcare insurance / Govt due to Covid.
• Understanding of the Industry and their offerings
• Leaders in the markets and how this company is placed
• Who is the primary competitors? Who is the real threat?
• What is the real threat in the industry? Is their any demand supply gap?
• How the industry is changing themselves over time to adjust with varied demand from clients
• As captive unit head, will you build your own team or rely on external consultant like Decimal
Points
• What is SMAC technology framework? Would the convergence of SMAC technology shape the
businesses of the future?
• How is the analytical industry in India currently spanned? Is it increasingly moving towards service
orientation with large global giants setting up shared services centers in India?
• Which industry vertical makes the most leverage of analytics practice, and explain a few use-cases
wherein analytics can be leveraged?
• How can analytics help the government sector in different aspects such as use of AI in defense,
helping banks track money laundering etc.
• Industry Structure/ Key Trends / Any Disruptions (Global vs. Local)
• Understand industry best practices for your core functional area /BU
• What are your interests in Next Gen Technologies?
• Application of DevOps to ensure smooth execution of E2E processes
• Understanding of cloud architecture to build cloud-based products

2. This document has been created to provide you some guidance for quick preparation. The questions listed here are not exhaustive by any
means and are not unique/ specific to the mentioned companies. Also, companies are also indicative (not at all exhaustive) – they are used
to provide you with better context for preparation. Use your own judgment to prepare exhaustively.
Do not circulate further – we have worked very hard to create this!
• How would you design tools like Customer Experience and success, Relationship Management,
Contract Management
• Case Studies, guess estimates and puzzles
o Identify bad customers at the time of loan origination?
o Identify target customers for a specific campaign for Credit cards?
o No. of cars in India, No. of ATMs in a state, etc.
• How would you go about designing a fraud detection model for an e-commerce client?
• How would you solve for higher NPAs for a Banking Client?
• How would you go about creating a routing optimization model for a logistics company
• How would you design a recommendation engine for Netflix?
• How would you measure the performance of newsfeed on Facebook?
• What are some of the practical application of regression analysis in Finance sector?
• What the latest development that you are aware of in the field of machine learning?
• How was the GMVs of last Big Billion Days vis-à-vis Amazon?
• Govt’s e-commerce policy? Is it an unnecessary fear?
• Amazon vs Narayana Murthy’s Catamaran Ventures deal called off – A blessing in disguise for
Flipkart?
• Flipkart “Quick” hyperlocal – Is it too late vs Amazon Prime Now & Jio Mart?
• CCI probe against Amazon, Flipkart – what’s in it for Indian consumers?
• How do you view e-commerce industry evolving in future? Will it be a winner takes all industry or
will there be room for niche players?
• What are regulatory challenges that Flipkart faces today?
• What are some adjacent businesses Flipkart can enter into?
• How does “data visualization” specifically fit into overall big data analytics industry framework?
• How have “data mining” and “machine learning” approaches delivered value for organizations in
today’s world?
• What are key “data analytics” value levers which are important to business strategy of a company?
• Who are the key competitors of Mu Sigma?
• What are the emerging trends with respect to data visualization?
• How are companies evolving their data driven digitally led strategy?
• How does the US Healthcare Insurance industry spanned out - # of top players and their market
• What is the future of AI in India? Can India become the next AI super-power?
• How is the analytical industry in India currently spanned? Is it increasingly moving towards service
orientation with large global giants setting up shared services centers in India?
• Which industry vertical makes the most leverage of analytics practice, and explain a few use-cases
wherein analytics can be leveraged?
• How can analytics help the government sector in different aspects such as use of AI in defense,
helping banks track money laundering etc.
•
Questions about Role/Profile (Technical or Functional Questions):
These are direct role-based questions which one should be thoroughly prepared on:
Technical Questions

3. This document has been created to provide you some guidance for quick preparation. The questions listed here are not exhaustive by any
means and are not unique/ specific to the mentioned companies. Also, companies are also indicative (not at all exhaustive) – they are used
to provide you with better context for preparation. Use your own judgment to prepare exhaustively.
Do not circulate further – we have worked very hard to create this!
• What are the typical Exploratory data Analysis (EDA) techniques that can be used to assess data
and its integrity?
• Name few usual challenges faced while data cleaning -Missing values, Outliers, Not a Number.
• Name couple of methods for Missing values imputation, Outlier detection & removal and NaN
treatment
• Name few data aggregation functions in R, Python and SQL
• What are different types of joins in SQL – difference between Inner join, outer join, left inner join,
right inner join, left outer join, right outer join
• Name the SQL packages available in R and Python
• What is the difference between Supervised and unsupervised Learning? What is linear regression?
What is difference between R-squared and Adj R squared? What does p-value signify?
• Why is data wrangling process critical for any data understanding/visualization process?
• Statistical concepts and it’s practical implications including Type I/II error, ANOVA, Binomial
Poisson distribution etc
• Basic concepts on classification algorithm, Regression, clustering, NLP, text analytics etc
• Diff between supervised and unsupervised learning
o Model building processes
o Hyperparameter tuning
o Overfit and underfit
o Accuracy (R2, Adj R2, AUC, Confusion Matrix etc.)
o Identifying the champion vs. challenger model
o Implementation etc
o Azure/AWS/Google Cloud concepts on architecture, storage etc
• What are the various assumptions in linear regression? What’s the difference between R square
and adjusted R square?
• How to measure goodness of fit? Please be aware of the entire regression output.
• What are the difference between Python and R? Which one you prefer?
• Explain the ETL cycle, and talk about one technique or tool which can be used in each of the three
processes?
• Which visualization tool do you prefer and why? Please compare the capabilities and limitations
of Tableau, PowerBI, Qlikview, and Microstrategy?
• Inferential Statistics
• Normal Distribution (+/- 1, 2, Standard deviations)
• Bessel’s Correction
• Central Limit Theorem – Standard error of the mean
• Common Probability Distributions: Normal, Binomial, Bernoulli, Uniform, Poisson, Exponential
• Exploratory Data Analysis: Univariate and Bivariate analysis, Box Plots, Histograms vs. Bar Graphs,
Percentiles, etc.
• Outlier Treatments frameworks
• Missing Value Imputations: Mean, Mode, Median
• Variable reduction and variable selection processes
• Principal Component Analysis
• Linear Discriminant Analysis
• Machine Learning and AI Algorithms

4. This document has been created to provide you some guidance for quick preparation. The questions listed here are not exhaustive by any
means and are not unique/ specific to the mentioned companies. Also, companies are also indicative (not at all exhaustive) – they are used
to provide you with better context for preparation. Use your own judgment to prepare exhaustively.
Do not circulate further – we have worked very hard to create this!
• Types of Regression and Classification Algorithms
• Primary metric of Optimization
• Regularization Techniques: L1 and L2
• Overfitting and Bias
• Accuracy vs. Precision
• Bagging vs. Boosting Algorithms
• Bootstrap aggregation
• Define Confusion Matrix metrics - Sensitivity (Recall), Specificity, Type I & II errors, Precision, F1
Score
• Interpret ROC Curve – What is on X and Y axis
• Which error is more serious – Type I and Type II? Explain with real life scenarios
• What are the assumptions of Linear Regression Model?
• Define Correlation, covariance, multi-collinearity, VIF, hetroskedasticity,
• Name and explain a statistical test that can be used for hypothesis testing of means of samples
• What are the general steps involved during a predictive model building?
• What are the different kinds of joins you can perform between two datasets? Explain.
• What is meant by parametric tests? How are they different from non-parametric tests?
• What are the pros and cons of random forest model over a regression model and when do you
prefer which one?
• How is the correlation coefficient different from prediction power score? Which scenarios do you
use which one?
• Flipkart Pay Later – how will Analytics help in this?
• Why is data wrangling process critical for any data understanding / visualization process?
• Statistical concepts and it’s practical implications including Type I/II error, ANOVA, Binomial
Poisson distribution etc
• Basic concepts on classification algorithm, Regression, clustering, NLP, text analytics etc
• Azure/AWS/Google Cloud concepts on architecture, storage etc
• Which visualization tool do you prefer? Why? Pros n cons of Tableau, PowerBI, Qlikview?
• Ability of derive KPIs / success matrix out of any business process e.g. what all KPIs to drive for a
program manager of Flipkart Plus
• What are the key metrics you will use to monitor customer services organization?
• Create a sales funnel to analyze MoM trends in Flipkart’s gross merchandise sales
• Imagine Flipkart finds that its sales through ‘top offers’ link have gone down 8% WoW. How will
you root cause this drop?
• How will you estimate incremental GMS impact of a holiday sales event run by Flipkart?
• How do you expect incomes of all employees in an office to be distributed?
• Would this team also need to work with external vendors and how?
• What are the key components to develop a data visualization dashboard?
• Which key performance metrics would need to be measured as a part of this dashboard?
• Would you need separate models/dashboards for data visualization, data mining and machine
learning initiatives?
o How would you marry these dashboards from a data point of view?
o What are some of the strategic insights and key recommendations that can be delivered
through this dashboard?

5. This document has been created to provide you some guidance for quick preparation. The questions listed here are not exhaustive by any
means and are not unique/ specific to the mentioned companies. Also, companies are also indicative (not at all exhaustive) – they are used
to provide you with better context for preparation. Use your own judgment to prepare exhaustively.
Do not circulate further – we have worked very hard to create this!
o What kind of “Change & Engage” model would need to be deployed?
• How would you measure performance through data analytics modelling?
• What is your understanding of key business processes and data structures?
• Can you share few of the best practices which need to be followed to enable a successful data
driven strategy?
• How would you ensure strong collaboration with other partner teams (such as sales) through a
business-oriented walk through of the dashboard?
• Do you have expertise in any data analytics/management tools?
• How would you use these tools to churn out actionable recommendations for the business?
• In your opinion, what is the significance of having a data management strategy for a company?
• How would you track the success of your data visualization work both from a technical and
strategic perspective?
• How can analytics be used in operational effectiveness? – You need to take reference to six sigma
concepts to answer this question
• Which visualization tool do you prefer and why? Please compare the capabilities and limitations
of Tableau, PowerBI, Qlikview, and Microstrategy?
Case Questions
• You are given 100 reviews of a movie, and you are to conclude whether the movie was a hit or a
flop. What would be your approach (explain using Python as a reference tool)?
• You are required to forecast SENSEX and NIFTY levels for the year 2030 for an investment
consultant firm? What would be your approach?
• Case question – there are two processes X (with standard deviation of 10) and Y (with standard
deviation of 100). Which process would you recommend for your organization?
• Case question: There is often a tussle between marketing and sales. Which model would you use
to showcase the effectiveness of marketing variables on sales and market share?
Things to highlight from CV/Profile
This helps make your CV more relevant to the role, helping with shortlists
• Any academic projects worked in area of EDA, machine learning, predictive and prescriptive
analytics
• Any Pharma industry / healthcare industry experience
• Good command over at least one statistical language – R/ Python
• Comfort level with Statistics
• Concepts on SQL (Joins & it’s implementation), Tableau (data ingestion/procession)
• Basics on Python – Data types, while/for loop, Numpy/Pandas packages, Pyplot, visualization etc
• Hands-on implementation on Machine learning algorithms
• Exp with frameworks like PySpark
• Familiarity with Deep Learning algorithms using Keras, PyTorch, Tensor flow
• Please highlight any live projects you were involved in hands-on
• Highlight any statistical modelling exercise you were involved in (end to end)

6. This document has been created to provide you some guidance for quick preparation. The questions listed here are not exhaustive by any
means and are not unique/ specific to the mentioned companies. Also, companies are also indicative (not at all exhaustive) – they are used
to provide you with better context for preparation. Use your own judgment to prepare exhaustively.
Do not circulate further – we have worked very hard to create this!
• Vendor certification should be highlighted such as Microsoft certified Data Analyst, Azure
certifications, Green belt certifications etc.
• Please draft your professional experience, projects etc. in the form of business problem
recommendation solution impact of the solution
• Avoid too many technical jargons or low-level details in resume.
• Technical terminology
• Usage of metrics – NorthStar, success metrics, guardrail metrics,
• Impact glorification
• Courses and Certifications
• Business Intelligence work
• Posters / Papers / Publications
• Competitions participated and won
• Prepare your analytics specific projects and how your internships/work-ex can display your
statistical skills (like data analysis, dashboard design, visualization)
o Where did you get the data from
o How did you clean it
o Why did you use a given technique and not others
o How did you validate your results
• Can you give me examples of instances where you had to interact with a client/stakeholder directly
and present your work?
• How would you have done this project differently given another chance at it?
• Can you give examples of project where you had to work as a team and collaborate with other
peers to achieve the given objective
• Prepare what kind of impact did certain projects on your CV create
• Comfort level with Statistics
• Concepts on SQL (Joins & it’s implementation), Tableau (data ingestion/procession)
• Basics on Python – Data types, while/for loop, Numpy/Pandas packages, Pyplot, visualization etc
• Hands-on implementation on Machine learning algorithms
• Exp with frameworks like PySpark
• Familiarity with Deep Learning algorithms using Keras, PyTorch, Tensor flow
• Certification on SAS, AWS, Data Analysis, Python, R, SQL, Tableau/PowerBI etc
• Lot of emphasis on designing and implementing key metrics. Study what are the key metrics used
for some business areas relevant to the company (e.g. customer services, customer churn, sales
etc.). Be clear about metrics and dimensions
• Hands on data pulling and data manipulation skills expected. Highlight any skills or past experience
related to SQL, Python, Excel modelling in the CV
• Understanding of Machine Learning also expected. Highlight any analytical projects in CV. Make
sure you know in and out of the methodology used in such projects. Also prep on key ML
techniques (regression, clustering, decision trees etc.)
• Technical proficiency in the field of data analytics
• Business focused insights from a data point of view
• Key stakeholder management skills (internal/external)
• Any academic projects worked in area of EDA, machine learning, predictive and prescriptive
analytics

7. This document has been created to provide you some guidance for quick preparation. The questions listed here are not exhaustive by any
means and are not unique/ specific to the mentioned companies. Also, companies are also indicative (not at all exhaustive) – they are used
to provide you with better context for preparation. Use your own judgment to prepare exhaustively.
Do not circulate further – we have worked very hard to create this!
• Good command over at least one statistical language – R/ Python