## OYO Rooms Data Analyst INTERVIEW Questions

**Company – OYODesignation – Data Analyst**

**Year of Experience required – 0 to 4 years**

Technical expertise – SQL, Python, Case Study, and Statistics

Salary offered – 10 to 18 LPA (no Stocks, 10% variable) – 80% hike

Technical expertise – SQL, Python, Case Study, and Statistics

Salary offered – 10 to 18 LPA (no Stocks, 10% variable) – 80% hike

**Number of Rounds – 4**

There were 4 to 5 rounds in the recruitment process, the number of rounds depend on the candidate’s performance in the technical round.

**Round 1 – **Written SQL round**Round 2 – **SQL (based on the questions asked in the first round)**Round 3 –** Project-based questions and statistics (basic)**Round 4 –** Case Study**Round 5 – **Hiring Manager as well as case study

## Round 1 – Written SQL round

There were 4 SQL questions(mostly from Hacker Rank) that need to be solved in 1 hour.

Question split:-

– 2 easy

– 1 medium

– 2 hard

List of Hacker Rank questions to practice before the interview

– Interview Questions

– 15 Days of Learning SQL

Medium-Level Questions:-

– New companies

– Occupations

For easy questions, concentrate on the basics of** rank, lead, lag, and aggregate functions.**

## Round 2 – SQL Interview

This round was mostly around the written questions asked in the previous round and the approach of your solution. You need to have at least 3 correct answers to get to this round

Tips – Concentrate on communicating the approach, the questions in this round is completely on the 5 written questions, so you can revise the approach or concepts of these questions before the second round.

## Round 3 – Project Based Questions and Statistics

I had a project in Machine Learning (ARIMA forecasting) so questions were mostly around the problem that we were trying to solve and some statistics concepts:-

– What is the p-value?

– What is correlation? Give an example of a negative correlation

– Complete walk-through of the ARIMA model

– What is multicollinearity?

– Difference between regression and classification model

– What is the degree of freedom?

Questions were mostly based on the type of project that you had written in your resume and the statistics or concepts associated with it.

So, for this round do prepare your project in as much detail as possible

## Round 4 – Case Study

The technical rounds were the most important rounds. If you have performed decently in the first 3 rounds then there is a high chance of converting the role.

Case Study asked to me – **How can Netflix increase its revenue by 50% in the next couple of years?****It was a mix of guesstimates and business case studies.**

So, I started with some approx numbers and their current split.

For Example – Netflix has a total revenue of $100 Million and they are currently in 4 verticals and 10 countries. The current verticals are Hollywood, Bollywood, TV Series, and Other Country shows. The 10 countries are India, the USA, the UK, and 7 more small population countries.

Assumption – India has 60% of the total revenue and 100% of the revenue is coming from Bollywood movies.

After a set of assumptions, we had to discuss the approach, the important points that we discussed were:-

– Moving to or acquiring already performing OTT or their most-watched series

– Advertisement to screen time ratio. To either increase the advertisement length or the frequency of it in a show or movie

– Reducing the number of users that can use one subscription in parallel

– Making a provision of taking the phone numbers that would be associated with one account at the time a user is buying the subscription. This will reduce the frequency of distribution of subscription

There were discussions on each of these points, you just need to bring as many diverse points in the discussion as possible. Do comment your approach in the comment box below.

## Round 5 – Hiring Manager Round

This round was mostly around cultural fit wherein the candidate’s previous experience was checked along with the work culture he/she was working in.

But, I was asked one more question i.e. to decide the price of a micro stay in OYO rooms. SO, OYO rooms were moving to a micro stay model where you can book a room for 6-12 hours, so the question was to have a dynamic rate charter for the booking of the room.

My approach was to have a Linear Regression model to get the rate of the room. And the independent variables that I suggested were:-

– Daily price of the room

– Day of booking

– Price of the adjacent rooms

– Time of booking

– Customer Life Time Value who is booking the room

– Number of rooms and number of booked rooms for that day

– Holiday season impact

## OYO SQL Interview Questions

There were 10+ SQL questions, 6-7 easy/theoretical , a couple of medium problem and 1 hard problem.**The hard problem was picked directly from Hacker Rank**, so practice all the problems.

The medium difficulty problems were like the one give below:

**Question 1:** **You have data on people have applied for a lottery ticket. The data consists of their name and ticket*** ***number. You have to choose winners by selecting the people present in the alternate rows (the first winner starting from row number 3). Write a query to make things easy to select the winners.**

**Answer:**

*select ***from (select name, ROW_NUMBER() over (order by ticket_no) as srNo**from db) t**where (t.srNo % 2) = 1*

**Question 2:** **Find all the students who either are male or live in Mumbai ( have Mumbai as a part of their address).****Answer:** *Select name**From students**Where lower(gender) in (‘male’,’m’)**Or lower(address) = ‘%mumbai%’*

**Question 3:** **Can you join two table without any common column?****Answer:** *Yes we can do cross join without any common column.**Eg: We have Roll Number, Name of Students in Table A and their Class (let’s say 5th) in Table B.**21**We will use cross join to append class against each student.*

*SELECT B.CLASS,A.ID,A.NAME**FROM A, B**WHERE 1=1*

**Question 4:**

**Select case when null=null then ‘Amit’ else ‘Rahul’ end from dual. What will be the output of the above query?****Answer:** *The Null value has a memory reference.2 Null values cannot have same memory Reference. So output will be ‘Rahul’.*

**Question 5: ** **List the different types of relationships in SQL.**

*There are different types of relations in the database:*

**One-to-One –**This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.**One-to-Many and Many-to-One –**This is the most frequent connection, in which a record in one table is linked to several records in another.**Many-to-Many –**This is used when defining a relationship that requires several instances on each sides.**Self-Referencing Relationships –**When a table has to declare a connection with itself, this is the method to employ.

**Question 6: What are the differences between OLTP and OLAP? **

**Answer:** *OLTP stands for online transaction processing, whereas OLAP stands for online analytical processing. OLTP is an online database modification system, whereas OLAP is an online database query response system.*

**Question 7: What is the usage of the NVL() function?**

**Answer:*** You may use the NVL function to replace null values with a default value. The function returns the value of the second parameter if the first parameter is null. If the first parameter is anything other than null, it is left alone.*

## OYO Case Study Questions

**Case Study – Suggest as many important KPIs as possible that you want to put on the CXOs dashboard**

Following were the suggested KPIs

- Average Daily Rate (ADR)
- Occupancy rate
- Revenue per Available Room (RevPAR)
- Gross Operating Profit per Available Room (GOPPAR)
- Average Length of Stay (ALOS)
- Customer Acquisition Cost (CAC)
- Customer Lifetime Value (CLV)
- Net Promoter Score (NPS)
- Online Reputation Score (ORS)
- Room Revenue Contribution by Channel
- Website Conversion Rate
- Direct Booking Ratio
- Repeat Guest Ratio
- Housekeeping Productivity Ratio
- Employee Turnover Rate
- Revenue per Employee (RPE)
- Cost per Occupied Room (CPOR)
- Cost per Available Room (CPAR)
- Total Revenue by Property
- Total Expenses by Property

## OYO Statistics and Python Interview Questions

I had a couple of projects on Machine Learning, so a few questions were asked on statistics

**What is Skewness? **

- Skewness is a measure of the asymmetry of a distribution. This value can be positive or negative.
- A negative skew indicates that the tail is on the left side of the distribution, which extends towards more negative values.
- A positive skew indicates that the tail is on the right side of the distribution, which extends towards more positive values.
- A value of zero indicates that there is no skewness in the distribution at all, meaning the distribution is perfectly symmetrical.

2. **What is Kurtosis? **

Kurtosis is a measure of whether or not a distribution is heavy-tailed or light-tailed relative to a normal distribution.

• The kurtosis of a normal distribution is 3.

• If a given distribution has a kurtosis less than 3, it is said to be platykurtic, which means it tends to produce fewer and less extreme outliers than the normal distribution.

• If a given distribution has a kurtosis greater than 3, it is said to be leptokurtic, which means it tends to produce more outliers than the normal distribution.

3.**How are covariance and correlation different from one another?**

Covariance measures how two variables are related to each other and how one would vary with respect to changes in the other variable. If the value is positive, it means there is a direct relationship between the variables and one would increase or decrease with an increase or decrease in the base variable respectively, given that all other conditions remain constant.

Correlation quantifies the relationship between two random variables and has only three specific values, i.e., 1, 0, and -1.

1 denotes a positive relationship, -1 denotes a negative relationship, and 0 denotes that the two variables are independent of each other.

4.**What is Multicollinearity ? **

Multicollinearity occurs when two or more independent variables are highly correlated with one another in a regression model. This means that an independent variable can be predicted from another independent variable in a regression model.

5.**What is VIF? **

Variance inflation factor (VIF) is a measure of the amount of multicollinearity in a set of multiple regression variables. In general, a VIF above 5 indicates high correlation and is cause for concern. Some authors suggest a more conservative level of 2.5 or above and it depends on the situation.

6.**What is a confusion matrix and why do you need it? **

Confusion matrix is a table that is frequently used to illustrate the performance of a classification model i.e., classifier on a set of test data for which the true values are well-known. It allows us to visualize the performance of an algorithm/model. It allows us to easily identify the confusion between different classes. It is used as a performance measure of a model/algorithm. It is summary of predictions on a classification model.

7.**What do you mean when you say “Strings are immutable”?****Strings in Python are immutable **i.e you can not change the defined string.

You can not change a part of the string, as it is immutable.

8.**Are lists mutable ?**Lists are mutable i.e. you can change the values already present in the list.

**9.Is dictionary zero indexed? Can we pull something like Team[0] from the above example?**

The whole purpose of having a dictionary is that you can have your own index i.e. key. So, to answer the question, Dictionary is not zero indexed.

You can not use the basic index thing example, you can not use Team[0] to pull the first value because you have already specified an index to all the values

10.**What is the function range() ?**

Range(10) will get you numbers from 0 to 9. But you need to put this range in some data type. Suppose you want to put this in a list.

There were a 2-3 more questions on Python, mostly around for loop and pattern printing.

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