01
What is the difference between mean and median for reporting salaries?
Show answer
Salaries are right-skewed, so the median better represents the typical employee while the mean is pulled up by a few high earners.
Data Analytics Interview Questions in Gurgaon
36 practice questions with answers for Data Analytics interviews in Gurgaon.
Data Analytics jobs in Gurgaon
Gurgaon (Gurugram) is home to many multinational, consulting, fintech and consumer-internet offices. Interviews often expect strong business understanding along with technical skills, especially for analytics and product-facing roles.
Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Gurgaon.
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02
Email open rates suddenly jumped from 22% to 45% without any campaign change. Should you celebrate?
Show answer
No. Privacy features in some mail apps pre-load email images, which fires the open tracking pixel even if the person never reads the email, so open rates get inflated. Check opens by email client to confirm. Move reporting towards clicks, click-to-send rate, conversions and unsubscribes, which reflect real behaviour, and avoid using opens alone to pick winning subject lines or to define engaged subscribers.
03
How do you handle outliers in Pandas?
Show answer
Detect with IQR or z-score, then cap with clip, remove, transform with log, or keep and flag them depending on whether they are errors or real events.
04
What does Flash Fill do in Excel, and when should you not rely on it?
Show answer
Flash Fill (Ctrl+E) detects a pattern from one or two examples you type and fills the rest of the column, for example extracting first names or reformatting phone numbers. It is fast for one-time cleanup. However, it produces static values, not formulas, so it will not update when source data changes, and it can guess wrongly on irregular rows. For repeatable reports, use formulas or Power Query instead.
05
What is the difference between extract filters and data source filters in Tableau?
Show answer
Extract filters decide which rows go into the extract file at all, which reduces its size and speeds every view; filtered rows are simply not available. Data source filters apply to all worksheets using that data source, for live or extract, and are useful for security or scope, such as only the India business. Both act before context filters. Normal worksheet filters only affect one sheet.
06
How do you write a good hypothesis for an experiment?
Show answer
A good hypothesis names the change, the expected effect, the metric and the reason. For example: showing delivery date on the product page will increase add-to-cart rate by at least 2% because uncertainty about delivery stops purchases. It should be written before the test starts, be measurable, and state the null hypothesis of no difference. A vague hypothesis like 'the new page will be better' leads to cherry-picking metrics afterwards.
07
Show a Pandas groupby example.
Show answer
df.groupby('region')['sales'].agg(['sum', 'mean', 'count'])
# splits by region, aggregates, combines the results
08
What is a transaction and what do the ACID properties mean?
Show answer
A transaction is a group of SQL statements that succeed or fail together, such as debiting one account and crediting another. ACID: Atomicity means all or nothing; Consistency means rules and constraints stay valid; Isolation means concurrent transactions do not see each other's partial work; Durability means committed data survives crashes. Analysts mostly read data, but knowing ACID explains why reports should read committed data.
09
Write SQL for a running total of sales by date.
Show answer
SELECT order_date,
SUM(amount) OVER (ORDER BY order_date) AS running_total
FROM sales;
10
What is the difference between an A/B test, an A/B/n test and a multivariate test?
Show answer
An A/B test compares one change with control. An A/B/n test compares several versions of one element, such as three headlines, with control. A multivariate test changes several elements at once, such as headline and button colour, and tests every combination to see each element's effect and their interactions. Multivariate tests need much more traffic because users are spread across many combinations.
11
A stakeholder wants 20 more visuals added to an already crowded dashboard page. How do you respond?
Show answer
I would ask which decisions each new visual supports and how often they will look at it. Usually a few are essential and the rest are nice to have. I would propose keeping the main page focused on core KPIs and moving detail into drill-through pages, tooltips or a separate analysis tab. Showing a quick mock-up of both versions helps them see that the cleaner page answers questions faster.
12
What is the difference between dimensions and measures in Tableau?
Show answer
Dimensions are qualitative fields such as region, product or date that slice data into groups and create headers. Measures are quantitative fields such as sales or profit that get aggregated, by default with SUM. Dragging a dimension to rows creates one row per member; dragging a measure creates an axis. A numeric field like customer ID should be converted to a dimension so it is not summed.
13
How do you calculate month-over-month growth?
Show answer
Aggregate revenue by month, use LAG(revenue) OVER (ORDER BY month), then (revenue - prev) / prev x 100.
14
What is the difference between a surrogate key and a natural key?
Show answer
A natural key comes from the source system and has business meaning, such as a customer mobile number or product SKU. A surrogate key is a meaningless integer or hash generated in the warehouse. Surrogate keys protect the model when source keys change, get reused or clash across systems, and they are essential for slowly changing dimensions where one customer has several historical rows. Keep the natural key as an attribute for lookups.
15
What is the logical order in which SQL executes a SELECT query?
Show answer
Although written as SELECT first, a query is logically processed as FROM and JOIN, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because SELECT runs later, but you can use it in ORDER BY. It also explains why aggregates belong in HAVING, not WHERE, and why window functions cannot be filtered in the same query level.
16
How do you detect outliers?
Show answer
Visually with a box plot, with the IQR rule (below Q1 − 1.5×IQR or above Q3 + 1.5×IQR) or a z-score above 3. Then decide with the business whether to cap, remove or keep them.
17
What is the difference between frequentist and Bayesian A/B testing?
Show answer
Frequentist testing asks how surprising the data would be if there were no difference, and gives p-values and confidence intervals with a fixed sample size. Bayesian testing combines a prior with the data to give a probability that treatment beats control and the expected loss of choosing it. Bayesian results are easier for business users to read, but still depend on priors and still need sensible stopping rules.
18
Write pandas code to find the top 2 products by revenue in each region.
Show answer
top2 = (
df.groupby(['region', 'product'], as_index=False)['revenue']
.sum()
.sort_values(['region', 'revenue'], ascending=[True, False])
.groupby('region')
.head(2)
)
print(top2)
19
Tell me about a time your analysis was challenged in a meeting.
Show answer
Situation: while presenting regional sales, a manager said my numbers were wrong because his team's tracker showed more. Task: handle it without derailing the meeting. Action: I acknowledged the concern, explained my source and definition briefly, and offered to reconcile after the meeting. That afternoon we found his tracker counted orders before cancellations. Result: we agreed on net orders as the standard, and he later asked me to rebuild his tracker.
20
You must present to the sales team that they missed their quarterly target by 18%. How do you handle it?
Show answer
Lead with the fact clearly, then show where the gap came from, such as which regions, products or deal stages, so it becomes actionable rather than a blame session. Separate controllable causes like lower conversion from uncontrollable ones like a delayed product launch. Highlight what did work, and end with two or three specific actions and the metrics to track them. Share the numbers with sales leaders privately first, to avoid surprises.
21
Write SQL to group raw page events into sessions using a 30-minute inactivity rule.
Show answer
WITH ordered AS (
SELECT user_id, event_ts,
LAG(event_ts) OVER (PARTITION BY user_id ORDER BY event_ts) AS prev_ts
FROM page_events
),
flagged AS (
SELECT *,
CASE WHEN prev_ts IS NULL
OR event_ts - prev_ts > INTERVAL '30 minutes'
THEN 1 ELSE 0 END AS new_session
FROM ordered
)
SELECT user_id, event_ts,
SUM(new_session) OVER (PARTITION BY user_id ORDER BY event_ts) AS session_no
FROM flagged;
22
Explain GROUP BY with an example.
Show answer
It groups rows sharing values so aggregates run per group, for example SELECT region, SUM(sales) FROM orders GROUP BY region.
23
A stakeholder asks for a pie chart of 12 product categories. When is a pie chart acceptable, and what would you suggest?
Show answer
A pie chart works only for showing parts of a whole with very few categories, two to four, where one share clearly dominates, such as 70 percent of orders are prepaid. Avoid it when there are many slices, when values are close, or when comparing across periods, because people judge angles poorly. A sorted bar chart is almost always easier to read and compare. Never use 3D pies.
24
What is a CTE and why use it?
Show answer
A Common Table Expression is a named temporary result defined with WITH that makes complex queries readable and allows recursion.
25
Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?
Show answer
A context filter is applied before other dimension filters and before FIXED LOD calculations, creating a smaller dataset the rest of the view works on. Use it when a Top N filter should operate within a selected category, for example top 10 products within Electronics, or when a FIXED LOD must respect a filter. Add a filter to context from its right-click menu; it turns grey. Too many context filters can hurt performance.
26
How would you write a short email summarising an analysis for a manager?
Show answer
Use a clear subject that states the finding, such as Q3 repeat purchase rate fell 4 points, mainly in Tier 2 cities. Open with one or two sentences of the key insight and the recommendation. Follow with three bullet-style lines of supporting evidence with numbers, then any caveats about data, and a clear next step or decision needed with a date. Attach or link the detailed workings instead of pasting large tables.
27
What is a good structure for a data story?
Show answer
A simple structure is context, conflict, resolution. Context sets up what the audience already knows, such as our target was 10 percent growth. Conflict shows the surprising finding or problem, such as growth is only 3 percent because repeat buyers dropped. Resolution gives the recommendation and expected impact. Another option is situation, complication, question, answer. Either way, the audience should know the main message within the first minute.
28
What is the order of operations in Tableau and why does it matter?
Show answer
Tableau applies filters in a fixed order: extract filters, data source filters, context filters, then FIXED LOD calculations, dimension filters, INCLUDE and EXCLUDE LODs, measure filters, and finally table calculations and table calc filters. It matters because a calculation only sees data filtered before it. For example, a FIXED LOD ignores a normal region filter unless that filter is added to context. Many wrong totals come from not knowing this order.
29
What is an action title and why should you use one?
Show answer
An action title states the insight of a chart or slide as a sentence instead of a label. Instead of Monthly Sales by Region, write South region drove 70 percent of the decline since July. Busy executives may read only the titles, so each one should carry the message and, ideally, the so-what. The chart below then proves the title. Read only your titles in order: they should tell the whole story.
30
What is the difference between population and sample?
Show answer
The population is the whole group of interest; a sample is a subset used to estimate population values.
31
RANK vs DENSE_RANK vs ROW_NUMBER?
Show answer
ROW_NUMBER gives a unique sequence. RANK gives ties the same rank and skips the next (1, 1, 3). DENSE_RANK gives ties the same rank without gaps (1, 1, 2).
32
A junior analyst wants to load one huge flat table with every column into Power BI instead of a star schema. How do you advise them?
Show answer
A flat table is quick to start and fine for a small one-off analysis. But at scale it bloats the model because repeated text attributes compress poorly, makes DAX filters and time intelligence harder, and breaks when you add a second fact like targets or returns. I would show them a star schema with one fact and a few dimensions, compare model size and measure speed, and let the results convince them.
33
What is the difference between an outlier and a data error?
Show answer
An outlier is a genuine but unusual value, such as a bulk corporate order of 50 laptops; it is real and may be important. A data error is an incorrect value, such as an age of 250 or a price entered in paise instead of rupees. Errors should be fixed or removed. Outliers should be investigated and often kept, perhaps analysed separately or with median-based metrics so they do not distort averages.
34
How do you find duplicate rows in a table?
Show answer
SELECT col, COUNT(*) FROM t GROUP BY col HAVING COUNT(*) > 1; use ROW_NUMBER() partitioned by the key to delete extras.
35
Estimate how many cups of tea are sold in a city of 1 crore people in a day.
Show answer
State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.
36
What is hypothesis testing in simple words?
Show answer
You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.
Companies with offices in Gurgaon (51)
Consulting
Accenture
Data Analytics interview questions
Bengaluru, Hyderabad, Pune +2
View questionsConsulting
Deloitte
Data Analytics interview questions
Hyderabad, Bengaluru, Mumbai +2
View questionsProduct
Amazon
Data Analytics interview questions
Bengaluru, Hyderabad, Chennai +2
View questionsProduct
Data Analytics interview questions
Bengaluru, Hyderabad, Gurugram +2
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Meta
Data Analytics interview questions
Gurugram, Bengaluru, Hyderabad +1
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Microsoft
Data Analytics interview questions
Hyderabad, Bengaluru, Noida +2
View questionsHardware & semiconductors
Apple
Data Analytics interview questions
Hyderabad, Bengaluru, Mumbai +1
View questionsProduct
Adobe
Data Analytics interview questions
Noida, Bengaluru, Gurugram
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Salesforce
Data Analytics interview questions
Hyderabad, Bengaluru, Mumbai +1
View questionsProduct
Data Analytics interview questions
Bengaluru, Gurugram, Mumbai
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Uber
Data Analytics interview questions
Bengaluru, Hyderabad, Gurugram
View questionsE-commerce & consumer tech
Walmart
Data Analytics interview questions
Bengaluru, Chennai, Gurugram
View questionsE-commerce & consumer tech
Zomato
Data Analytics interview questions
Gurugram
View questionsFintech
Mastercard
Data Analytics interview questions
Pune, Vadodara, Gurugram +1
View questionsFintech
DE Shaw
Data Analytics interview questions
Hyderabad, Bengaluru, Gurugram
View questionsFintech
Tower Research Capital
Data Analytics interview questions
Gurugram
View questionsIT services
IBM
Data Analytics interview questions
Bengaluru, Pune, Hyderabad +2
View questionsConsulting
EY
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Bengaluru, Kolkata, Gurugram +2
View questionsConsulting
KPMG
Data Analytics interview questions
Gurugram, Bengaluru, Mumbai +2
View questionsConsulting
PwC
Data Analytics interview questions
Kolkata, Bengaluru, Mumbai +2
View questionsConsulting
Publicis Sapient
Data Analytics interview questions
Gurugram, Noida, Bengaluru
View questionsIT services
Nagarro
Data Analytics interview questions
Gurugram, Jaipur
View questionsIT services
EPAM Systems
Data Analytics interview questions
Hyderabad, Bengaluru, Pune +2
View questionsIT services
GlobalLogic
Data Analytics interview questions
Noida, Bengaluru, Gurugram +2
View questionsFrequently asked questions
Which companies hire Data Analytics professionals in Gurgaon?
Companies with large offices in Gurgaon include Accenture, Deloitte, Amazon, Google, Meta, Microsoft, Apple, Adobe, Salesforce, LinkedIn. Use their company pages below to practise employer-specific sets.
Are Data Analytics interviews in Gurgaon different from other cities?
The questions depend more on the company than the city. Gurgaon's mix of employers decides the style: Interviews often expect strong business understanding along with technical skills, especially for analytics and product-facing roles
How do I find Data Analytics jobs in Gurgaon?
Shortlist companies with Gurgaon offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Gurgaon filter, and practise the questions on this page before each interview.
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