01
Explain the types of SQL joins.
Show answer
INNER returns matching rows, LEFT keeps all left rows, RIGHT keeps all right rows, FULL keeps all rows from both, and CROSS returns every combination.
Data Analytics Interview Questions in Chandigarh
36 practice questions with answers for Data Analytics interviews in Chandigarh.
Data Analytics jobs in Chandigarh
Chandigarh, Mohali and Panchkula form the Tricity IT cluster, with IT-services firms, SaaS companies and start-ups in Mohali's IT City and Chandigarh's IT Park. Interviews usually check strong fundamentals and practical tool skills.
Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Chandigarh.
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02
What is regression to the mean? Give a business example.
Show answer
Regression to the mean means extreme results tend to be followed by more average results, simply because part of the extreme was luck. For example, if you pick the 10 worst-performing stores this quarter and give them a training programme, many will improve next quarter even without training. To claim the programme worked, compare them with similar low performers who did not get it.
03
Write a DAX measure for year-over-year sales growth percentage.
Show answer
Sales LY =
CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
Sales YoY % =
VAR cur = [Total Sales]
VAR prev = [Sales LY]
RETURN
DIVIDE(cur - prev, prev)
-- needs a marked date table; format the measure as a percentage
04
A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?
Show answer
Totals in DAX are not the sum of visible rows; the measure is recalculated in the total's filter context. A measure like a distinct count, a ratio or an IF condition can give a different result at total level. If the business wants the sum of row values, iterate explicitly, for example SUMX ( VALUES ( Product[Name] ), [My Measure] ). Confirm with stakeholders which total is correct before changing it.
05
Tell me about a time you worked with messy data under a tight deadline.
Show answer
Situation: for a college placement project, I received survey data from three forms with different column names two days before submission. Task: combine and analyse it. Action: I mapped columns to a standard list, cleaned obvious issues like duplicate responses and mixed date formats in Power Query, and noted assumptions. I focused analysis on the five questions that mattered most. Result: we submitted on time, and the faculty praised the documented cleaning steps.
06
Write pandas code to extract month name, weekday and hour from a timestamp column.
Show answer
df['ts'] = pd.to_datetime(df['ts'], errors='coerce')
df['month'] = df['ts'].dt.month_name()
df['weekday'] = df['ts'].dt.day_name()
df['hour'] = df['ts'].dt.hour
df['is_weekend'] = df['ts'].dt.dayofweek >= 5
# orders by weekday
print(df['weekday'].value_counts())
07
A report filters WHERE order_date = '2026-03-31' and misses almost all orders from that day. Why?
Show answer
order_date is probably a DATETIME or TIMESTAMP column, so the filter matches only rows at exactly midnight, 2026-03-31 00:00:00. Use a half-open range instead: WHERE order_date >= '2026-03-31' AND order_date < '2026-04-01'. This keeps the index usable, unlike wrapping the column in CAST or DATE(), and it correctly includes orders at 23:59:59.500. Also check the time zone of the stored timestamps.
08
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Show answer
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
09
What is the difference between discrete and continuous fields in Tableau?
Show answer
Discrete fields, shown as blue pills, have distinct values and create headers, for example month names as separate columns. Continuous fields, shown as green pills, form an unbroken range and create an axis, for example a continuous date line chart. The same date can be either: discrete months give a bar per month label, continuous months give a time-series axis. Colour also changes: discrete gives separate colours, continuous a gradient.
10
What is a Data Table in Excel's What-If Analysis?
Show answer
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
11
How do you clean messy text data in Excel?
Show answer
Use TRIM, CLEAN, UPPER or PROPER, Text to Columns, Flash Fill, SUBSTITUTE, and Power Query for repeatable cleaning steps.
12
Write a Tableau calculation to find each customer's first purchase date and flag new versus repeat orders.
Show answer
// First Purchase Date
{ FIXED [Customer ID] : MIN([Order Date]) }
// Order Type
IF [Order Date] = [First Purchase Date]
THEN 'New'
ELSE 'Repeat'
END
// Cohort Month for retention analysis
DATETRUNC('month', [First Purchase Date])
13
Write SQL to flag rows that fail basic validation rules in a customer table.
Show answer
SELECT customer_id,
CASE WHEN email IS NULL OR email NOT LIKE '%_@_%._%' THEN 1 ELSE 0 END AS bad_email,
CASE WHEN LENGTH(pincode) <> 6 THEN 1 ELSE 0 END AS bad_pincode,
CASE WHEN signup_date > CURRENT_DATE THEN 1 ELSE 0 END AS future_signup,
CASE WHEN age NOT BETWEEN 18 AND 100 THEN 1 ELSE 0 END AS bad_age
FROM customers;
-- sum each flag to report the error rate per rule
14
What is data storytelling?
Show answer
Data storytelling is communicating insights by combining three things: data, the evidence; visuals, charts that make patterns obvious; and narrative, the context and so-what that explains why it matters and what to do. A table of numbers informs, but a story persuades someone to act. For an analyst, it means leading with the key finding, showing just enough evidence, and ending with a clear recommendation.
15
Write pandas code to fill missing values in a column with the median of each group.
Show answer
# Fill missing delivery_days with the median for that city
df['delivery_days'] = (
df['delivery_days']
.fillna(df.groupby('city')['delivery_days'].transform('median'))
)
# Rows in a city with no values at all stay NaN; fall back to overall median
df['delivery_days'] = df['delivery_days'].fillna(df['delivery_days'].median())
16
How do you decide the sample size for a survey?
Show answer
It depends on margin of error, confidence level and expected variability; larger samples give smaller errors with diminishing returns.
17
Sales dropped 20% last month. How will you analyse it?
Show answer
Confirm data accuracy first → break down by region, product, channel and customer segment → compare with seasonality and last year → check pricing, stock-outs, marketing changes and funnel conversion → present the root causes with clear charts.
18
How would you design a sales dashboard for executives?
Show answer
Start with KPIs (revenue, margin, growth), add trend and comparison to target, allow drill-down by region and product, keep it to one screen and test with the audience.
19
Write an Excel formula to look up a price using two criteria, product and city.
Show answer
=XLOOKUP(1, (A2:A500=G2) * (B2:B500=H2), C2:C500, "Not found")
-- A = product, B = city, C = price
-- G2 and H2 hold the product and city to find
-- each condition returns TRUE/FALSE; multiplying gives 1 only where both match
20
What are leading and lagging indicators? Give examples.
Show answer
Lagging indicators report outcomes that have already happened, such as monthly revenue, profit or churn; they are accurate but too late to change. Leading indicators move earlier and predict outcomes, such as qualified leads, demo bookings, cart additions or support ticket backlog. A sales dashboard should pair them: pipeline coverage today signals revenue next quarter. Leading indicators need validation that they really predict the lagging result.
21
How do you calculate working days between two dates in Excel, excluding holidays?
Show answer
-- A2 = start date, B2 = end date, Holidays = named range of holiday dates
=NETWORKDAYS(A2, B2, Holidays)
-- if the weekend is only Sunday (weekend code 11)
=NETWORKDAYS.INTL(A2, B2, 11, Holidays)
-- date that is 10 working days after A2
=WORKDAY(A2, 10, Holidays)
22
What do the match_mode and search_mode arguments of XLOOKUP control?
Show answer
match_mode decides how a value is matched: 0 exact (default), -1 exact or next smaller, 1 exact or next larger, and 2 wildcard. search_mode decides direction: 1 searches first to last, -1 searches last to first, and 2 or -2 use binary search on sorted data. Searching last to first is a quick way to fetch the latest entry for a customer without sorting, and next-smaller matching handles tax slabs or commission bands.
23
Organic search traffic fell 40% overnight. How do you investigate?
Show answer
First check whether it is a tracking problem: did the analytics tag break on some templates, did consent settings change, does server log or backend order data show the same fall? If the drop is real, check the search console for impressions and rankings, recent site releases such as changed URLs, missing redirects, noindex tags or robots.txt changes, and whether a search algorithm update or seasonal pattern matches. Then break the drop down by page group.
24
What are indexes and when can they hurt?
Show answer
Indexes speed up lookups by keeping sorted pointers. They slow writes and use space, so index columns used in filters and joins, not every column.
25
What is Power Query's role in Power BI?
Show answer
It extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.
26
Write pandas code to convert daily sales into weekly totals, showing zero for days with no sales.
Show answer
df['date'] = pd.to_datetime(df['date'])
daily = (df.groupby('date')['sales'].sum()
.asfreq('D', fill_value=0)) # adds missing days as 0
weekly = daily.resample('W-MON', label='left', closed='left').sum()
print(weekly.head())
# W-MON with label/closed left gives weeks starting on Monday
27
Import mode vs DirectQuery?
Show answer
Import loads data into memory for fast visuals; DirectQuery queries the source live, giving fresh data but slower performance and some DAX limits.
28
What is referential integrity and how do orphan records appear in analytics data?
Show answer
Referential integrity means every foreign key value points to an existing row in the parent table, for example every order's customer_id exists in customers. Orphan records break this rule. They appear when source systems load tables at different times, records are deleted without cascading, or IDs are formatted differently across systems. Detect them with a LEFT JOIN where the parent key is NULL, and decide whether to fix, map or flag them.
29
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.
30
What is a percentile and what is the interquartile range?
Show answer
A percentile is the value below which a given percentage of data falls; the 90th percentile delivery time means 90 percent of deliveries were faster. The interquartile range is the 75th percentile minus the 25th percentile, the spread of the middle half. IQR is robust to outliers, and values beyond 1.5 times the IQR from the quartiles are often flagged as outliers in box plots.
31
How do you detect and handle skewed data?
Show answer
Check histograms and skew(), then apply log, square-root or Box-Cox transforms, or use medians and robust statistics.
32
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.
33
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.
34
A query on a 2-billion-row fact table in a cloud warehouse scans the whole table even when you filter one week. How do you fix it?
Show answer
Partition the table by date so the engine can prune partitions outside the filtered week, and cluster or sort by frequently filtered columns such as store or customer. Make sure the filter is on the partition column directly, not wrapped in a function like CAST or DATE_TRUNC that blocks pruning. Select only needed columns, because storage is columnar. For dashboards, also consider a pre-aggregated daily summary table.
35
Write SQL to calculate open rate and click-through rate for each email campaign.
Show answer
SELECT
s.campaign_id,
COUNT(DISTINCT s.user_id) AS delivered,
COUNT(DISTINCT o.user_id) AS opened,
COUNT(DISTINCT c.user_id) AS clicked,
ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT s.user_id), 1) AS open_rate,
ROUND(100.0 * COUNT(DISTINCT c.user_id) / COUNT(DISTINCT s.user_id), 1) AS ctr
FROM email_sends s
LEFT JOIN email_opens o ON o.campaign_id = s.campaign_id AND o.user_id = s.user_id
LEFT JOIN email_clicks c ON c.campaign_id = s.campaign_id AND c.user_id = s.user_id
WHERE s.status = 'delivered'
GROUP BY s.campaign_id;
36
Tell me about a time you had to say no to a stakeholder's request.
Show answer
Situation: a regional manager asked me to show his team's numbers excluding two weeks of a stock-out. Task: be helpful without misreporting. Action: I explained that removing the period would make his region incomparable with others, and offered an annotation and a separate view showing the stock-out impact. Result: he accepted, the leadership review understood the dip, and the main dashboard stayed consistent for every region.
Companies with offices in Chandigarh (1)
Few big employers listed in Chandigarh?
Many Chandigarh candidates also apply to teams in Gurgaon and to remote or hybrid roles. Practise the Gurgaon set as well.
Frequently asked questions
Which companies hire Data Analytics professionals in Chandigarh?
Companies with large offices in Chandigarh include Grant Thornton Bharat. Use their company pages below to practise employer-specific sets.
Are Data Analytics interviews in Chandigarh different from other cities?
The questions depend more on the company than the city. Chandigarh's mix of employers decides the style: Interviews usually check strong fundamentals and practical tool skills
How do I find Data Analytics jobs in Chandigarh?
Shortlist companies with Chandigarh offices, keep your CV focused on Data Analytics projects, apply on company career pages and job portals with the Chandigarh filter, and practise the questions on this page before each interview.
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