01
How do you deal with missing or duplicate data?
Show answer
Profile the data, remove exact duplicates using keys, impute missing values (mean/median/mode or a business rule), flag imputed rows and document every cleaning step.
Data Analytics Interview Questions for IT services Companies
40 questions weighted to the interview style of it services companies — 22 easy, 17 medium, 1 hard.
How it services companies interview for Data Analytics
IT services companies hire in large numbers, often in campus or bulk drives. Interviews usually start with an online aptitude and coding test, then one or two technical rounds on fundamentals and your projects, then HR. Clear basics and good communication matter more than rare, advanced topics.
This set focuses on Python for Analysis (7), SQL (6), Excel (5), Dashboards and KPIs (4).
Technical interview HR round Managerial round Coding test Online assessment (aptitude + reasoning)
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02
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.
03
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.
04
How do you calculate a running total?
Show answer
SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.
05
A campaign landing page gets heavy traffic but very few sign-ups. How would you diagnose it?
Show answer
Check traffic quality first: is the right audience coming, and does the ad message match the page? Then look at page behaviour by device: load time, scroll depth, form start versus form submit, and which form field users abandon. Session recordings or heatmaps help. Test the form on mobile for errors. Common fixes are faster pages, fewer form fields, a clear call to action above the fold and matching the ad's offer.
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
What are primary metrics and guardrail metrics in an A/B test?
Show answer
The primary metric is the one decision metric the test is designed to move, such as checkout conversion. Guardrail metrics are ones that must not get worse, such as page load time, refund rate, unsubscribe rate or revenue per user. A change that lifts conversion but increases cancellations should not ship. Define both before launch, so the decision rule is clear and nobody picks the favourable metric afterwards.
08
What are field parameters in Power BI?
Show answer
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
09
What is tidy data?
Show answer
Tidy data follows three rules: each variable is a column, each observation is a row, and each type of observational unit is a table. A sheet with months as columns is not tidy; unpivoting it to Region, Month and Sales columns makes it tidy. Tidy data is easy to filter, group, join and chart in pandas, SQL, Power BI and Tableau, so reshaping into this form is a core cleaning step.
10
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.
11
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.
12
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.
13
Explain correlation vs regression.
Show answer
Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.
14
What is the difference between Goal Seek and Solver?
Show answer
Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
15
How do you merge two DataFrames on different column names?
Show answer
pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.
16
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.
17
How do you combine text from several cells with a separator in Excel?
Show answer
-- join first and last name with a space
=TEXTJOIN(" ", TRUE, A2, B2)
-- all products of one order in a single cell, comma separated
=TEXTJOIN(", ", TRUE, FILTER(C2:C500, A2:A500=F2))
-- older method
=A2 & " " & B2
-- TRUE in TEXTJOIN skips empty cells, which CONCAT and & do not
18
Tell me about a time you used data to challenge an assumption.
Show answer
Situation: our team believed weekend discounts drove most sales. Task: I was preparing the monthly review. Action: I compared discount and non-discount weekends over six months and controlled for paydays and festivals. Discounts moved sales forward from the following week but added little overall. I presented it with one clear chart. Result: management trialled fewer discount weekends, and margin improved without a fall in monthly revenue.
19
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.
20
How do you unpivot a wide monthly sales sheet into a long table in Power Query?
Show answer
Load the table into Power Query. Select the identifier columns such as Region and Product, then choose Transform, Unpivot Other Columns. The month columns become two columns, Attribute and Value; rename them Month and Sales. Change Month to a date type and Sales to a decimal number. Using Unpivot Other Columns rather than Unpivot Columns means new months added later are picked up automatically on refresh.
21
What are slicers and filters?
Show answer
Slicers are on-page visual filters users click; filters can be set at visual, page or report level and may be hidden from users.
22
How do you split a full name column into first and last name in Excel?
Show answer
-- A2 = 'Priya Ramesh Iyer'
-- first name
=TEXTBEFORE(TRIM(A2), " ")
-- last name (text after the last space)
=TEXTAFTER(TRIM(A2), " ", -1)
-- older Excel: first name
=LEFT(TRIM(A2), FIND(" ", TRIM(A2)&" ")-1)
-- or use Data > Text to Columns or Flash Fill (Ctrl+E) for a one-off task
23
Write a Python function that reads all Excel files in a folder and combines them into one DataFrame.
Show answer
from pathlib import Path
import pandas as pd
def combine_excel(folder):
frames = []
for f in Path(folder).glob('*.xlsx'):
df = pd.read_excel(f)
df['source_file'] = f.name
frames.append(df)
return pd.concat(frames, ignore_index=True)
all_sales = combine_excel('monthly_reports')
24
Write a Tableau calculated field for profit ratio and a profit category.
Show answer
// Profit Ratio (aggregate first, then divide)
SUM([Profit]) / SUM([Sales])
// Profit Category
IF SUM([Profit]) < 0 THEN 'Loss'
ELSEIF [Profit Ratio] < 0.1 THEN 'Low margin'
ELSE 'Healthy'
END
// avoid AVG([Profit]/[Sales]): it averages row ratios and gives a wrong total
25
What is an A/A test and why would you run one?
Show answer
An A/A test splits users into two groups that both see the same version. Since nothing differs, you expect no significant difference except about 5% of the time by chance. It checks that randomisation, tracking and the analysis pipeline work correctly, and it reveals sample ratio mismatches or metric variance before real tests depend on the platform. Running many A/A tests also confirms your false positive rate is near 5%.
26
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.
27
Many reporting queries in your team use SELECT DISTINCT to remove duplicate rows. What is the risk?
Show answer
SELECT DISTINCT often hides a real problem, such as a join on the wrong key or a one-to-many join that multiplies rows. The output looks clean, but sums computed before the DISTINCT, or legitimate repeated rows, can be wrong, and DISTINCT adds a costly sort on large tables. Instead, find why duplicates appear, fix the join or aggregate the child table first, and add a uniqueness test on the key.
28
How should you lay out a dashboard page?
Show answer
Readers scan from the top-left, so place the most important KPIs as cards across the top, then trend charts, then breakdowns and detail tables at the bottom. Group related visuals, align them on a grid and keep consistent spacing, fonts and colours. Put filters in one consistent place. Limit a page to roughly five to eight visuals that answer related questions, and give the page a clear title stating its purpose.
29
What are strategic, operational and analytical dashboards?
Show answer
Strategic dashboards serve leadership with a few high-level KPIs and trends, updated daily to monthly. Operational dashboards monitor live or near-live activity for teams on the ground, such as orders pending dispatch or call queue length, often with alerts. Analytical dashboards let analysts and managers explore data with filters and drill-downs to find causes. Knowing which type you are building decides the level of detail, refresh rate and design.
30
What is the difference between a calculated field and a helper column in Excel?
Show answer
A calculated field lives inside a PivotTable and works on summarised data; a helper column is a normal column of formulas that feeds the PivotTable or other formulas.
31
What is data validation in Excel?
Show answer
Data → Data Validation restricts what can be entered in a cell (lists, number ranges, dates), reducing manual entry errors in shared sheets.
32
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.
33
How do you create a new column from conditions?
Show answer
Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.
34
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.
35
How can a chart mislead even when the numbers are correct?
Show answer
Common tricks are a truncated y-axis on bar charts, which exaggerates small differences; inconsistent scales across side-by-side charts; dual axes chosen to imply a correlation; cherry-picked time windows; cumulative charts that always rise; and area or 3D effects that distort size. Bars should start at zero, while line charts may zoom in if clearly labelled. Always check that the visual impression matches the actual magnitude.
36
Write pandas code for a pivot table of sales by region and category with sum and count.
Show answer
import pandas as pd
summary = pd.pivot_table(
df,
index='region',
columns='category',
values='sales',
aggfunc=['sum', 'count'],
fill_value=0,
margins=True # adds an All row and column
)
print(summary)
37
Difference between UNION and UNION ALL?
Show answer
UNION removes duplicate rows and sorts internally; UNION ALL keeps every row and is faster.
38
What is the difference between a list, tuple, dictionary and set in Python?
Show answer
A list is ordered and mutable, good for sequences you change, like [10, 20, 30]. A tuple is ordered but immutable, useful for fixed records such as coordinates. A dictionary stores key-value pairs with fast lookup by key, like {'Delhi': 120}. A set holds unique unordered values and is great for removing duplicates or checking membership quickly. Analysts use dictionaries constantly for mappings and renaming columns.
39
What is the difference between COUNT, COUNTA and COUNTIF?
Show answer
COUNT counts numbers, COUNTA counts non-empty cells and COUNTIF counts cells meeting one condition; COUNTIFS handles several conditions.
40
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.
IT services companies (33)
IT services
TCS
Data Analytics interview questions
Mumbai, Chennai, Pune +2
View questionsIT services
Infosys
Data Analytics interview questions
Bengaluru, Mysuru, Pune +2
View questionsIT services
Wipro
Data Analytics interview questions
Bengaluru, Hyderabad, Pune +2
View questionsIT services
Cognizant
Data Analytics interview questions
Chennai, Bengaluru, Pune +2
View questionsIT services
Capgemini
Data Analytics interview questions
Mumbai, Bengaluru, Pune +2
View questionsIT services
HCLTech
Data Analytics interview questions
Noida, Chennai, Bengaluru +2
View questionsIT services
Tech Mahindra
Data Analytics interview questions
Pune, Hyderabad, Bengaluru +2
View questionsIT services
IBM
Data Analytics interview questions
Bengaluru, Pune, Hyderabad +2
View questionsIT services
Mphasis
Data Analytics interview questions
Bengaluru, Pune, Chennai
View questionsIT services
LTIMindtree
Data Analytics interview questions
Mumbai, Pune, Bengaluru +2
View questionsIT services
Persistent Systems
Data Analytics interview questions
Pune, Nagpur, Bengaluru +2
View questionsIT services
Hexaware
Data Analytics interview questions
Navi Mumbai, Chennai, Pune +2
View questionsIT services
Coforge
Data Analytics interview questions
Noida, Greater Noida, Bengaluru +2
View questionsIT services
Zensar Technologies
Data Analytics interview questions
Pune, Hyderabad, Bengaluru
View questionsIT services
Birlasoft
Data Analytics interview questions
Pune, Noida, Bengaluru +1
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 questionsIT services
Virtusa
Data Analytics interview questions
Hyderabad, Chennai, Bengaluru
View questionsIT services
UST
Data Analytics interview questions
Thiruvananthapuram, Kochi, Bengaluru +2
View questionsIT services
CGI
Data Analytics interview questions
Bengaluru, Hyderabad, Chennai +1
View questionsIT services
NTT DATA
Data Analytics interview questions
Bengaluru, Pune, Noida +2
View questionsIT services
Genpact
Data Analytics interview questions
Gurugram, Hyderabad, Bengaluru +2
View questionsIT services
L&T Technology Services
Data Analytics interview questions
Vadodara, Bengaluru, Chennai +2
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Cyient
Data Analytics interview questions
Hyderabad, Bengaluru, Noida
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Sonata Software
Data Analytics interview questions
Bengaluru, Hyderabad
View questionsIT services
Mastek
Data Analytics interview questions
Mumbai, Pune
View questionsIT services
Happiest Minds Technologies
Data Analytics interview questions
Bengaluru, Noida
View questionsIT services
KPIT Technologies
Data Analytics interview questions
Pune, Bengaluru
View questionsIT services
Tata Elxsi
Data Analytics interview questions
Bengaluru, Thiruvananthapuram, Chennai +1
View questionsIT services
Quest Global
Data Analytics interview questions
Bengaluru, Thiruvananthapuram, Belagavi +1
View questionsIT services
Infogain
Data Analytics interview questions
Noida, Gurugram, Pune +1
View questionsIT services
YASH Technologies
Data Analytics interview questions
Indore, Pune, Hyderabad
View questionsFrequently asked questions
How are Data Analytics interviews at it services companies different?
IT services companies hire in large numbers, often in campus or bulk drives. Interviews usually start with an online aptitude and coding test, then one or two technical rounds on fundamentals and your projects, then HR. Clear basics and good communication matter more than rare, advanced topics.
Which it services companies are covered?
This page links to 33 it services companies, including TCS, Infosys, Wipro, Cognizant, Capgemini, HCLTech, Tech Mahindra, IBM. Each has its own page with rounds and a separate practice set.
What rounds should I expect?
Common rounds at these companies include: Technical interview; HR round; Managerial round; Coding test; Online assessment (aptitude + reasoning). The exact process varies by company and role.
Other company types
More ways to practise
Company-wise Data Analytics questionsPackage-wise Data Analytics questionsExperience-wise Data Analytics questionsRole-wise Data Analytics questionsCity-wise Data Analytics questionsMost asked Data Analytics questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsData Analytics mock interviewOnline assessment questionsTechnical round questionsManagerial round questionsData Analytics topic sheets