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.
Coforge Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Coforge hiring for Data Analytics roles
Coforge, formerly NIIT Technologies, is an Indian IT services company focused on industries such as banking, insurance, and travel and transportation. It provides application development, testing, cloud, data and integration services. In India it hires for software engineers, test engineers, data analysts and data engineers.
For Data Analytics roles, this Coforge practice set focuses most on Python for Analysis (6), Data Modelling & Warehousing (4), Excel (4), Data Cleaning (4). It has 21 easy, 18 medium, 1 hard questions, so you can start easy and work up to the harder ones.
- Company type
- IT services
- Headquarters
- Noida, India
- Main India offices
- Noida, Greater Noida, Bengaluru, Pune, Hyderabad
Coforge Data Analytics interview process
- Online assessment (aptitude + reasoning)
- Technical MCQs or coding test
- Technical interview
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
What is the difference between a metric and a KPI?
Show answer
A metric is any quantity you measure, such as page views, tickets closed or average order value. A KPI, key performance indicator, is a metric chosen because it directly reflects progress towards an important business goal and usually has a target and owner. Every KPI is a metric, but most metrics are not KPIs. A good dashboard leads with a few KPIs and keeps supporting metrics for diagnosis.
03
What is dimensional modelling?
Show answer
Dimensional modelling is a way of designing analytical tables around business processes. Numeric events such as sales or payments go into fact tables, and descriptive context such as customer, product, store and date goes into dimension tables. The design is easy for business users to understand and fast for BI tools to aggregate. For example, a retail model might have one sales fact joined to date, product, store and customer dimensions.
04
What are additive, semi-additive and non-additive facts?
Show answer
Additive facts, like sales amount or quantity, can be summed across every dimension. Semi-additive facts, like account balance or stock on hand, can be summed across accounts or stores but not across time; for time you take the closing or average value. Non-additive facts, like percentages, ratios or unit prices, cannot be summed at all, so you store their components (profit and revenue) and calculate the ratio at query time.
05
How do you remove duplicates in Excel but keep the latest record?
Show answer
Sort by date (newest first), then Data → Remove Duplicates on the key column — Excel keeps the first occurrence. Or use UNIQUE / a COUNTIFS helper column.
06
When would you use a one-tailed test instead of a two-tailed test?
Show answer
A two-tailed test checks for a difference in either direction and is the safer default for A/B tests. A one-tailed test checks only one direction, such as treatment being better, and needs fewer users. Use it only if you decided beforehand that you would act the same way whether the change has no effect or a negative effect. Otherwise you may miss harm, which is why most teams stay two-tailed.
07
How should you document data cleaning decisions?
Show answer
Keep a cleaning log or README listing each rule, why it exists and its impact, for example removed 1,240 test orders flagged by email domain. Code-based cleaning in SQL, Python or Power Query steps is itself documentation if steps are named clearly. Record counts before and after each major step. This lets reviewers trust the numbers, helps a colleague rerun the work, and answers the question why your total differs from the raw file.
08
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)
09
What are UTM parameters and why do they matter?
Show answer
UTM parameters are tags added to a URL so analytics tools know where a visit came from. The main ones are utm_source (instagram, newsletter), utm_medium (paid_social, email, cpc), utm_campaign (diwali_sale_2026), plus optional utm_content and utm_term. Without them, traffic from emails, messaging apps and many social links shows up as direct or referral, and you cannot measure which campaign drove sign-ups or sales.
10
What is a data mart?
Show answer
A data mart is a subset of the warehouse built for one department or subject, such as sales, finance or HR. It contains the facts and dimensions that team needs, often pre-aggregated and with business-friendly names. Marts make reporting faster and simpler for that team. Ideally they are built from the central warehouse using conformed dimensions, so the finance mart and the sales mart still agree on revenue.
11
What is Power BI and its main components?
Show answer
A Microsoft BI tool made of Power BI Desktop for building reports, the Service for sharing, and Mobile; it connects, models and visualises data.
12
Tell me about a time you had to learn a new tool or library quickly for a project.
Show answer
Situation: in my internship the team needed survey results analysed in Python, which I had only used in coursework. Task: deliver cleaned data and charts within a week. Action: I spent two evenings on pandas and Seaborn tutorials using our real file, kept a notes document of commands, and asked a senior to review my first notebook. Result: I delivered on day five, and my notebook became the template for the next survey.
13
How do SUMIFS and SUMPRODUCT differ?
Show answer
SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
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
What is a story in Tableau?
Show answer
A story is a sequence of sheets or dashboards, called story points, that walk an audience through an analysis step by step, like slides. Each point can keep its own filters and selections and has a caption explaining the insight. It is useful for presenting findings such as why churn rose, moving from the headline number to segments and recommendations. For everyday monitoring, a regular dashboard is better than a story.
16
Your Tableau map shows a large number of unknown locations. How do you fix it?
Show answer
Click the unknown indicator at the bottom right of the map and choose Edit Locations. Usually Tableau cannot match names because of spelling variants, such as 'Bengaluru' versus 'Bangalore', or an ambiguous city that needs the state and country set. Make sure fields have the correct geographic role, add a country or state field to the view, clean names in the data source, or use latitude and longitude columns.
17
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.
18
Write Python code to extract valid 10-digit Indian mobile numbers from a messy text column.
Show answer
import re
pattern = re.compile(r'(?:\+?91[\s-]?|0)?([6-9]\d{9})')
def get_mobile(text):
if not isinstance(text, str):
return None
digits = re.sub(r'[\s()-]', '', text)
m = pattern.search(digits)
return m.group(1) if m else None
df['mobile'] = df['contact'].apply(get_mobile)
19
What is the difference between concat, merge and join in pandas?
Show answer
concat stacks DataFrames vertically (more rows) or side by side along an axis, for example appending monthly files with the same columns. merge combines DataFrames on key columns, like a SQL join, with how='inner', 'left', 'right' or 'outer'. join is a shortcut for merging on the index. Use concat to append and merge to look up related data. Always check row counts after a merge.
20
What is a correlated subquery?
Show answer
A correlated subquery refers to a column from the outer query, so it is logically evaluated once per outer row. Example: find employees earning above their department average with WHERE salary > (SELECT AVG(salary) FROM emp e2 WHERE e2.dept_id = e1.dept_id). It is readable but can be slow on large tables. The same result is often faster with a window function such as AVG(salary) OVER (PARTITION BY dept_id).
21
What is a p-value, explained simply?
Show answer
A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis, usually no effect, were true. A p-value of 0.03 means that if the new page truly made no difference, results this strong would appear only about 3 percent of the time by chance. Below a chosen threshold such as 0.05 we reject the null. It is not the probability that the hypothesis is true.
22
How do you present insights to non-technical stakeholders?
Show answer
Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.
23
Why do you want to work as a data analyst?
Show answer
A good answer links genuine interest, evidence and the role. For example: I enjoy turning messy questions into clear answers. In my final-year project I analysed two years of canteen sales in Excel and Python and showed that changing the menu on exam weeks could cut waste. I liked that the analysis led to an actual change. I want a role where my SQL, Power BI and communication skills help teams make better decisions.
24
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)
25
Explain Matplotlib vs Seaborn.
Show answer
Matplotlib is the low-level plotting library with full control; Seaborn builds on it with attractive statistical charts and simpler syntax.
26
What are novelty and primacy effects in experiments?
Show answer
A novelty effect is when users engage more with a change simply because it is new, so the early lift fades over time. A primacy effect is the opposite: existing users resist a change at first because they are used to the old design, so results start poor and improve. Both mean short tests mislead. Run tests for at least one or two full weeks and compare new versus returning users.
27
What is the difference between OLTP and OLAP systems?
Show answer
OLTP systems run day-to-day operations, such as billing or order entry. They handle many small inserts and updates, use normalised tables and need fast single-row access. OLAP systems support analysis, scanning millions of rows to aggregate sales by month or region. They use denormalised star schemas, columnar storage and are mostly read-heavy. Analysts should query OLAP copies so heavy reports do not slow down the live application.
28
SUMIFS vs COUNTIFS?
Show answer
SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions.
Example: =SUMIFS(Sales, Region, "North", Month, "Jan")
29
What is a date table in Power BI and why should you mark it as one?
Show answer
A date table has one row per day with no gaps, covering the full range of your data, plus columns like year, month, quarter and week. Marking it as a date table tells Power BI to use it for time intelligence functions such as TOTALYTD and SAMEPERIODLASTYEAR. Without it, those functions can give wrong results and auto date tables bloat the model. Relate it to every fact table's date column.
30
How do you declutter a chart?
Show answer
Remove everything that does not help the message: heavy gridlines, borders, background colour, 3D effects, redundant legends and too many decimal places. Label data directly instead of using a legend, sort bars meaningfully, round numbers, and use one highlight colour with grey for the rest. Shorten axis labels and keep consistent fonts. Ask whether removing each element would lose information; if not, remove it.
31
What is the difference between a t-test and a z-test?
Show answer
Both compare means. A z-test assumes the population standard deviation is known or the sample is large, so the normal distribution applies. A t-test estimates standard deviation from the sample and uses the t-distribution, which has heavier tails for small samples. In practice analysts mostly use t-tests: one-sample, independent two-sample (Welch's version if variances differ) or paired, for before-after measurements on the same users.
32
Tell me about a time a query you wrote produced wrong results.
Show answer
Situation: my weekly active users number jumped 30 percent overnight. Task: find out whether it was real before the report went out. Action: I traced the query and found a new join to a device table created duplicate users. I changed the count to COUNT(DISTINCT user_id), added a row-count check, and informed my manager before publishing. Result: the corrected number went out on time, and I added that check to every report.
33
The customer table has near-duplicates like 'Rahul Sharma, 98xxxx1234' and 'Rahul Sharma, +91 98xxxx1234'. How do you deduplicate?
Show answer
First normalise: trim and lower-case names, collapse spaces, and strip country codes and symbols from phones and emails. Then match on strong keys such as phone or email. For remaining records use fuzzy matching on names with a similarity score, restricted to the same city or pin code to limit comparisons. Review borderline scores manually, keep a mapping from old IDs to a master ID, and fix entry at the source.
34
What is the difference between a calculated table in DAX and a table created in Power Query?
Show answer
A Power Query table is built during data refresh using M, before data enters the model, and is compressed like any imported table. A DAX calculated table is computed after load from other model tables, using functions such as CALENDAR, SUMMARIZE or DISTINCT. Prefer Power Query for shaping and cleaning source data. Use calculated tables for model-dependent needs, such as a date table from existing data or a small what-if table.
35
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;
36
What is broadcasting in NumPy?
Show answer
Broadcasting lets NumPy perform operations on arrays of different shapes without copying data, by virtually stretching the smaller array. For example, subtracting a 1D array of column means from a 2D array centres every column in one line: X - X.mean(axis=0). Shapes are compatible when trailing dimensions are equal or one of them is 1. It is a key reason vectorised code is much faster than Python loops.
37
What is the difference between SUM and SUMX in DAX?
Show answer
SUM adds up a single column in the current filter context. SUMX is an iterator: it goes row by row over a table, evaluates an expression for each row, then sums the results. Use SUMX when the value must be computed per row first, for example SUMX(Sales, Sales[Qty] * Sales[Price]). Iterators are flexible but can be slower on very large tables, so storing a simple column is sometimes better.
38
What is the Central Limit Theorem and why does it matter for analysts?
Show answer
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, usually from about 30 observations, even if the underlying data is skewed. This is why we can use z-tests, t-tests and confidence intervals for averages such as order value or session time. It does not make the raw data normal, and very heavy-tailed data may need larger samples.
39
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
40
What are vanity metrics?
Show answer
Vanity metrics look impressive but do not help decisions or reflect real value, such as total app downloads, total registered users or social media likes. They usually only go up. Better alternatives are actionable metrics such as active users, retention by cohort, conversion rate and revenue per user. A quick test: if the number changed, would anyone do anything differently? If not, it probably does not belong on the main dashboard.
Tips to crack the Coforge Data Analytics interview
- Learn travel and insurance domain basics like bookings, policy lifecycle and claims, which are key Coforge verticals.
- Revise SQL, Java or Python and testing concepts for the technical interview.
- Know that Coforge was earlier called NIIT Technologies and be able to describe its industry-focused strategy.
- Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
- Practise the hard questions on this page aloud; Coforge interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Coforge Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + reasoning); Technical MCQs or coding test; Technical interview; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Coforge Data Analytics interview?
This practice set leans on Python for Analysis (6), Data Modelling & Warehousing (4), Excel (4), Data Cleaning (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Coforge interviewers ask you to walk through real work.
Are these the exact questions asked at Coforge?
No. These are curated practice questions that match the skills and difficulty usually tested in it services interviews like Coforge's. They are not a leaked or verified list of past questions.
Where does Coforge hire Data Analytics professionals in India?
Coforge has large teams in Noida, Greater Noida, Bengaluru, Pune, Hyderabad. Openings and locations change often, so check the Coforge careers page and filter by city and experience.
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