Grant Thornton Bharat Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Grant Thornton Bharat hiring for Data Analytics roles
Grant Thornton Bharat is the Indian member firm of the Grant Thornton global network of audit, tax and advisory firms. It provides assurance, tax, risk advisory, deals, forensic and digital consulting services to businesses and government organisations. In India it hires for audit, risk consulting, cybersecurity, data analytics and technology roles, with offices in metros and several tier-2 cities.
For Data Analytics roles, this Grant Thornton Bharat practice set focuses most on Excel (7), Statistics and Case Studies (6), Data Cleaning (5), Web & Marketing Analytics (4). It has 7 easy, 27 medium, 6 hard questions, so you can start easy and work up to the harder ones.
Grant Thornton Bharat Data Analytics interview process
Resume screening or online assessment
Technical interview
Managerial round
Partner or HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for Grant Thornton BharatStart practicing
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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.
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.
04
Write an Excel formula to put invoices into ageing buckets (0-30, 31-60, 61-90, 90+ days).
-- C2 = invoice date, TODAY() = as-of date
=LET(age, TODAY()-C2,
IFS(age<=30, "0-30",
age<=60, "31-60",
age<=90, "61-90",
TRUE, "90+"))
-- then a PivotTable with bucket in rows and SUM of amount gives the ageing report
05
How do you merge two DataFrames on different column names?
Clarify the goal first: profit, cash or market share. Build a store-level dataset with revenue, contribution margin after rent and staff, trend over 24 months, footfall, catchment demographics and cannibalisation with nearby stores. Separate structural losers from stores that are new or temporarily disrupted. Estimate how much revenue would shift to nearby stores or online if a store closes. Present a shortlist with scenarios and risks, not a single 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.
Peeking means checking the p-value repeatedly during a test and stopping as soon as it drops below 0.05. Each look is another chance for random noise to look significant, so the real false positive rate can rise from 5% to 20% or more. Fix it by fixing the sample size in advance and analysing once, or by using methods built for continuous monitoring, such as sequential testing or always-valid confidence intervals.
10
Your event logs store timestamps in UTC, but the business reports daily sales in IST, and late-night orders land on the wrong day. How do you fix it?
Convert timestamps to the business time zone before deriving the date. IST is UTC plus 5 hours 30 minutes, so an order at 20:00 UTC belongs to the next day in IST. In SQL use AT TIME ZONE or CONVERT_TZ, and in pandas use tz_localize('UTC') followed by tz_convert('Asia/Kolkata'). Store raw UTC, add a local date column in the model, and document which one each report uses.
11
Write pandas code to fill missing values in a column with the median of each group.
# 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())
12
Organic search traffic fell 40% overnight. How do you investigate?
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.
13
Tell me about a time you found an error in someone else's spreadsheet.
Situation: a teammate's incentive calculation sheet was about to go to payroll. Task: I was asked to review it. Action: I noticed totals did not match the sales extract, traced it to a SUM range that stopped at row 500 while data had grown to 640 rows, and told the teammate privately with the fix. Result: we converted the data to an Excel Table so ranges expand, and payouts went out correctly.
14
Your findings deck has 30 slides and you have 10 minutes with leadership. How do you cut it down?
Write the single key message first, then keep only slides that directly support it, usually three to five: the headline finding, the main driver, the impact in money, and the recommendation with next steps. Merge related charts, use action titles, and move everything else to an appendix for questions. Rehearse to fit in about seven minutes, leaving time for discussion, because the discussion is where decisions are made.
15
Write Python code to extract UTM source, medium and campaign from a column of landing page URLs.
Drag Order Date to Columns as a continuous or discrete month, put Sales on Rows, then drag Profit Ratio to Rows next to it. Right-click the second pill and choose Dual Axis. On the Marks card set Sales to Bar and Profit Ratio to Line. Decide whether to synchronise axes: do not synchronise when units differ, like rupees and percentage. Label the axes clearly so readers know which is which.
A z-score tells how many standard deviations a value is from the mean: z = (x minus mean) divided by standard deviation. A delivery time with z = 2.5 is unusually slow. Analysts use z-scores to flag outliers, often beyond plus or minus 3, to compare values on different scales, such as marks in different exams, and to look up probabilities when data is roughly normal.
18
Tell me about a time you worked with messy data under a tight deadline.
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.
19
What is the difference between a view and a materialised view?
Common dimensions are accuracy, values reflect reality; completeness, required fields are present; consistency, the same fact matches across systems; validity, values follow formats and rules such as a 6-digit pin code; uniqueness, no unwanted duplicates; and timeliness, data is fresh enough for its use. Measuring these with simple checks, like percent missing or duplicate rate, turns vague complaints about bad data into fixable issues.
21
What is the difference between a table and a normal range?
Create an integer parameter Top N with a range of 5 to 50 and show its control. Drag Customer to the Filters shelf, choose the Top tab, select By field, and set it to top Top N by SUM(Sales). Alternatively create a set based on the parameter so you can show Top N versus Others in one chart. If the Top N must respect a category filter, add that category filter to context.
A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).
25
Why would you publish a Tableau data source separately instead of embedding it in each workbook?
A published data source lives on Tableau Server or Cloud and can be reused by many workbooks. Calculations, joins, field names and extract refresh schedules are defined once, so every report uses the same revenue logic and the database is queried once per refresh instead of once per workbook. You can certify it and set permissions. Embedded sources are fine for a quick personal analysis but drift over time.
Filter actions filter other sheets when a user selects or hovers on a mark, such as clicking a region to filter a product table. Highlight actions emphasise related marks without removing others. URL actions open a web page with field values in the link. Go to Sheet navigates to another dashboard. Parameter and set actions update a parameter or set based on selection, enabling advanced interactions like dynamic drill-downs and comparisons.
LAMBDA lets you write a custom, reusable function with formulas only, no VBA. You define it, for example =LAMBDA(price, rate, price*(1+rate)), and save it in the Name Manager as AddGST, then call =AddGST(B2, 18%) anywhere. Helper functions like MAP, REDUCE, SCAN and BYROW pair with it for array logic. Use it when a complex calculation repeats across a workbook, so the logic lives in one tested place.
30
What is the difference between loc and iloc in pandas?
loc selects by labels: row index labels and column names, for example df.loc[df['city'] == 'Pune', ['name', 'sales']]. iloc selects by integer position, for example df.iloc[0:5, 0:3] for the first five rows and three columns. With loc, slices include the end label; with iloc, the end position is excluded like normal Python. Use loc for readable filtering and iloc when position matters.
31
How do you write a good hypothesis for an experiment?
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.
32
Tell me about a time a dashboard you built changed a business decision.
Situation: our operations team assumed late deliveries were evenly spread. Task: I built a delivery performance dashboard. Action: I added a breakdown by courier partner and pin code cluster with a weekly trend, and reviewed it with the operations manager. Result: it showed one partner caused 60 percent of delays in two zones. The company shifted those zones to another partner, and on-time delivery rose from 84 to 92 percent in two months.
33
Analytics shows 1,820 purchases last month but the order database shows 2,150. Which is right, and how do you explain the gap?
The order database is the source of truth for counts and revenue. Analytics usually undercounts because of ad blockers, declined cookie consent, users closing the page before the confirmation tag fires, and payment redirects that break sessions. It can also overcount from duplicate tags on page refresh. Quantify the gap per month; if it is stable around 15%, use analytics for channel mix and trends and the database for totals.
34
Your workbook has 200,000 rows and hundreds of VLOOKUPs and takes minutes to recalculate. What do you do?
First switch calculation to manual to work safely, then find the cause. Replace whole-column references with Tables, swap repeated VLOOKUPs for one XLOOKUP or INDEX-MATCH helper column, and remove volatile functions like OFFSET, INDIRECT and TODAY used in many cells. Better still, move the joins into Power Query or the Data Model so lookups happen once at refresh. Finally paste values for historic data that never changes and keep formulas only where needed.
Do not delete them blindly. First find out what they mean: often they are returns, cancellations or credit notes, which are valid business events. Check with the source team or a documentation note. If they are returns, keep them but report gross sales, returns and net sales separately. If some are clearly entry errors, such as a negative quantity on a new order, flag and correct them and log the rule.
Row context evaluates one row at a time (calculated columns); filter context is the set of filters from slicers, visuals and relationships that a measure sees.
38
Write pandas code to compute month-on-month percentage change and cumulative sales for each store.
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
Incrementality testing measures sales that happened because of a campaign, compared with what would have happened anyway. You hold out a random group of users or a set of matched cities from the campaign and compare their conversions with the exposed group. Attribution tools often over-credit channels like retargeting and brand search, which reach people already likely to buy, so incrementality tests show the true lift and the true cost per incremental sale.
40
A file has dates in mixed formats, some dd/mm/yyyy and some mm/dd/yyyy. How do you handle it?
Find out which system or region produced each format, since a source column often explains it. Values with a day above 12 reveal the format unambiguously. Parse with an explicit format per source, for example pd.to_datetime(col, format='%d/%m/%Y', errors='coerce'), rather than letting the tool guess. Check failed or suspicious parses, such as future dates, and ask the source team to send ISO format yyyy-mm-dd going forward.
Tips to crack the Grant Thornton Bharat Data Analytics interview
Revise accounting and audit basics or IT risk concepts, depending on the service line you are applying to.
For technology and risk roles, learn basics of internal controls, IT general controls, cybersecurity frameworks and data analysis in Excel.
Prepare to explain why you prefer advisory or audit work and how you handle detail-oriented tasks under tight deadlines.
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; Grant Thornton Bharat interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Grant Thornton Bharat Data Analytics interview?
Candidates usually go through 4 stages: Resume screening or online assessment; Technical interview; Managerial round; Partner or 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 Grant Thornton Bharat Data Analytics interview?
This practice set leans on Excel (7), Statistics and Case Studies (6), Data Cleaning (5), Web & Marketing Analytics (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Grant Thornton Bharat interviewers ask you to walk through real work.
Are these the exact questions asked at Grant Thornton Bharat?
No. These are curated practice questions that match the skills and difficulty usually tested in consulting interviews like Grant Thornton Bharat's. They are not a leaked or verified list of past questions.
Where does Grant Thornton Bharat hire Data Analytics professionals in India?
Grant Thornton Bharat has large teams in Gurugram, Mumbai, Bengaluru, Kolkata, Chandigarh, Kochi. Openings and locations change often, so check the Grant Thornton Bharat careers page and filter by city and experience.