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Data Analytics Interview Questions in Kolkata

36 practice questions with answers for Data Analytics interviews in Kolkata.

Data Analytics jobs in Kolkata

Kolkata has a steady IT-services and BFSI technology job market around Salt Lake Sector V and New Town. Many openings are with service companies and their delivery centres, with a focus on strong fundamentals.

Below are 36 questions that match the mix of service, product and domain employers hiring data analysts in Kolkata.

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What is the LET function in Excel and why would you use it?

ExcelMedium

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LET assigns names to intermediate results inside a single formula, for example =LET(sales, SUM(D2:D100), cost, SUM(E2:E100), (sales-cost)/sales). It makes long formulas readable and faster, because a repeated sub-calculation is evaluated once instead of several times. It is especially useful with dynamic arrays, where the same FILTER result might otherwise be recalculated three or four times in one nested formula.

Orders arrive in the warehouse with customer IDs that do not exist yet in the customer dimension. What do you do?

Data Modelling & WarehousingMedium

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These are late-arriving dimensions. Do not drop the orders, or revenue will be understated. Common practice is to insert a placeholder customer row with the natural key and 'Unknown' attributes, link the order to it, and update that row when full details arrive. Alternatively map to a single Unknown member with key -1. Also monitor how many such rows appear daily, because a spike usually means a broken feed.

What is the difference between an outlier and a data error?

Data CleaningMedium

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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.

What is the difference between a calculated field and a helper column in Excel?

ExcelMedium

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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.

Tell me about a time a dashboard you built changed a business decision.

Dashboards and KPIsMedium

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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.

What is the difference between a calculated table in DAX and a table created in Power Query?

Power BIMedium

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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.

You must present to the sales team that they missed their quarterly target by 18%. How do you handle it?

Data StorytellingMedium

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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.

What is a transaction and what do the ACID properties mean?

SQLMedium

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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.

RANK vs DENSE_RANK vs ROW_NUMBER?

SQLMedium

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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).

What is Power Query's role in Power BI?

Power BIMedium

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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.

What is a junk dimension?

Data Modelling & WarehousingHard

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A junk dimension combines several low-cardinality flags and indicators, such as payment type, is_gift, is_express and order channel, into one small dimension table. Each row is a unique combination of those values, and the fact table stores a single key to it. This keeps the fact table narrow and avoids many tiny dimensions or loose flag columns. It works best when the combinations are limited, typically a few hundred rows.

What is the difference between dimensions and measures in Tableau?

TableauEasy

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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.

Tell me about a time you disagreed with your manager about how to measure something.

Data StorytellingMedium

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Situation: my manager wanted to report average resolution time for support tickets. Task: I felt a few very old tickets distorted the average. Action: I prepared a quick comparison showing mean versus median and the percentage resolved within 24 hours, and explained why the median was more representative. Result: we agreed to show median and the 24-hour rate, keeping the mean in a tooltip, and the team stopped chasing misleading spikes.

What is Tableau and what are its main products?

TableauEasy

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Tableau is a visual analytics tool for building interactive charts and dashboards by dragging fields onto shelves. Tableau Desktop is used to author workbooks, Tableau Prep cleans and shapes data, Tableau Server and Tableau Cloud host and share dashboards with security and scheduled refresh, and Tableau Public is a free version where work is saved publicly. Analysts usually build in Desktop and publish to Server or Cloud.

How do you write a good hypothesis for an experiment?

A/B Testing & ExperimentsEasy

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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.

What is cohort analysis?

Statistics and Case StudiesMedium

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Grouping users by a shared start date or trait and tracking behaviour over time, for example monthly retention per signup month.

What is the difference between concat, merge and join in pandas?

Python for AnalysisEasy

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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.

Write a CREATE TABLE statement for a sales fact table linked to date, product and store dimensions.

Data Modelling & WarehousingEasy

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CREATE TABLE fact_sales ( date_key INT NOT NULL REFERENCES dim_date(date_key), product_key INT NOT NULL REFERENCES dim_product(product_key), store_key INT NOT NULL REFERENCES dim_store(store_key), invoice_no VARCHAR(20) NOT NULL, -- degenerate dimension quantity INT NOT NULL, net_amount DECIMAL(12,2) NOT NULL, discount_amount DECIMAL(12,2) DEFAULT 0 );

Your test increased conversion rate by 3% but average order value dropped by 5%. What do you recommend?

A/B Testing & ExperimentsMedium

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Look at the metric that reflects business value, such as revenue or margin per visitor, which combines both effects. Here revenue per visitor is roughly 1.03 times 0.95, about 2% lower, so the change may hurt. Check whether the drop is significant, which segments drive it, and whether new buyers are making smaller first orders that grow later. Without a strong reason, I would not ship it as is.

How do you remove duplicates in Excel?

ExcelEasy

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Data tab, Remove Duplicates, pick the columns to compare. For a non-destructive way use UNIQUE() or conditional formatting to highlight repeats first.

What is columnar storage and why do analytical warehouses use it?

Data Modelling & WarehousingMedium

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Columnar storage keeps all values of one column together on disk instead of storing full rows together. Analytical queries usually read a few columns across millions of rows, so the engine reads only those columns. Similar values sit side by side and compress very well, which cuts storage and scan cost. The trade-off is slower single-row inserts and updates, which is why OLTP databases stay row-based.

Site conversion rate rose from 2.0% to 2.4%, but total revenue stayed flat. What would you check?

Web & Marketing AnalyticsMedium

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Check the components: sessions may have fallen, for example after cutting paid campaigns, which removes low-intent traffic and lifts the rate without new sales. Average order value may have dropped because of discounts or a mix shift towards cheaper products. Also check whether conversion definitions or tracking changed. Revenue equals sessions times conversion rate times AOV, so break down the change across those three factors by channel.

What is the unit of randomisation in an experiment, and how do you choose it?

A/B Testing & ExperimentsMedium

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The unit of randomisation is what gets assigned to control or treatment: a user, a session, a device, a store or a city. Usually it should be the user, so one person always sees the same version. Session-level randomisation can show one user both versions and contaminate results. Your analysis unit should match it; if you randomise by user but analyse per page view, standard errors become too small.

Tell me about a time you migrated or rebuilt reports in a new BI tool.

TableauMedium

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Situation: our team moved 15 Excel and older BI reports into Tableau. Task: I owned the sales set. Action: I listed every metric with its definition, rebuilt the shared data source first, then the dashboards, and ran side-by-side number checks for two weeks with the old reports. I held short demos for users. Result: all reports went live with matching numbers, and refresh effort fell from a day to an automatic schedule.

How are NULLs handled in SQL?

SQLMedium

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NULL means unknown, so comparisons with = fail; use IS NULL, COALESCE for defaults and remember most aggregates ignore NULLs except COUNT(*).

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?

Data Modelling & WarehousingMedium

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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.

Why does correlation not imply causation? Give a business example.

Statistics and Case StudiesEasy

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Two variables can move together because of a third factor, reverse causation or pure chance. Example: stores with more staff have higher sales, but busy locations get more staff because they already sell more, so hiring alone may not raise sales. Ice-cream sales and AC sales both rise in summer because of temperature. To claim causation you need experiments, natural experiments or careful control of confounders.

Explain GROUP BY with an example.

SQLMedium

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It groups rows sharing values so aggregates run per group, for example SELECT region, SUM(sales) FROM orders GROUP BY region.

What is the difference between melt and pivot?

Python for AnalysisMedium

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melt converts wide data to long format; pivot converts long data to wide, and they are used to reshape data for analysis or charts.

What is a CTE and when would you use one?

SQLMedium

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A Common Table Expression (WITH name AS (...)) is a named temporary result used inside one query. Use it to break complex logic into readable steps or for recursive queries.

Write SQL to find the number of days between each customer's consecutive orders and their average gap.

SQLMedium

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WITH gaps AS ( SELECT customer_id, order_date, order_date - LAG(order_date) OVER ( PARTITION BY customer_id ORDER BY order_date) AS days_since_prev FROM orders ) SELECT customer_id, COUNT(*) AS orders, AVG(days_since_prev) AS avg_gap_days FROM gaps GROUP BY customer_id HAVING COUNT(*) > 1; -- order_date is DATE (PostgreSQL); use DATEDIFF in other databases

Tell me about a time a stakeholder rejected your first dashboard design.

TableauMedium

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Situation: I built a Tableau operations dashboard, and the warehouse head said it was too complex to use on the floor. Task: make it useful for them. Action: I watched how supervisors worked, learned they needed three numbers on a tablet every hour, and rebuilt it with large KPI tiles and one drill-down. Result: daily usage went from a handful of users to every shift supervisor within two weeks.

What is descriptive vs inferential statistics?

Statistics and Case StudiesEasy

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Descriptive statistics summarise the data you have; inferential statistics use a sample to draw conclusions about a larger population.

What is the difference between WHERE and HAVING?

SQLEasy

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WHERE filters rows before grouping. HAVING filters groups after GROUP BY and can use aggregates like SUM() or COUNT().

What is the difference between discrete and continuous fields in Tableau?

TableauEasy

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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.

What are vanity metrics?

Dashboards and KPIsEasy

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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.