Paytm Data Analytics Interview Questions & Answers

Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.

df['sales'].rolling(window=7).mean() gives a 7-period moving average, useful to smooth daily noise.

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.

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

It evaluates an expression after modifying the filter context, for example CALCULATE(SUM(Sales[Amount]), Region[Name] = "North").

A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.

COUNT counts numbers, COUNTA counts non-empty cells and COUNTIF counts cells meeting one condition; COUNTIFS handles several conditions.

Use XLOOKUP or COUNTIF to flag missing items in each list, or merge them in Power Query with a full outer join and filter unmatched rows.

VLOOKUP only looks to the right and breaks when columns are inserted. INDEX-MATCH looks in any direction and is robust. XLOOKUP is the modern option with exact match by default and left lookups.

Compare with a control group or use difference-in-differences, control for seasonality, and measure incremental sales against campaign cost.

df = pd.read_csv('file.csv'); then head(), info(), describe() and isna().sum() for a quick health check.

Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.

A subquery is inline; a CTE is a named, readable block for one statement; a temp table is physically stored for the session and can be indexed and reused.

Aggregate revenue by month, use LAG(revenue) OVER (ORDER BY month), then (revenue - prev) / prev x 100.

A clustered index defines the physical order of rows (one per table); a non-clustered index is a separate structure with pointers to the rows.

df['date'] = pd.to_datetime(df['date']); df['amount'] = pd.to_numeric(df['amount'], errors='coerce'); astype('category') for repeated strings.

Visually with a box plot, with the IQR rule (below Q1 − 1.5×IQR or above Q3 + 1.5×IQR) or a z-score above 3. Then decide with the business whether to cap, remove or keep them.

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.

Pick metrics tied to a business goal that are measurable, actionable and owned by someone, and pair a lagging metric with a leading one.

pd.merge(df1, df2, left_on='cust_id', right_on='id', how='left') joins on differently named keys.

Salaries are right-skewed, so the median better represents the typical employee while the mean is pulled up by a few high earners.

Slicers are on-page visual filters users click; filters can be set at visual, page or report level and may be hidden from users.

A Microsoft BI tool made of Power BI Desktop for building reports, the Service for sharing, and Mobile; it connects, models and visualises data.

A Common Table Expression is a named temporary result defined with WITH that makes complex queries readable and allows recursion.

Detect with IQR or z-score, then cap with clip, remove, transform with log, or keep and flag them depending on whether they are errors or real events.

Create an engine with SQLAlchemy or a driver such as psycopg2, then run pd.read_sql(query, engine) to load results into a DataFrame.

Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.

SELECT order_date, SUM(amount) OVER (ORDER BY order_date) AS running_total FROM sales;

The population is the whole group of interest; a sample is a subset used to estimate population values.

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.

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

UNION removes duplicate rows and sorts internally; UNION ALL keeps every row and is faster.

Track users through steps (visit → product view → cart → payment), compute conversion between each step, find the biggest drop, segment by device and source, and suggest fixes to test.

IF returns one result if a test is true and another if false. Nest IFs for multiple outcomes, or use IFS for cleaner formulas.

Mean for symmetric data without outliers, median for skewed data (salaries, house prices), mode for categories (the most common city).

A tool that summarises large tables by dragging fields into rows, columns, values and filters, so you can slice sales by region or month without writing formulas.

DAX is the formula language for Power BI models. It works on columns and tables with filter context rather than on individual cells.

A result can be statistically significant yet too small to matter; always report effect size and business impact together with the p-value.

A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.

CALCULATE evaluates an expression in a modified filter context, e.g. CALCULATE([Total Sales], Region[Name] = "North"). It is the key function for time intelligence and comparisons.

DAU/MAU, conversion rate, average order value, cart abandonment, retention/churn, CAC, LTV, return rate and NPS.

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

LEFT JOIN customers to orders and keep rows where order_id IS NULL, or use NOT EXISTS, which also handles NULLs safely.

A primary key uniquely identifies each row in its table; a foreign key references another table's primary key to enforce relationships.

It automatically formats cells based on rules such as colour scales, data bars or highlighting values above a target, making patterns visible at a glance.

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

Matplotlib is the low-level plotting library with full control; Seaborn builds on it with attractive statistical charts and simpler syntax.

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).