Data Analytics Interview Questions for Principal Roles (₹25–30 LPA)

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

Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.

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 ...).

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.

A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.

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

MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.

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.

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

SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.

Check histograms and skew(), then apply log, square-root or Box-Cox transforms, or use medians and robust statistics.

Line for trends, bar for comparisons, scatter for relationships, histogram for distributions and pie only for few parts of a whole.

Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.

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.

Indexes speed up lookups by keeping sorted pointers. They slow writes and use space, so index columns used in filters and joins, not every column.

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.

Find each user's first activity date as a cohort, join to later activity, and compute the share of each cohort active in day, week or month N.

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

INNER JOIN returns only matching rows. LEFT JOIN returns every row from the left table and NULLs where the right table has no match — e.g. all customers, including those with no orders.

Pandas for data handling, NumPy for numerics, Matplotlib and Seaborn for charts, and openpyxl or SQLAlchemy for files and databases.

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

Check data pipelines first, then segment by channel, region, product and device, compare to the same period last year, and look for launches, outages or campaigns.

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

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

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.

Bookmarks save a report view state for navigation; drill-through jumps to a detail page filtered to the item you picked.

It depends on margin of error, confidence level and expected variability; larger samples give smaller errors with diminishing returns.

SUMIFS adds values that meet several conditions; COUNTIFS counts rows that meet several conditions. Example: =SUMIFS(Sales, Region, "North", Month, "Jan")

Use RFM scoring or clustering on behaviour and value, validate segments with business teams and attach an action to each segment.

Pull data with SQL or APIs, clean and aggregate with Pandas, build charts and an Excel or PDF output, and schedule it with cron or Airflow with logging and alerts.

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

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

A pivot table summarises large data by dragging fields into rows, columns, values and filters — for example total sales by region and month — without writing formulas.

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

Relative references change when copied (A1); absolute references stay fixed ($A$1); mixed references lock only the row or column (A$1 or $A1).

Import loads data into memory for fast visuals; DirectQuery queries the source live, giving fresh data but slower performance and some DAX limits.

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

Use TRIM, CLEAN, UPPER or PROPER, Text to Columns, Flash Fill, SUBSTITUTE, and Power Query for repeatable cleaning steps.

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

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.

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.

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.

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

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.

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

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.

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.

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

DELETE removes chosen rows and can be rolled back; TRUNCATE empties the table quickly; DROP removes the table structure itself.

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).

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

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

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

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.

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.

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

Organising tables to reduce redundancy and anomalies by splitting data into related tables, commonly up to third normal form.

SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.

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

Start from the business questions, show 4–6 top KPIs with trend vs target, allow drill-downs, keep colours consistent, remove clutter, add time/region filters and give one clear insight per visual.

A bridge that lets the Service refresh data from on-premises sources securely on a schedule.

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

You assume no effect (null), collect evidence and decide whether the data is surprising enough to reject that assumption at a chosen significance level.

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

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

An ETL tool inside Excel for importing, cleaning and shaping data with recorded steps that refresh automatically when the source changes.

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

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.

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.

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.

A report is multi-page and interactive from one dataset; a dashboard is a single-page canvas of pinned tiles from many reports in the Service.

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

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