Meesho Data Analytics Interview Questions & Answers

Understand why it is missing, consider imputation or a missing flag, test sensitivity, and talk to data owners about fixing collection if it is systematic.

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 ...).

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.

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

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.

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

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.

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

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

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

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

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.

Read the execution plan, add or fix indexes, select only needed columns, filter early, avoid functions on indexed columns, and replace correlated subqueries with joins.

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

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

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.

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

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 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.

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

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).

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

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.

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 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.

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

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

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

Read in chunks with chunksize, load only needed columns, set efficient dtypes or switch to Dask, Polars or a database.

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.

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.

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.

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

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.

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

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.

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

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

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

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.

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

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

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

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

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

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.

Rules that restrict data per user role, for example sales managers seeing only their region, defined with DAX filters and assigned in the Service.

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.

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

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.

Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.