Data Analytics Interview Questions for Freshers (₹3–4 LPA)

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

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

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).

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

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

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

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

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

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.

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

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

Data → Data Validation restricts what can be entered in a cell (lists, number ranges, dates), reducing manual entry errors in shared sheets.

DAX functions like TOTALYTD, SAMEPERIODLASTYEAR and DATEADD compare periods; they need a proper continuous date table marked as a date table.

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

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 ...).

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

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.

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.

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

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.

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.

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 extracts and transforms data using recorded steps in M language, such as merging, unpivoting, changing types and removing errors, before loading to the model.

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

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

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

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.

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.

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

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

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

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

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

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.

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

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

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.

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

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

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

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.

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.

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 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.

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.

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

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.

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

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.

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

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.

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.

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

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

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

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

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

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.