LinkedIn Data Analytics Interview Questions & Answers

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

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.

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

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

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.

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

Joining a table to itself with aliases, useful for hierarchies such as employees and managers or comparing rows within the same table.

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.

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.

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.

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

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.

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.

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

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

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

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

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.

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

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

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

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

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

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

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.

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.

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

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.

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.

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

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

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

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.

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

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

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

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.

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

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

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.

It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.

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 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.

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

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 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.

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

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

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

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

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

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

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

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

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

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.

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.

Fix the root cause when possible, or wrap formulas with IFERROR or IFNA to return a clear fallback value.

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

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

Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.

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

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.

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.

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

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

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

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

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