HCLTech Data Analytics Interview Questions & Answers

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

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.

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

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

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

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.

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

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.

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.

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

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

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.

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.

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

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

A view stores a query and runs it each time; a materialised view stores the result physically and must be refreshed, making reads faster.

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

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

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

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.

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 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 ...).

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

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

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.

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

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.

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

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.

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

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.

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

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

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

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.

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

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.

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.

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.

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.

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.

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

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

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

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

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.

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

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

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.

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

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.

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

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

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.

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

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.

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

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

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

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

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

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

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).

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

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

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

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.

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