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TCS · Data Analyst

TCS Data Analyst Interview Questions (2026): process, questions and answers

Updated 21 October 2026 · prep guide

The TCS Data Analyst interview usually focuses on SQL, Excel, Power BI or Tableau, basic statistics, data cleaning, and business problem solving. Use this page to practice short, clear answers and to prepare for common technical and scenario questions that fit large IT services and consulting work.

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How the TCS Data Analyst interview usually works

  1. Resume screening: Candidates commonly report an initial screen based on their resume, project work, and skills listed in SQL, reporting tools, and analytics. Be ready to explain every tool, project, and metric you mention.
  2. Technical discussion: The technical round often covers SQL joins, window functions, aggregation, Excel formulas, dashboard design, and basic statistics. Expect questions that check both syntax and whether you can use the concept in a business setting.
  3. Case and scenario round: You may be asked to interpret a business problem, clean messy data, or suggest a KPI for a reporting dashboard. Interviewers usually want a structured approach, not a perfect final answer.
  4. HR and communication: The last stage often checks communication, teamwork, adaptability, and why you want the role. Keep answers simple, professional, and consistent with your resume.

Technical questions and answers

What is the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN?

An INNER JOIN returns only matching rows from both tables. A LEFT JOIN returns all rows from the left table and matching rows from the right table, with NULL where there is no match. A FULL OUTER JOIN returns all rows from both tables, matching where possible and using NULL for missing matches. I would choose based on whether I need only matched data or also unmatched records for analysis.

When would you use a window function instead of GROUP BY?

Use a window function when you want to calculate something without collapsing rows. For example, SUM(sales) OVER (PARTITION BY region) gives regional total sales for each row, while GROUP BY region returns one row per region. Window functions are useful for running totals, rankings, and comparing each record to its group, while keeping the detailed data visible.

How do you find the second highest salary in SQL?

One common way is to use DENSE_RANK() or ROW_NUMBER() over salary in descending order. For example, SELECT salary FROM (SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk FROM emp) t WHERE rnk = 2;. DENSE_RANK() is better if duplicate salaries exist and you want the second distinct salary.

What is the difference between WHERE and HAVING?

WHERE filters rows before aggregation, while HAVING filters groups after aggregation. For example, use WHERE status = 'Closed' to limit rows first, and HAVING COUNT(*) > 10 to keep only groups with more than 10 records. In interviews, I would mention that WHERE works on row-level conditions and HAVING works with aggregate conditions.

How do you handle missing values in a dataset?

First, I check how much data is missing and whether the missing pattern looks random or meaningful. Then I decide whether to remove rows, fill values with median, mean, mode, or a business rule, or flag them as a separate category. I avoid blindly filling everything, because the right approach depends on the variable, the business use case, and the impact on analysis.

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What is the difference between a mean, median, and mode?

The mean is the average, the median is the middle value after sorting, and the mode is the most frequent value. The mean is sensitive to outliers, while the median is more robust when data is skewed. In business analysis, I would use the median for salary or income data if there are extreme values, and the mode for categorical fields.

How do you create a KPI dashboard in Power BI or Tableau?

I first define the business goal and choose a small set of metrics that answer it clearly, such as revenue, conversion rate, or SLA compliance. Then I clean and model the data, create measures, and build visuals that are easy to read, with filters and drill-down where needed. A good dashboard should show trends, exceptions, and actions, not just many charts.

What is data normalization, and why is it useful?

Normalization usually means organizing data to reduce duplication and improve consistency. In SQL databases, it helps avoid repeated values and update issues. In analytics, people also use normalization to scale variables so they can be compared fairly. In an interview, I would clarify which meaning is being asked, then explain the one that fits the context.

How do you find duplicates and remove them in Excel or SQL?

In Excel, I can use Remove Duplicates, conditional formatting, or COUNTIF to identify repeated values. In SQL, I can use ROW_NUMBER() partitioned by the key columns, then keep only the first row. For example, DELETE FROM t WHERE id IN (...) is not enough by itself, because I need a safe rule for which duplicate row to keep.

How do you check whether two variables are related using statistics?

I would start by understanding the variable types. For numeric variables, I can use correlation to measure strength and direction, and for categorical relationships, I can use a contingency table or chi-square test. I would also look at the business meaning, because a statistically strong relationship is not always practically important.

Scenario questions

A client dashboard shows revenue dropped sharply this month. How would you investigate it?

I would break the problem into segments such as region, product, customer type, and channel to find where the drop started. Then I would check data quality, recent business changes, and whether the decline is due to volume, price, or missing records. I would present findings with visuals and clearly separate confirmed facts from assumptions.

You receive a messy Excel file with blanks, mixed date formats, and duplicate rows. What would you do?

I would first profile the file to see the scope of the issues. Then I would standardize dates, remove or flag duplicates, handle blanks based on business rules, and create a cleaned version with a log of changes. I would not change data silently, because traceability matters in reporting and client-facing work.

The business team wants a report, but their requirements are vague. How do you handle it?

I would ask focused questions about the goal, audience, frequency, and decisions the report should support. Then I would suggest a draft KPI list and one sample layout to confirm expectations early. The key is to turn vague requirements into measurable outputs, because that reduces rework and helps the client get a useful report faster.

HR and behavioural questions

Tell me about yourself.

Structure it as present, past, and future. Start with your current role or education, then mention relevant skills like SQL, Excel, and dashboards, and finish with why this role fits your interests. Sample answer: 'I have worked on data cleaning, reporting, and SQL analysis. I enjoy turning raw data into business insights, and I am looking for a role where I can build strong analytics skills and work with client-facing reporting.'

Why do you want to join TCS?

Show that you understand the company type and the role. You can mention large-scale client work, process discipline, and the chance to work on different business domains. Sample answer: 'I want to join TCS because it offers exposure to structured analytics work, large enterprise data, and cross-domain learning. I also value a role where I can improve my technical skills while working with teams that solve business problems for clients.'

Tell me about a time you solved a problem with data.

Use STAR: situation, task, action, result. Keep the story short and quantify the result if possible. Sample answer: 'In a project, we had inconsistent sales data across sheets. I cleaned the records, standardized fields, and built a summary report. That reduced manual checking and helped the team trust the numbers before sharing them with stakeholders.'

How do you handle pressure or tight deadlines?

Show that you stay organized and communicate early. Mention prioritizing tasks, confirming scope, and sharing progress updates. Sample answer: 'When deadlines are tight, I break the work into must-have and nice-to-have items, complete the core analysis first, and keep stakeholders informed if I see a risk. That helps me stay calm and deliver the most important part on time.'

Your last 48 hours before the interview

  1. Revise SQL joins, aggregates, subqueries, and window functions, and write 10 to 15 queries by hand.
  2. Review Excel basics, especially PivotTable, XLOOKUP or VLOOKUP, IF, COUNTIF, and data cleaning steps.
  3. Practice one Power BI or Tableau project explanation, including data source, model, measures, and key visuals.
  4. Refresh statistics basics, such as mean, median, standard deviation, correlation, and when to use each.
  5. Prepare two project stories and one problem-solving story using STAR, and say them out loud.
  6. Read your resume line by line and prepare to explain every skill, tool, and metric without hesitation.

Common mistakes to avoid

Frequently asked questions

What SQL questions are asked in a TCS Data Analyst interview?

Candidates commonly report questions on joins, GROUP BY, subqueries, HAVING, window functions, duplicates, and NULL handling. You may also be asked to write a query for the second highest value, top N records, or monthly trend analysis. The best preparation is to practice writing queries on paper and explaining them clearly.

Is Excel important for TCS Data Analyst roles?

Yes, Excel is often important because it is still widely used for cleaning, quick analysis, and reporting. Be ready to discuss formulas, PivotTable, charts, filtering, and basic data validation. Interviewers often want to know whether you can work efficiently with large spreadsheets and produce clean outputs.

Do I need Power BI or Tableau for the interview?

Usually, yes, at least at a basic level if the role mentions reporting or dashboarding. You should be able to explain how you connect data, create measures or calculated fields, choose visuals, and design a dashboard for business users. Even if you are stronger in one tool, know the concepts that transfer to the other.

How should I answer business case questions in this interview?

Use a simple structure: clarify the goal, identify the key metric, break the problem into segments, check data quality, and suggest next steps. Interviewers usually care more about your thinking process than a perfect answer. Speak in a logical order and connect your analysis to business impact.

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This is an independent preparation guide based on commonly reported interview patterns. The process varies by team, location and year. Interview AI Helper is not affiliated with TCS.

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