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Skills for a data analyst resume: SQL, Excel, BI and more

Data analyst roles in India range from MIS and reporting jobs built on Excel to product analytics roles that expect SQL, Python and experimentation. Your skills section should show clearly which end of that range you are on, using the exact tool names that appear in job descriptions, such as SQL, Power BI, Tableau and pandas.

Below are skills grouped into SQL and databases, Excel, BI and visualisation, Python and R, statistics and experimentation, data engineering basics, business and domain skills, and communication. The final group shows how to turn those skills into experience bullets with a clear business result.

35 copy-ready examplesWritten for job seekers in India

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How to write it

Formula

Category label + specific tools and techniques, for example “SQL: joins, CTEs, window functions, query optimisation | BigQuery, PostgreSQL”.

  1. List SQL first for most analyst roles; it is the skill nearly every data analyst job description mentions.
  2. Name the BI tool the employer uses. If the post says Power BI, lead with Power BI even if you also know Tableau.
  3. Mention specific techniques, such as window functions, DAX or cohort analysis, to show depth.
  4. Link a portfolio such as Tableau Public or GitHub if you have one; it backs up the skills list.
  5. Include business skills like KPI definition and stakeholder communication, because analysts are judged on decisions, not just queries.

SQL and databases

  1. SQL: joins, CTEs, window functions (ROW_NUMBER, LAG), subqueries, aggregations

  2. Databases: MySQL, PostgreSQL, Microsoft SQL Server

  3. Cloud data warehouses: Google BigQuery, Snowflake, Amazon Redshift

  4. Query optimisation and reusable views for reporting

Excel and spreadsheets

  1. Advanced Excel: XLOOKUP, INDEX-MATCH, pivot tables, Power Query, conditional formatting

  2. Excel modelling: what-if analysis, Goal Seek, scenario tables

  3. Google Sheets: QUERY, ARRAYFORMULA, IMPORTRANGE

BI and visualisation

  1. Power BI: DAX measures, Power Query, star-schema data modelling, row-level security

  2. Tableau: calculated fields, LOD expressions, interactive dashboards, Tableau Public portfolio

  3. Looker Studio dashboards connected to GA4 and BigQuery

  4. Dashboard design: KPI selection, drill-downs, appropriate chart types

Python and R

  1. Python: pandas, NumPy, Matplotlib, Seaborn

  2. Jupyter notebooks for exploratory data analysis (EDA)

  3. Data cleaning: missing values, outliers, duplicates, data type fixes

  4. R (basic): dplyr, ggplot2

Statistics and experimentation

  1. Descriptive statistics, distributions and correlation

  2. Hypothesis testing: t-test, chi-square, p-values, confidence intervals

  3. A/B test design and analysis

  4. Regression analysis (linear and logistic)

  5. Cohort, funnel and retention analysis

Data engineering basics

  1. ETL concepts, data pipelines and scheduled refreshes

  2. dbt (basic) for transforming data in the warehouse

  3. Git for version control of SQL scripts and notebooks

Business and domain skills

  1. KPI definition and metric trees

  2. Product analytics: Mixpanel, Amplitude, GA4

  3. Domain knowledge: e-commerce, BFSI or supply chain metrics

  4. Requirement gathering with business stakeholders

Communication

  1. Data storytelling and presentations for senior leadership

  2. Writing clear analysis summaries and documentation

  3. Turning business questions into structured analysis plans

Bullets that prove a skill

  1. Built a Power BI sales dashboard for 12 regional managers, replacing a weekly manual Excel report.

  2. Wrote SQL with window functions to calculate 30-day repeat purchase rate by acquisition channel.

  3. Analysed an A/B test on the checkout page and presented a recommendation backed by confidence intervals.

  4. Cleaned and merged three years of order data in pandas, cutting monthly reporting time from two days to half a day.

  5. Built a cohort retention analysis that helped the marketing team shift spend towards channels with better retention.

Mistakes to avoid

  • Listing “Data Analysis” as a skill. It is the job title, not a skill; name the tools and techniques instead.
  • Claiming Python and machine learning with no project to show for it, when the role mainly needs SQL and BI.
  • Writing “Excel” without any detail, which hides real depth such as Power Query or modelling.
  • Ignoring business skills, so the resume reads like a list of tools with no sign of impact.
  • Linking a GitHub or Tableau Public profile that is empty or out of date.

Frequently asked questions

What are the top skills for a data analyst resume?

SQL, Excel and one BI tool such as Power BI or Tableau form the core for most data analyst roles in India. Python with pandas, basic statistics and A/B testing are increasingly expected for product and e-commerce analytics. Add business skills such as KPI definition and stakeholder communication, because analysts are hired to support decisions, not only to write queries.

Do I need Python for a data analyst job?

Not always. Many reporting and MIS analyst roles rely on SQL, Excel and Power BI. Product analytics, data science-adjacent and startup roles more often ask for Python. Check the job descriptions you are targeting: if Python appears in most of them, learn pandas well enough to clean data and run analysis, and show it in a project.

Should I list both Power BI and Tableau?

List both if you can build a working dashboard in each. Put the one the employer asks for first. If you know only one, that is fine; the concepts transfer, and recruiters generally care more about your SQL, data modelling and dashboard design than about which BI tool you used. A portfolio link helps more than listing extra tools.

How do freshers show data analyst skills without experience?

Build two or three projects using public datasets, such as sales, cricket or government open data, and describe them like work bullets: the question, the tools used and the finding. Publish dashboards on Tableau Public or code on GitHub and link them. Courses and certificates help, but a well-explained project is usually more convincing in interviews.

Where should statistics skills go on a data analyst resume?

Give them their own line in the skills section, such as “Statistics: hypothesis testing, confidence intervals, regression, A/B testing”. Then show at least one in action in a bullet, for example analysing an experiment or building a forecast. Statistics often separates analysts who report numbers from those who can explain whether a change is real.

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