SQL vs Python for data analysts and which to learn first

The practical answer to SQL vs Python for data analysts is SQL first, then Python. foundit's December 2025 Hiring Snapshot ranks Python (22% share) above SQL/Databases (18%) for 0-3 years of experience, across all roles. LinkedIn's 2026 India data and analytics cluster, via Storyboard18, names querying and database optimisation, which in practice are mostly SQL work. The order is a judgement, not a ranking from foundit's table.

What does each tool do in a data analyst's week?

Most business data sits in databases, so querying is usually the first step of any analysis. SQL is the language for that step: an analyst writes queries to select rows, filter them, join tables together and summarise the result into totals, rates and rankings. Python is a general-purpose language that analysts reach for when a spreadsheet or a single query stops being enough, for cleaning messy files, reshaping data, running statistical tests and automating a report that must be rebuilt every week.

The market data splits the work in a similar way. LinkedIn's Skills on the Rise 2026 for India, as reported by Storyboard18 in February 2026, puts querying, database optimisation, data storytelling and data-driven decision-making in one data and analytics cluster. Two of the four are database skills and two concern what is done with the result.

The same coverage lists prompt engineering, workflow automation, LLMOps, AutoML and API integration among the top rising skills nationally, so expectations are growing on top of the base.

Which one appears more in early-career job postings?

foundit's early-career table gives the clearest skill ranking.

In December 2025, foundit's Hiring Snapshot listed Python (22% share), SQL/Databases (18%), Java (16%), Cloud (12%) and Data Analysis (10%) as the top skills for 0-3 years of experience. The shares are foundit's own measure across the skills it tracks for that band, not the share of jobs asking for each skill, and the presence of Java and cloud suggests the band includes software roles. Python's lead therefore says more about software and AI hiring than about analyst work.

Entry-level AI roles tell a similar story. foundit reported that roles needing 0-3 years of experience made up 18% of AI jobs in 2025 and grew 28% year on year, with hiring focused on foundational skills such as Python, SQL and ML basics. CXOToday's coverage of a NASSCOM and Indeed report from May 2026 lists analytical skills, Microsoft Excel and Python among the skills in demand for internships, which it says have risen 103% in postings over three years.

The table sets the early-career skill signals side by side, with what each one measures.

Do data analyst interviews test hard SQL and algorithm-style Python?

The mix of questions differs between employers. Surveys show where employers put the weight.

In LinkedIn's Skills on the Rise 2026 for India, as reported by Storyboard18 on 24 February 2026, the data and analytics cluster covers querying, database optimisation, data storytelling and data-driven decision-making, so querying sits beside the business-facing skills and not apart from them.

Indian employers point the same way. In WEF's India economy profile, 79% of surveyed employers list analytical thinking as a core skill of 2025, and the NASSCOM-Deloitte compensation survey for 2025 puts strategic thinking and analytical skills among the top skills sought.

A reasonable inference, not a surveyed fact: a role hired to answer business questions with data is more likely to be tested on that than on puzzle-style coding problems. A fitting preparation is to write queries against realistic tables of orders, customers or sessions, to explain what a metric counts and what it leaves out, and to say what a result would change in a decision. Sample questions for freshers are a closer rehearsal than weeks of puzzle practice.

How much SQL is enough, and when does Python start to matter?

A workable target for a first analyst role is comfort with joins across several tables, grouping and aggregation, subqueries and common table expressions, and window functions such as ranking and running totals. The common confusion between the language and the database engines that run it is untangled in a post on SQL vs MySQL.

Python starts to matter when the work stops fitting in a query or a spreadsheet: files too messy to clean by hand, analyses that repeat every week, statistical tests, and first steps towards modelling. The market data leans the same way.

In its 2025 figures, foundit found machine learning in 34% of AI job postings (up 29% year on year), generative AI and LLMs in 22% (up 58%), data science and advanced analytics in 18% (up 24%) and MLOps in 10% (up 42%), although those shares cover AI-tagged postings only. CXOToday's account of the NASSCOM and Indeed report lists AI, Python, machine learning, Azure and TensorFlow as the top skills in tech AI roles. The further a role moves from reporting towards modelling, the more weight Python carries.

Some candidates are hired with little Python, and some analyst roles never need it. The early-career tables still put Python first, though, so skipping it entirely narrows the choice of roles.

Where do Excel, Sheets and BI tools fit around SQL and Python?

Spreadsheets and BI tools sit at the two ends of the work. Spreadsheets remain the common language of business teams, and Excel appears in CXOToday's list of internship skills. BI tools carry the output to decision-makers: LinkedIn's Skills on the Rise 2026 for India, as reported by Storyboard18, places data storytelling in the same cluster as querying, and NASSCOM and Indeed's skills analysis, as covered by CXOToday in May 2026, includes data analytics and visualisation among the top skills across industries.

A stack serves better than any single tool: a spreadsheet for checking, SQL for reaching the data, a BI tool for presenting it and Python for the heavier work.

In what order should the skills be learned?

One sensible sequence, again a judgement and not a surveyed finding, starts with spreadsheets, moves to SQL, adds statistics for decisions and a BI tool, and brings in Python after that, with AI-assisted workflows on top. Each step leaves something a hiring manager can see: a cleaned workbook, a set of queries on a public dataset, a dashboard, an automated report. A post on moving from commerce into analytics applies the same order to students without a technical degree, and a post on switching from a non-tech job applies it to working people.

AI tools sit on top of that stack, not instead of it. NASSCOM's 2026 AI-Native Talent Index scored India's early-career technology talent 62 and found over 90% of it AI-native or AI-proficient, yet NASSCOM added that sustained investment in technical depth, mentorship and independent judgment remains essential.

WEF's India profile reports that 94% of surveyed employers expect AI and big data to be skills of increasing use by 2030. An assistant can draft a query in seconds, but a candidate still has to notice when a join has duplicated rows or a filter has dropped half the customers, which is the reason to learn SQL properly before leaning on a tool.

A hypothetical fresher in Indore could spend the first stretch on a spreadsheet project, move to SQL on a public retail dataset, add a dashboard, and bring in Python when the first repeated task appears. The demand picture behind these choices is laid out in a post on analyst demand by sector, GCC and city, and a comparison of data analytics and AI/ML careers helps when the choice is between analytics and machine learning.

Where a structured programme fits

Some candidates prefer a taught sequence. Analytics in the Age of AI is a live online programme by "Vishlesan i-Hub Foundation, IIT Patna", with Masai as its ed-tech and implementation partner and an optional campus immersion at IIT Patna. It covers SQL, Python, BI tools, applied machine learning, generative AI workflows and agentic analytics through hands-on projects and a capstone, and its modules include SQL & Working with Real Data and Python for Analytics.

Frequently asked questions

Should a beginner learn SQL or Python first for data analytics?

SQL first, for most analyst goals. Querying is the step that reaches the data, and LinkedIn's 2026 India data and analytics cluster, as reported by Storyboard18, names querying and database optimisation, which in practice are mostly SQL work. Python follows when work needs cleaning at scale, automation or modelling. foundit's December 2025 table puts Python (22%) ahead of SQL (18%), but it is not limited to analyst roles.

Do data analysts need Python?

Not for every role, but it is widely listed. Python tops foundit's December 2025 early-career skills table at a 22% share, appears in CXOToday's list of internship skills from a NASSCOM and Indeed report, and sits among the top skills named for tech AI roles. Candidates should check the postings they want.

How much SQL does a data analyst need?

Enough to answer business questions without help: joins across several tables, grouping and aggregation, subqueries, common table expressions and window functions. The target is practical. Write the query, check the result against a known total, and explain what it means for the decision at hand.

Is Excel enough to become a data analyst?

The evidence points to more than Excel. CXOToday's coverage of a NASSCOM and Indeed report lists Microsoft Excel among internship skills, but foundit's early-career table puts Python and SQL/Databases at the top, and LinkedIn's India data and analytics cluster centres on querying and database optimisation. No verified share of postings asks for Excel alone, so Excel works best as the first step in a longer stack.

Will AI tools make SQL and Python unnecessary for analysts?

The surveys do not show that. NASSCOM's 2026 AI-Native Talent Index says technical depth and independent judgment remain essential, and its October 2026 study of 200 Indian enterprises found the majority require 100% human approval before any agentic action. An assistant can draft code, but someone has to read and check it, which still takes SQL and Python.

Next step

The Analytics in the Age of AI, Vishlesan i-Hub IIT Patna page sets out the curriculum and how to apply.