Ian Klosowicz
SQL first. It's not close. If you're trying to break into data analytics and you're deciding where to start, SQL is the answer. It shows up in more job postings, gets tested in more interviews, and gets used more in the actual day-to-day work than Python does at the entry level.
That's the direct answer. The rest of this post explains why, covers the situations where Python might belong first, and gives you a clear sequence for both.
SQL is the language of data. Every relational database speaks it. Every data warehouse — Snowflake, BigQuery, Redshift, SQL Server, PostgreSQL — runs on it. Analyst work is fundamentally about asking questions of data that lives in databases, and SQL is how you do that.
Python can query databases too, technically. In practice, no analytics team is writing pandas code to pull rows from a production database when SQL exists. SQL is faster to write, easier to read, and what every other analyst on the team is already using. Learning Python first and trying to use it as a SQL substitute is like learning to drive a manual transmission when everyone else on your team drives automatic. It works, but it's the wrong tool for the environment.
I learned SQL before Python. I got hired as an analyst without Python on my resume at all. The SQL I built up in those first months is what got me through technical screens, and it's what I use every single day in data engineering now — alongside Snowflake and Coalesce. Python came later and made sense faster because the data modeling foundation was already solid. That order matters.
Pull up 50 entry-level data analyst job postings and read the requirements sections. The pattern is consistent:
"Preferred" and "required" mean different things. Required means they filter you out without it. Preferred means a strong candidate without it still gets considered. SQL at the entry level is almost always in the required column. Python is mostly in the preferred column, with exceptions for more technical roles.
The market is telling you something. When a skill appears as required in 90% of postings and another appears as preferred in 30%, the order to learn them isn't ambiguous. Go where the hiring bar is highest first, because that's where you'll get filtered out if you skip it.
Beyond the job posting argument, SQL builds a specific kind of analytical thinking that makes everything else easier.
Relational data modeling. SQL forces you to understand how tables relate to each other, what joins do, and how data is structured in a real database. This mental model is foundational. It makes Power BI data modeling easier. It makes pandas merges make sense. It makes the whole data stack more legible. Python without that foundation produces analysts who can manipulate flat files but get lost the moment the data comes from multiple sources.
Direct interview testability. SQL gets tested live in technical interviews at a much higher rate than Python does for analyst roles. You'll be asked to write a query on the spot — usually a JOIN, a GROUP BY with a HAVING clause, a window function, or a CTE. The ability to write clean SQL under pressure is a direct hiring filter. Python is tested less often and less rigorously for analyst roles because it's less universally required.
Immediate job relevance. From day 1 in an analyst role, you're writing SQL. Pulling data for a report, verifying a number, answering an ad hoc question from a stakeholder — all of it runs through SQL. Python use in most analyst roles is more situational. SQL use is constant.
Faster feedback loop. SQL is easier to learn to a job-ready level than Python. The core concepts — SELECT, WHERE, GROUP BY, JOIN, ORDER BY — take days to grasp and weeks to get solid. Getting to a job-ready level in Python takes longer because the language is broader and the relevant libraries add surface area. You can be production-ready in SQL in 4 to 6 weeks of focused practice. Comparable Python fluency takes longer.
SQL first is the right answer for most people. There are genuine exceptions.
You're targeting data science, not data analytics. Data scientist roles require Python. The technical bar is different, the interview process tests different things, and the day-to-day work skews more toward modeling and less toward reporting. If data scientist is your actual goal, Python belongs in your preparation from the start.
You have a programming background already. If you've written code in any language before — Java, JavaScript, R, anything — Python picks up fast. A programmer can get to functional pandas fluency in 2 to 3 weeks. At that speed, the argument for SQL first weakens because the time cost of adding Python early is low. Get Python to a usable level quickly, then go deep on SQL.
The specific roles you're targeting require Python. Check your actual target postings, not analyst job postings in general. If you're going after product analyst roles at tech companies, marketing analytics at large digital businesses, or quantitative analyst roles in finance, Python may appear as a hard requirement more often. Let the postings you actually care about drive the sequence.
You already have solid SQL. If you're coming from a background that gave you real SQL experience — finance, operations, any role that involved querying databases — you may already be at a job-ready SQL level. In that case, Python is the gap to close, and starting there makes sense.
For someone starting from zero with the goal of landing an entry-level analyst role:
That sequence gets most people from zero to job-ready in 3 to 4 months. Trying to run SQL and Python in parallel stretches it to 6 to 9 months and produces weaker results in both.
The Analyst Hive program is built around exactly this order. SQL and the first BI project in Month 1, before any job search activity begins. The sequence is deliberate: skills in the wrong order slow the job search down, they don't speed it up.
The most common version of this mistake: someone reads that data analysts use SQL, Python, Power BI, Excel, and statistics. They decide to learn all of them at once. They spend 3 months spread thin across all 5. None of it goes deep enough to clear a technical screen. The resume lists everything. The interview exposes that nothing is solid.
The 125,000 analysts who follow me on LinkedIn ask this question constantly. The pattern from people who actually get hired is consistent every time: they went deep on one thing, built something real with it, and then moved to the next. The people who try to go wide first take longer and often stall.
Depth before breadth. SQL before Python. One finished project before the next skill. That's the sequence.
It feels slower because you're not "learning Python" yet. It's faster because you're building the foundation that makes everything else land correctly when you get to it.
Can Python replace SQL for data analysts?
Technically yes, practically no. Python can query databases and manipulate data without SQL. In a real analytics environment, that's not how teams operate. SQL is the standard language for database queries, it's what your teammates are using, it's what stakeholders expect, and it's what gets tested in interviews. Python and SQL work together — SQL to pull and filter, Python to transform and analyze — but SQL is the foundation, not a detail you can skip.
How long does it take to learn SQL well enough to get hired?
4 to 6 weeks of deliberate practice gets most people to a job-ready level. That means writing queries from scratch without looking up syntax, being comfortable with JOINs across 3 or more tables, and knowing when to use a CTE versus a subquery versus a window function. The SQL tested in entry-level analyst interviews isn't exotic — it's clean, correct, readable queries under mild time pressure. That level is reachable in under 2 months for most people.
Do I need Python if I'm good at SQL?
Not to get most entry-level analyst jobs. SQL fluency plus a BI project qualifies you for a large portion of the market. Python becomes worth adding when you're in a role and encounter problems that SQL can't solve cleanly, when a specific job you want requires it, or when you want to move toward more technical roles over time. Add it when it unlocks something specific, not as a checkbox during the initial job search.
Is R worth learning instead of Python?
R is used in academia, statistics-heavy research, and some quantitative finance roles. For general data analyst roles in business environments, Python is the more marketable choice — it appears more often in job postings, has broader library support, and transfers to adjacent roles like data engineering more readily. If you're targeting academic research or statistical roles specifically, R may be the better fit. For most people: SQL first, then Python over R.
What SQL concepts should I learn first?
Start with SELECT, WHERE, GROUP BY, ORDER BY, and aggregate functions (COUNT, SUM, AVG, MIN, MAX). Then JOINs — INNER, LEFT, and the logic behind them. Then subqueries. Then CTEs, which are cleaner than subqueries for complex logic. Then window functions: ROW_NUMBER, RANK, LAG, LEAD, and running totals with SUM OVER. That sequence covers the SQL tested in the vast majority of entry-level analyst interviews. Advanced topics like query optimization and indexing come later, in the job.
Should I learn SQL or Excel first?
SQL. Excel is useful and shows up in analyst work, but it's a tool for smaller-scale, manual analysis. SQL works at any data scale, is tested more rigorously in interviews, and is more foundational to the rest of the analyst skill stack. If you already know Excel reasonably well, skip to SQL immediately. If you've never used either, SQL is still the better starting point for the job market you're entering.
If you want a day-by-day plan through SQL, your first BI project, and the rest of the job search — in the right order — the Analyst Hive program is daily tasks, clear sequence, and built around what actually gets entry-level analysts hired.
