Ian Klosowicz
Most data analysts don't need Python to get their first job. SQL and a BI tool get the majority of entry-level candidates hired. Python becomes relevant once you're in a role and working on problems that SQL can't solve cleanly, or when you're targeting roles that explicitly list it as a requirement.
That's the short version. The longer version matters because the answer changes depending on which type of analyst role you're going after, which industry you're targeting, and where you are in the job search right now.
Pull up 50 entry-level data analyst job postings and look at what appears in the requirements section. SQL shows up in nearly all of them. A BI tool — Power BI or Tableau — shows up in most. Excel shows up frequently. Python shows up in some, often listed as "preferred" rather than "required."
That distribution reflects what analysts actually spend their time on at the entry level. Most of the work is querying databases, building reports, and answering business questions with structured data. SQL handles the querying. The BI tool handles the reporting. Python isn't in the loop for most of that work.
The roles where Python appears consistently as a hard requirement tend to be more technical: analytics engineer, data scientist, product analyst at a tech company, or any role that involves building models, automating pipelines, or working with unstructured data. Those roles exist and they pay well, but they're not where most people without a technical background start.
I broke into data without Python. I learned SQL, built BI projects, and got hired into an analyst role that never required me to write a line of Python. Working in data engineering now, I do use Python, but that came after the analyst foundation was already solid — not before it.
Python appears as a genuine requirement in these contexts:
Python appears as a "preferred" or "nice to have" in a much broader set of postings. That phrasing matters. "Preferred" means they'd take a strong candidate without it. "Required" means they won't move forward without it. Most entry-level postings that mention Python are using the preferred framing.
If you're reading a job posting and Python is listed as required, take it seriously. If it's listed as preferred alongside SQL and a BI tool, focus on being excellent at SQL and the BI tool first. Those carry more weight at the entry-level screen than Python basics do.
When analysts do use Python, the use cases tend to be specific. Understanding what it's actually for helps you decide whether your target roles need it.
Data manipulation and cleaning at scale. pandas is the primary library for this. When a dataset is too large or too messy for SQL or Excel to handle cleanly, Python handles the transformation. This comes up in roles where the data isn't already in a clean warehouse — it's arriving from APIs, scrapes, or flat files that need significant prep work before analysis.
Statistical analysis and modeling. scipy, statsmodels, and scikit-learn handle hypothesis testing, regression, and predictive modeling. These show up in roles that require going beyond descriptive analytics into inferential or predictive work. Most entry-level analyst roles don't require this.
Automation and scripting. Repetitive tasks — pulling data from an API on a schedule, reformatting files, sending automated reports — can be scripted in Python. This is genuinely useful in analytics workflows, but it's also not something that blocks hiring at the entry level.
Visualization beyond BI tools. matplotlib and seaborn produce custom charts that BI tools can't. This comes up in research, data science, and technical reporting contexts. Most business analytics roles use Power BI or Tableau for this and don't need custom Python visualization.
The through-line: Python fills gaps that SQL and BI tools leave. At the entry level, most of those gaps don't exist yet in the work you're doing. They show up as you move into more complex roles.
When Python does become relevant, it comes down to the handful of Python libraries analysts use, not the whole ecosystem.
The order most people get wrong: they try to learn SQL, Python, Power BI, Excel, and statistics all at once because they've read that analysts use all of these. None of it goes deep enough to be useful, the resume ends up listing everything at a surface level, and interviews expose the gaps immediately.
The order that actually works:
The 125,000 analysts who follow my content on LinkedIn ask about Python constantly. The pattern I see from people who actually get hired is consistent: the ones who go deep on SQL first move faster. The ones who try to spread across SQL, Python, and everything else simultaneously take longer and often stall.
Before adding Python, it is worth being fluent in the SQL analysts actually write on the job, which is what the majority of the work runs on.
There are situations where Python belongs earlier in the sequence:
You're targeting data science roles, not analyst roles. Data science requires Python. If your goal is a data scientist title rather than a data analyst title, Python is a core requirement, not an add-on. The skill stack is different and the hiring bar is higher on the technical side.
The specific roles you're applying to list Python as required. Check the postings. If 80% of your target roles require Python, it belongs in your preparation now, not later. Don't ignore what the market is telling you.
You have a programming background already. If you've written code before — in any language — Python picks up fast. A week or 2 of focused practice with pandas and basic scripting gets you to a level where you can honestly list it. At that point it's not a major time investment to add.
You're targeting tech company product analyst roles. Product analytics at tech companies tends to use Python more than traditional business analyst roles. If that's your target, treat Python as a requirement, not optional.
Outside those situations, finishing your SQL and BI foundation first is the better use of your time during a job search.
This comparison comes up constantly, and it's worth being direct about it.
SQL is not replaceable by Python for most analyst work. Python can do everything SQL can do, technically. In practice, querying a database with pandas instead of SQL is slower, harder to read, and not how data teams operate. SQL is the language of databases, and analyst work is database work. You will write SQL in almost every analyst role. Python is conditional on the role and the problem.
Python extends what's possible. It doesn't replace the foundation. The analysts I see trying to use Python to skip SQL learning almost always struggle once they're in a role, because the day-to-day work requires SQL fluency that Python practice doesn't build.
If you're trying to decide where to spend the next 4 weeks and you don't have strong SQL yet, SQL wins. No contest.
The Analyst Hive program sequences exactly this way: SQL and BI tool foundations in Month 1, before any job search activity starts. The sequencing is deliberate because skills in the wrong order slow the job search down, not speed it up.
Is Python required for entry-level data analyst jobs?
Not for most of them. SQL and a BI tool are the core requirements that appear in the majority of entry-level analyst postings. Python shows up in some, usually listed as preferred rather than required. If you're targeting general business analyst roles, Python is not the bottleneck. If you're targeting product analytics or data science-adjacent roles, it matters more and should be treated as a requirement.
Should I learn Python or SQL first?
SQL first, without question. SQL shows up in more analyst job postings, gets tested in more interviews, and gets used more in the day-to-day work than Python does at the entry level. Python extends what SQL can do — it doesn't replace it. Build the SQL foundation for 4 to 6 weeks, then move to a BI tool. Add Python after you're hired or when you're applying to roles that explicitly require it.
How long does it take to learn enough Python to list it on a resume?
For basic data manipulation with pandas — reading files, filtering, grouping, merging, cleaning — 3 to 4 weeks of deliberate practice gets most people to a level they can honestly represent. That's enough for roles where Python is listed as preferred. For roles that require statistical modeling or scripting automation, plan for 8 to 12 weeks to get to a level worth showing. Don't list Python on your resume if you've only completed a beginner tutorial — interviewers test it and shallow knowledge shows immediately.
Can I get a data analyst job with just SQL and Excel?
Yes, for some roles. SQL and Excel is a viable combination for analyst roles in smaller companies, operations, finance support, and some healthcare environments. Adding a BI tool makes you competitive for a much larger portion of the market. Python on top of that opens the most technical roles. Most people should aim for SQL plus a BI tool as the baseline before applying.
What Python libraries do data analysts actually use?
pandas for data manipulation is the most common by far. numpy comes along with it for numerical operations. matplotlib or seaborn for visualization when BI tools aren't sufficient. scikit-learn if the role involves any predictive modeling. For most entry-level analyst work, pandas is the only one that consistently comes up. The others depend heavily on the role and the company's data stack.
Do I need Python if I already know SQL and Power BI?
Not to get hired for most analyst roles. SQL and Power BI qualifies you for a large portion of the entry-level market. Python becomes worth adding when you're targeting roles that require it, when you've been hired and encounter problems the existing tools can't solve, or when you want to move toward more technical analyst or engineering roles later in your career. Add it when it unlocks something specific, not as a checkbox.
If you want a clear sequence through the skills that actually get entry-level analysts hired, without the detours, the Analyst Hive program is daily tasks built around what the job market actually requires. No padding, no tool-of-the-week, just the path.
