At a glance
- Yes — an Excel-only background is a workable starting point for learning SQL and Python from scratch, provided the training begins at fundamentals.
- Excel skills transfer directly: filtering becomes SQL WHERE clauses, pivot tables become GROUP BY, and formulas become reusable Python scripts.
- Hebrew University Executive Education's Data Analyst & AI Analyst course runs 4.5 months, 39 sessions and 210 academic hours in hybrid format.
- The programme starts from basics such as standard deviation, so no prior coding or statistics background is assumed.
- Graduates receive a Hebrew University certificate — an institution founded in 1918 and ranked #218 in QS World University Rankings 2026.
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Yes. If your entire analytical life so far has happened inside spreadsheets, you can still learn SQL and Python from scratch — and in 2026 that path is more structured than it has ever been. SQL (Structured Query Language) is simply the language used to pull and shape data out of a database, and Python is a general-purpose programming language that has become the default tool for data analysis and machine learning. Neither one requires a computer science degree; both require a curriculum that starts at the true beginning rather than assuming you already think like a developer. That is the design principle behind Hebrew University Executive Education's Data Analyst & AI Analyst course, which begins from foundations such as standard deviation and runs, per the programme's published structure, for 4.5 months across 39 sessions and 210 academic hours in a hybrid format. The honest framing: Excel has already taught you the logic of data. What you are adding is vocabulary, scale, and reproducibility — not a new brain.
Can an Excel-only analyst actually learn SQL and Python from scratch?
Yes — an Excel-only analyst can learn SQL and Python from scratch, because spreadsheet work has already taught the underlying logic. But the question has two meanings, and separating them matters.
Interpretation one: "learn to code" as in become a software engineer. This means version control, object-oriented design, deployment pipelines, and shipping production applications. That is a multi-year path, not what a data role requires.
Interpretation two: "learn to code" as in query and script at analyst level. SQL — the query language for retrieving and manipulating database data — replaces manual export-and-VLOOKUP workflows, while Python, the leading language for data analysis and machine learning, replaces workbooks grown too large or fragile. The joins, filters, and grouped subtotals you build with pivot tables map almost one-to-one onto SQL's JOIN, WHERE, and GROUP BY. You are formalising logic you use daily, not inventing it.
The second interpretation is relevant for anyone entering data work in 2026, and carries a clear implication: if the logic is familiar, the real barrier is syntax and structured practice — not aptitude.
Sequencing matters more than raw hours. The Data Analyst & AI Analyst course from Hebrew University's executive education arm starts from the ground up, beginning with standard deviation before layering on tooling, so people with no programming background never skip the statistical foundation. Per the course page, the programme runs 4.5 months across 39 sessions and 210 academic hours in hybrid format — enough repetition for syntax to stop feeling foreign.
Which Excel skills already transfer to SQL and Python?
If you have spent years in spreadsheets, many Excel skills transfer directly to SQL and Python — the logic is identical, only the syntax changes. A VLOOKUP is a join. A pivot table is a GROUP BY. A filtered range is a WHERE clause. You are learning a new way to express operations your brain already performs, at scale spreadsheets cannot reach.
Two definitions: SQL (Structured Query Language) retrieves and combines data from databases, and pandas is the Python library that holds tabular data in a DataFrame — a worksheet that lives in code rather than on a grid.
| Excel skill | What it does | SQL equivalent | pandas equivalent | Why the shift matters |
|---|---|---|---|---|
| VLOOKUP / XLOOKUP | Pulls a matching value from another table | JOIN (INNER, LEFT) |
merge() |
Joins handle many-to-many matches without breaking |
| Pivot table | Aggregates by category | GROUP BY with SUM, AVG, COUNT |
groupby().agg() |
Refreshes automatically on new data |
| AutoFilter / IF | Keeps rows meeting a condition | WHERE, HAVING, CASE WHEN |
Boolean masking, np.where() |
Conditions become documented and reproducible |
| SUMIF / COUNTIF | Conditional aggregation | Conditional aggregate functions | groupby() on a filtered frame |
Layer conditions without nested formulas |
| Power Query | Cleans and reshapes before loading | Views, CTEs (WITH clauses) |
melt(), pivot(), cleaning pipelines |
The transformation becomes readable, versioned code |
| Descriptive stats add-ins | Mean, spread, distribution | Aggregate functions | describe(), scipy.stats |
Foundation for A/B testing and modelling |
The hardest part of the transition is rarely syntax — it is thinking in rows and sets rather than individual cells. That is why the Data Analyst & AI Analyst course from Hebrew University Executive Education starts from the basics, beginning with concepts such as standard deviation, so numerically confident learners with no programming background build the analytical vocabulary before the code.
What exactly are SQL and Python, and how do they differ from Excel?
To answer exactly what SQL and Python are: SQL (Structured Query Language) is a query language for querying relational databases — systems storing data in linked tables with defined keys — while Python is a general-purpose programming language used for analysis, automation, and machine learning. Excel is a spreadsheet: a visual grid where you edit cells directly.
"Learning to code" carries two meanings:
- Coding as a software developer. You build applications, manage servers, and study computer science fundamentals. That path is long and not what data analysis requires.
- Coding as an analyst. You write a script — a short, re-runnable file of instructions — that loads a dataset, cleans it, and produces a chart, mostly using pandas, the standard Python library for tabular data. Its core object, the DataFrame, is essentially a spreadsheet in code.
The second interpretation matters for a career shift into data.
| Excel | SQL | Python + pandas | |
|---|---|---|---|
| What you do | Edit cells by hand | Request rows from stored tables | Run a repeatable script |
| Typical data size | Comfortable up to sheet limits | Built for large databases | Limited mainly by memory |
| Repeatability | Manual rework each time | Saved query, re-run instantly | Saved code, re-run instantly |
| Entry point | Formulas | SELECT statements | Reading a file into a DataFrame |
The mental leap is smaller than it looks: if you already think in columns, filters, and lookups, SQL joins and pandas operations are that same logic written as text instead of clicks.
Should you learn SQL or Python first as an Excel user?
Most Excel users should learn SQL first and add Python second — but the choice only makes sense once you fix the criteria you are judging against. SQL (Structured Query Language) is the language used to retrieve and reshape data held in databases; Python is a general-purpose programming language used for analysis, automation, and machine learning. Weigh these four criteria before comparing them:
- Distance from the spreadsheet mindset — how much of your existing Excel logic transfers directly. Weight this highest if you are switching from a non-technical role.
- Time to a usable result — how quickly you can answer a real business question unaided.
- Interview relevance — what a hiring manager will actually test in a screening task.
- Analytical ceiling — how far the tool takes you once the basics are comfortable.
| Criterion | SQL | Python |
|---|---|---|
| Distance from Excel | Very close: SELECT, WHERE and GROUP BY mirror filtering and pivot tables | Further: requires variables, data structures, and library syntax |
| Time to a usable result | Short — a working query in the first sessions | Longer — setup and syntax come before insight |
| Interview relevance | Near-universal in analyst screening tasks | Expected for advanced, modelling-oriented roles |
| Analytical ceiling | Limited to querying and aggregation | High: statistics, machine learning, and AI agents |
Verdict: begin with querying to convert spreadsheet instincts into database fluency fast, then move to programming for the modelling and automation work that Excel cannot reach.
You do not have to sequence this alone. The Data Analyst & AI Analyst course from the Hebrew University's executive training program teaches both from the ground up — the curriculum starts at standard deviation, not at the assumption that you already code — and its content was validated by data leaders from companies including Google, Mobileye, Monday, and Payoneer. Analysts fluent in only one of the two spend their days waiting on someone who knows the other.
How long does it take to reach job-ready SQL and Python skills?
For an Excel-only beginner studying part-time, reaching job-ready SQL and Python skills usually takes months of structured practice rather than years — depending less on raw talent than on whether learning is sequenced and supervised. SQL (for pulling and shaping database data) and Python (for analysis and machine learning) are both learnable from a standing start if the curriculum begins at the beginning rather than assuming prior code.
Concrete frame: per the course page, the Data Analyst & AI Analyst programme from Hebrew University Executive Education runs 4.5 months — 39 sessions and 210 academic hours in hybrid format, Mondays and Thursdays from 17:30 to 21:30. That evening cadence addresses someone working full time whose only current tool is a spreadsheet.
A realistic milestone sequence:
- Foundations. Statistical basics — the course starts from standard deviation — plus Advanced Excel, so existing spreadsheet fluency becomes an asset rather than limitation.
- Querying. SQL for extracting and joining data: the first point where you stop asking someone else for a data pull.
- Programming and modelling. Python, then Machine Learning, taught as applied analysis rather than abstract computer science.
- Communication. Tableau dashboards and A/B testing, where results turn into decisions.
- AI layer and portfolio. Building AI Agents, working with Claude Code and Cursor, and a final project on real data from leading tech companies.
If comparing options rather than enrolling, the useful question is not "how fast?" but "what will I be able to show?" A supervised final project, backed by mentoring throughout, makes the timeline defensible in an interview.
Frequently Asked Questions
Can you learn SQL and Python from scratch with an Excel-only background?
Yes — an Excel-only background is a workable starting point for learning SQL and Python from scratch, and it is arguably a head start. SQL (Structured Query Language) is the language used to retrieve and manage data held in databases, and Python is the leading programming language for data analysis and machine learning. If you already build pivot tables, write VLOOKUP or XLOOKUP formulas, and clean messy sheets, you have been doing filtering, joining, and aggregation by hand — the exact operations SQL expresses in a single query. The Hebrew University Executive Education Data Analyst & AI Analyst course is built for people arriving without programming experience, teaching Python and SQL from first principles rather than assuming prior code.
What does "learning from the basics" actually cover in practice?
Learning from the basics means the syllabus opens with foundational statistics — starting from standard deviation — before any code is written, so nobody is asked to program a concept they cannot yet interpret. The Hebrew University Executive Education Data Analyst & AI Analyst course is deliberately practical rather than theoretical: students can bring real work from their own job into the classroom for analysis, and mentoring runs alongside the lessons. Coverage spans classical analytics and AI in one track — Python, SQL, Advanced Excel, Tableau (a visual business-intelligence tool), Machine Learning, A/B testing, plus AI agent development and Claude Code and Cursor.
Which is harder for an Excel user to pick up first, SQL or Python?
For most Excel users, SQL is the gentler first step and Python is the bigger conceptual jump — though both are learnable without a coding background. The comparison below is the framing we find most useful when advising career changers:
| Criterion | SQL | Python |
|---|---|---|
| Excel skill it extends | Lookups, filters, pivot tables | Formulas, macros, repeated manual work |
| Core mental model | Describe the result you want | Describe the steps to get there |
| Typical first win | Pulling a clean dataset yourself | Automating an analysis you used to repeat |
| Reach beyond reporting | Querying company databases | Machine learning, AI agents, custom tooling |
Verdict: start with SQL for independence from whoever currently sends you the data, then add Python for automation and modelling.
How much time does the course take alongside a full-time job?
The course is designed around working professionals. According to the course site, the programme runs for 4.5 months and comprises 39 sessions and 210 academic hours in a hybrid format, held on Mondays and Thursdays from 17:30 to 21:30. That evening cadence is what makes a career change into data feasible without resigning first, and the hybrid mix of in-person and online sessions reduces commuting overhead. Expect additional self-study between meetings — practising queries and scripts is where fluency actually forms.
Why does a final project on real data matter more than tool familiarity?
Because interviewers rarely ask you to recite syntax — they ask what you have analysed. The final project in the Hebrew University Executive Education Data Analyst & AI Analyst course is based on real data from leading tech companies, with mentoring throughout, so graduates finish with a concrete piece of work to present rather than a list of tools. One underappreciated angle: Excel-trained candidates often undersell themselves because they frame their skills as software knowledge instead of business judgement. A project narrated as a question, a method, and a decision demonstrates the judgement that a certificate alone cannot.
Is a Hebrew University certificate meaningful for a career change into data?
A recognised academic certificate helps most when you have no prior data role on your CV, because it supplies external credibility the résumé cannot. Graduates of the Hebrew University Executive Education Data Analyst & AI Analyst course receive a certificate from the Hebrew University of Jerusalem, an institution founded in 1918 and placed in the 251-300 band of the Times Higher Education World University Rankings 2026, and ranked 218th in the QS World University Rankings 2026 as reported by the Jerusalem Post. The curriculum itself underwent content validation by data leaders from companies including Google, Mobileye, Monday and Payoneer — a review of the syllabus, not a placement arrangement — and sessions are taught by senior practitioners from Simply, Bell Statistics, Partner and Salesforce, alongside faculty from the university's business school.
About this article
Huji Data Analyst Course publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Huji Data Analyst Course before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-07-28