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Quick answer
A data analyst answers defined questions using existing data, reporting and visualization, while a data scientist tackles ambiguous questions using statistics, experimentation and predictive modelling. Analyst roles are easier to enter and lean on SQL and business intelligence tools; data science roles expect stronger statistics and programming, and often a postgraduate background.
Where we would start on Udemy or DataCamp
We choose these picks only among our affiliate partners’ courses (365 Data Science, DataCamp and Udemy). Our full ranking also includes courses that earn us nothing.
If the answer is analyst, this is the route: pandas end to end, starting from a Python crash course. Note it has no statistics section.
Why this course, and its limitations
A focused pandas course with introductory Python material. We value it for data handling. It has no recorded statistics section, so it should not be treated as a complete data-analysis or AI qualification.
Learning: 4.4/5. Credential: 1.5/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
If the answer is analyst, this is the SQL half of the job: querying from a first SELECT to window functions, with exploratory analysis and reporting to management. It shares no course with the Python track on this page, so the two fit together.
Why this course, and its limitations
A 39-hour career track: eight SQL courses, from a first query to exploratory analysis and a case study reporting to management, plus no-code courses on statistics, visualisation and communication. We value that span of an analyst's work at a finishable length. What holds the score down is scope: no AI or machine learning, which our curriculum-currency factor weighs, and no Python, spreadsheet or BI tool. Finishing earns a completion record, not the separately assessed Data Analyst Associate certification DataCamp designed it to prepare for.
Learning: 4.3/5. Credential: 3.0/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
If you want the analyst route graded, with the statistics the Udemy course leaves out: thirty-six hours from no code at all to pandas, statistics and hypothesis testing, with a quarter of a million people through it.
Why this course, and its limitations
A 36-hour career track of nine Python courses: the basics, pandas, Seaborn, exploratory analysis and three statistics courses ending in hypothesis testing. We value that ordered foundation, its statistics and its graded exercises. What holds the score down is scope: it teaches no machine learning or AI, which our curriculum-currency factor weighs, and no SQL, which both of DataCamp's analyst certifications assess. Finishing earns a completion record, not a certification.
Learning: 4.3/5. Credential: 3.0/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
What is the difference between a data analyst and a data scientist?
The clearest difference is the type of question each role handles. A data analyst is usually given a question, such as why sales fell in one region last quarter, and answers it from available data. A data scientist is more often given a problem area and has to define the question, then design a way to answer it that may involve modelling or an experiment.
That difference in ambiguity drives everything else. Answering defined questions rewards fluency with SQL, spreadsheets and dashboards. Answering undefined questions rewards statistical reasoning, experimental design and programming, because you frequently have to build the measurement rather than query it.
Titles are used loosely, and the same work appears under both labels depending on the organization. Some companies call anyone touching data a data scientist; others reserve it for people building models. Read the responsibilities and required tools in a job description rather than the title.
How do the two roles compare?
The roles diverge on tooling, statistical depth, typical background and entry difficulty. The table below sets out the practical comparison.
The table below compares Data analyst and Data scientist across 8 dimensions.
| Dimension | Data analyst | Data scientist |
|---|---|---|
| Typical question | Defined and specific | Ambiguous and open-ended |
| Core tools | SQL, Excel, Power BI or Tableau | SQL, Python or R, statistical and ML libraries |
| Statistics needed | Descriptive statistics and basic inference | Inference, experimental design, causal reasoning |
| Programming | Helpful, not always required | Required |
| Main output | Dashboards, reports, ad hoc analysis | Experiments, models, analytical recommendations |
| Usual entry route | Business, finance, operations, self-taught | Quantitative degree, often postgraduate |
| Entry difficulty | Lower | Higher and more competitive |
| Common certifications | Google Data Analytics, Microsoft PL-300 | IBM Data Science, Google Advanced Data Analytics |
Both roles spend far more time on data cleaning and stakeholder communication than newcomers expect. That part is common to both and is rarely what people imagine when they enter the field.
What does a data analyst actually do?
A data analyst turns available data into answers that people act on. The work involves pulling data with SQL, cleaning and reconciling it, building dashboards and reports, investigating anomalies, and explaining what the numbers mean to people who do not work with data.
Much of the value is in reliability and clarity rather than sophistication. A dashboard that stakeholders trust and use daily is worth more than a complex model nobody understands. Analysts who become indispensable usually do so through business understanding rather than technical depth.
The realistic downsides are worth stating. A significant share of analyst work is repetitive reporting, requests arrive faster than they can be fulfilled, and data quality problems are constant and rarely within your power to fix. Our guide to becoming a data analyst covers the path in detail.
Is dashboard work at risk from automation?
Routine reporting is the most automatable part of the analyst role, and self-service and AI-assisted querying tools have reduced demand for simple report generation. What has not been automated is deciding what to measure, catching when a number is wrong, and translating findings into decisions. Analysts whose value rests entirely on producing charts on request are more exposed than those who own the interpretation.
What does a data scientist actually do?
A data scientist designs how to answer questions that do not yet have an agreed method. That may mean building a predictive model, designing an experiment to test a change, constructing a metric that did not previously exist, or determining whether an observed effect is real.
Modelling is a smaller share of the job than expected. Practitioners consistently describe the majority of their time going to data acquisition, cleaning, exploration, and communicating results. The statistics that matter most in practice are often experimental design and causal inference rather than deep learning.
The role also carries more responsibility for being right. An analyst reporting a wrong number is embarrassing; a data scientist recommending a change based on a flawed experiment can cost real money. That is part of why the bar is higher. Our guide to becoming a data scientist sets out the realistic route.
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Try the AI Certification Picker →Which role is easier to get into?
Data analyst roles are considerably easier to enter, and for most career changers they are the correct target. The required skills, SQL, spreadsheets, a visualization tool and clear communication, can be learned in months rather than years, and employers hire analysts without degrees in quantitative subjects.
Data science hiring is more competitive at the junior end. Many postings expect a postgraduate degree or equivalent experience, strong statistics, and evidence of independent analytical work. The supply of applicants for entry-level data science roles is large relative to the number of positions.
The practical implication is straightforward. If your goal is data science and you are starting from outside the field, entering as an analyst first is usually faster than applying directly to data science roles for a year. You gain domain knowledge, data access and internal credibility, all of which make the next step easier. The certification roadmap shows how these stages sequence.
Which role pays more?
Data science roles generally sit higher on compensation than analyst roles at equivalent seniority, reflecting the higher entry requirements. The gap is real but smaller than commonly assumed, and it narrows considerably with seniority, since a senior analyst with deep domain expertise can out-earn a junior data scientist.
Factors that affect pay more than the title itself:
- Industry, since the same role is compensated very differently across technology, finance, healthcare and the public sector.
- Whether the role sits close to revenue decisions or in a support function.
- Location and remote status, which can shift bands substantially.
- Specialist domain knowledge, which is frequently rewarded more than technical breadth.
For neutral data rather than recruiter estimates, the U.S. Bureau of Labor Statistics Occupational Outlook Handbook publishes occupational descriptions and outlook information for data scientist and related roles. Figures circulating on social media should be treated cautiously, since they oversample unusually high outcomes.
Can you move from data analyst to data scientist?
Moving from analyst to data scientist is a well-worn path and generally easier from inside a company than through external applications. The advantage is that you already have data access, domain knowledge and relationships with the people whose problems you would be solving.
The gaps to close are usually these:
- Programming. Move from SQL and spreadsheets to genuine Python fluency, including pandas and scikit-learn.
- Statistics. Add inference, hypothesis testing and experimental design beyond descriptive reporting.
- Modelling. Learn to build, evaluate and explain predictive models, including when not to use one.
- Scope. Take on questions that are not yet defined, rather than answering requests as they arrive.
The most effective tactic is to do data science work in your analyst job before applying for the title. Volunteer for an experiment design, build a forecast, or propose a metric. An internal move built on that record is usually easier to make than a direct external hire.
Which certifications suit each role?
Certifications map cleanly onto these roles, and choosing the wrong one wastes months. Analyst credentials emphasize SQL, spreadsheets and business intelligence tools; data science credentials emphasize Python, statistics and modelling.
For analysts, the Google Data Analytics Professional Certificate and the IBM Data Analyst Professional Certificate are the common entry points, with Microsoft Certified: Power BI Data Analyst Associate (PL-300) adding a vendor-verified tool credential that appears in job descriptions. Our certifications for data analysts guide compares them.
For data scientists, the IBM Data Science Professional Certificate and the Google Advanced Data Analytics Professional Certificate provide broader coverage, and the Machine Learning Specialization adds modelling depth. Further data science fundamentals are available through IBM Training, and our certifications for data scientists guide covers the options. Coursera's Global Skills Report tracks skill and credential trends across more than 100 countries.
Which should you choose?
Choose data analyst if you want to enter the field quickly, enjoy working closely with business teams, and prefer concrete questions with clear answers. Choose data science if you have or are willing to build strong statistical foundations, enjoy ambiguity, and want to work on problems where the method itself is part of the challenge.
Consider also what you would find tedious. Analyst work involves recurring reporting and frequent interruptions from requests. Data science involves long stretches of data preparation and results that sometimes show nothing conclusive. Both are normal parts of the respective jobs.
If genuinely undecided, start as an analyst. It is the lower-risk entry, it teaches you whether you enjoy working with data at all, and it leaves the data science path fully open. Very few people regret entering the field through analysis.
Certifications featured in this guide
Every option below is one we cover in depth. Each link goes to the provider’s own page; where we’ve published a full review, read that first.
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Frequently asked questions
Do data analysts need to know Python?
Not always, though it increasingly helps. Many analyst roles run on SQL, spreadsheets and a business intelligence tool such as Power BI or Tableau. Python becomes valuable for automating repetitive work, handling data too large or messy for spreadsheets, and moving toward data science.
The signal that you need it is usually a task rather than a career plan: the same clean-up done every Monday, a file too big for the spreadsheet to open, a join the BI tool cannot express. Learning Python in response to one of those sticks, because there is something to use it on immediately. Learning it in the abstract, before any such task exists, tends not to.
Is data analyst a good first job in tech?
It is one of the better entry points, because the skills are learnable in months, the work exposes you to how a business actually operates, and it opens routes into data science, analytics engineering and product roles. The trade-offs are that junior analyst work can be repetitive and that competition has increased as the path has become widely recommended.
Domain knowledge is the strongest differentiator, and it is the one thing a career changer already has. Somebody arriving from finance, healthcare or logistics can read the numbers in that field and know when one looks wrong — which a technically stronger candidate from outside cannot, and which takes months to acquire. Apply into the industry you came from rather than treating your past as irrelevant.
Will AI replace data analysts?
AI is automating routine report generation and making basic querying accessible to non-specialists, which reduces demand for purely mechanical analyst work. It does not replace judgment about what to measure, recognizing when data is wrong, understanding organizational context, or persuading stakeholders to act.
Recognising when data is wrong is the most durable of those and the hardest to describe on a CV. Every organisation's data has known lies in it — the field that stopped being populated in March, the region that double-counts, the definition that changed and was never backfilled — and no tool knows any of them. An analyst who does is the reason the number reaching the board is right.
How long does it take to become a data analyst?
For someone studying consistently part-time, reaching job-ready competence in SQL, spreadsheets, a visualization tool and basic statistics typically takes several months. Building a small portfolio of analyses on real datasets adds more. Career changers with relevant domain experience often move faster because the business understanding is already there.
Spend the portfolio time on questions rather than on datasets. An analysis that starts from “why did returns rise in this region” and reaches a defensible answer demonstrates the job; a notebook that visualises a famous public dataset demonstrates the tool. Interviewers have seen a great many of the second kind, and they cannot tell them apart from one another.
Do I need a masters degree for data science?
Not universally, but it helps at the junior end where competition is heaviest, and some employers use it as a filter. Practitioners without postgraduate degrees are common, particularly those who entered through analytics and progressed internally. If you already work with data, gaining experience is usually a better investment than a degree.
The internal route is worth taking seriously because it sidesteps the filter entirely. Employers who screen external applications on a master's routinely promote analysts into data science roles without one, having watched them work — which is a real inconsistency and one you can use. If you are already inside an organisation with a data science team, that is the cheapest path available to you.
Which role should I pick if I dislike coding?
Data analyst, and specifically roles centred on business intelligence tools. Many analyst positions require SQL but little or no general programming, and SQL is far more approachable than Python for people who dislike software development. Data science is not a realistic target without programming, since it is central to almost every task in the role.
SQL feels different from programming to most people who dislike programming, which is why the distinction is worth making rather than lumping them together. You describe what you want rather than how to compute it, there is no state to keep in your head, and a query either returns the rows or it does not. Plenty of people who bounced off Python get on with SQL perfectly well.
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