Amber enrol buttons are DataCamp and Udemy affiliate links; we earn a commission if you enrol through them. How we're funded.
Quick answer
To become a data scientist, learn SQL and Python, get genuinely comfortable with statistics, complete two or three real projects end to end, and target analyst or analytics engineer roles first if you have no experience. A degree helps but is not required; demonstrable work on messy, real data is what converts applications into interviews.
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.
One purchase that promises the statistics, Python, machine learning and TensorFlow deep learning a junior data scientist is asked about, in about 32 hours. It starts from no prior experience, and its requirements ask for Anaconda and Microsoft Excel.
Why this course, and its limitations
365 Careers' data-science course, bought once, that starts from no prior experience and runs from probability and statistics through Python, regression and clustering to deep learning with TensorFlow 2.0. We value that foundations-first map of the field at a finishable length. Some promised tools get little depth, no section title covers building on a language model, and it needs Anaconda and Microsoft Excel. The certificate is an unassessed completion record. Learner evidence, read on Udemy on 25 September 2026: 162,263 ratings averaging 4.6 from 818,885 learners, and a syllabus updated 2026-09.
Learning: 4.4/5. Credential: 2.0/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
Start with data handling rather than machine learning. Seventeen hours of pandas from zero Python — though you will need statistics from somewhere else.
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.
Start here rather than with a machine-learning course: thirty-six hours from zero Python to statistics and hypothesis testing, which is the actual foundation.
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.
This guide sets out what the job involves in practice, the skills that actually get tested, a realistic sequence to follow part-time, which certificates are worth the effort, and how to handle the experience paradox that blocks most beginners.
What does a data scientist actually do?
A data scientist turns messy organizational data into decisions, usually through a mix of analysis, statistical modeling, and machine learning. The job title covers three fairly different roles, and knowing which one you want changes what you should study.
- Product or analytics data scientist: experimentation, A/B testing, metric design, and causal questions. Heavy SQL, statistics, and communication.
- Machine learning data scientist: predictive models, feature engineering, and evaluation, often shading into machine learning engineering.
- Research data scientist: novel methods, forecasting, optimization, or applied science, where advanced degrees are common.
Across all three, the day-to-day is less glamorous than course material suggests. Most time goes to finding and cleaning data, checking whether a metric means what people think it means, and explaining results to stakeholders who will act on them. Modeling is often the smallest part of the week.
Do you need a degree to become a data scientist?
No, a specific degree is not required, but you do need to prove equivalent capability. Many working data scientists came from economics, physics, biology, engineering, or analytics backgrounds, and self-taught entrants do get hired, most often by moving up from an analyst role rather than landing a data scientist title first.
Where a degree still matters is research-heavy positions, some regulated industries, and visa-sponsored roles with formal requirements. A PhD is genuinely useful for applied science and research teams, and largely unnecessary for product analytics and applied modeling work.
The realistic substitute for credentials is evidence: a portfolio of projects using real data, a public repository with readable code, and the ability to explain your modeling choices under questioning. If you are weighing formal study against certificates, our analysis of whether AI certifications are worth it covers what each signal buys you.
What skills do you actually need?
The hiring bar is narrower than the internet suggests. Depth in SQL, Python, and statistics beats shallow familiarity with twenty tools.
Core technical skills
- SQL, to a level that includes joins, window functions, CTEs, and query debugging. This is tested in almost every interview.
- Python with pandas, NumPy, scikit-learn, and a plotting library. R is a fine alternative in some industries but Python dominates job listings.
- Data cleaning and feature engineering: handling missing values, outliers, leakage, and inconsistent categories.
- Version control with Git, plus enough command line to work in a shared codebase.
Statistics and modeling
- Distributions, sampling, confidence intervals, and hypothesis testing, understood well enough to explain what a p-value does not mean.
- Experiment design: randomization, sample size, and the traps in A/B testing.
- Regression and classification, cross-validation, overfitting, class imbalance, and the metric trade-offs between precision and recall.
- Enough causal reasoning to separate correlation from a defensible causal claim.
The skills that get overlooked
Communication is the differentiator at every level. Writing a clear summary that a non-technical manager can act on, scoping an ambiguous request into an answerable question, and saying honestly when data cannot support a conclusion are the behaviors that get people promoted. Business context matters too: a technically excellent model of the wrong metric is worthless.
Cloud and deployment literacy is increasingly expected, though not at engineer depth. Being able to work in a cloud notebook environment, understand where data lives, and hand a model over cleanly is usually enough. Skills reports published by Coursera have consistently placed data and AI skills among the fastest-growing categories requested by employers.
What is a realistic step-by-step path?
Expect roughly nine to eighteen months part-time from a standing start, less if you already code or already work with data. The order matters more than the speed.
- Learn SQL first, because it is the highest-return skill and the most commonly tested. Practice against a real database, not slides.
- Learn Python for data work: pandas for manipulation, then visualization. Build the habit of working in notebooks and scripts.
- Study statistics properly, alongside the coding. Applied statistics with real datasets sticks better than theory in isolation.
- Add machine learning fundamentals: regression, classification, validation, and evaluation before anything deep or generative.
- Complete one substantial end-to-end project on data you actually care about, including cleaning, analysis, a model, and a written conclusion.
- Learn the workflow tools that appear in job descriptions: Git, a cloud notebook environment, and basic dashboarding.
- Apply to analyst, analytics engineer, and junior data scientist roles simultaneously, and keep building while you apply.
Two or three strong projects beat ten tutorials. The most common mistake is collecting courses instead of finishing work that someone else could review.
Not sure this is the right one for you?
Tell the picker about your background and what you want the certificate to do, and it narrows the list to the one or two courses we would start with. It suggests only our affiliate partners’ courses, and says so before it suggests anything.
Try the AI Certification Picker →Which data science certificates are worth doing?
Certificates are useful for structure and for showing initiative, not for guaranteeing interviews. Choose one comprehensive program rather than several overlapping ones.
The table below compares 5 programs on best for and honest limitation.
| Program | Best for | Honest limitation |
|---|---|---|
| IBM Data Science Professional Certificate | Complete beginners who want Python, SQL, and a capstone in one sequence | Breadth over depth; the capstone is common to many applicants |
| Google Advanced Data Analytics Certificate | Analysts adding statistics, Python, and machine learning | Assumes prior analytics familiarity; lighter on engineering |
| Machine Learning Specialization (Stanford and DeepLearning.AI) | Modeling fundamentals explained clearly | Not a job-ready data science program on its own |
| Microsoft Azure or AWS cloud AI credentials | Candidates targeting employers standardized on one cloud | Vendor-specific; teaches services more than statistics |
| University certificate or masters | Career changers who need structure, deadlines, and a formal signal | Substantial time and cost; verify current fees with the institution |
Our reviews go deeper on the two most popular options, the IBM Data Science Professional Certificate and the Google Advanced Data Analytics Certificate, while the Machine Learning Specialization remains the clearest explanation of the modeling fundamentals. Training catalogs from IBM and Microsoft list current requirements and renewal rules directly.
How do you build a portfolio that gets interviews?
Build projects that resemble work, not competitions. A hiring manager skimming your repository wants to see judgment, not a leaderboard score on a cleaned dataset everybody has used.
- Use messy data you obtained yourself: a public API, a government release, scraped listings, or your own workplace exports with permission.
- Frame a decision, not a task. Ask which customers to contact, which price to set, or whether a change worked, then answer it.
- Show the analysis, including what you tried that failed and why you rejected it. Reviewers read this as maturity.
- Write a short summary at the top of the repository: the question, the data, the method, the result, and the limitations.
- Keep the code readable and reproducible. Clear names and a working environment file matter more than clever one-liners.
One project deployed as a small dashboard or API demonstrates more than three notebooks, because it proves you can finish something other people can use.
How do you get the first job without experience?
Enter sideways. The fastest reliable route into data science is a data-adjacent job followed by an internal move, because internal candidates are evaluated on demonstrated work rather than on a resume screen.
- Target data analyst, business analyst, analytics engineer, and reporting roles, where the SQL-heavy bar is lower and the learning is paid.
- Use your current domain as an advantage. Finance, healthcare, logistics, and marketing experience makes you more useful than a generalist with the same technical level.
- Volunteer for measurement work internally: build the dashboard, run the experiment analysis, automate the manual report.
- Prepare deliberately for interviews, which are typically SQL exercises, a statistics and experimentation round, a modeling or case discussion, and a behavioral round.
- Apply consistently rather than perfectly. Job descriptions list wishes, not requirements, and meeting most of them is normal.
What does the data science job market look like?
Demand remains strong but the bar has risen. The U.S. Bureau of Labor Statistics Occupational Outlook Handbook lists data scientist among the faster-growing occupations it tracks, while employers now expect more engineering competence and more comfort with AI tooling than they did a few years ago.
Three shifts matter for planning. Generative AI has absorbed some routine analysis and reporting work, which raises the value of experimentation, causal thinking, and judgment. Titles are blurring, so analytics engineer, machine learning engineer, and product analyst roles often want the same core skills. And entry-level competition is intense, which is exactly why portfolio evidence and a domain specialty matter more than another certificate. If you want a structured sequence that combines both, follow our AI certification roadmap.
What mistakes waste the most time?
The common failures are predictable, and avoiding them is worth months.
- Starting with deep learning before statistics and SQL, then failing basic interview questions.
- Collecting certificates without finishing a single original project.
- Polishing tutorial datasets that thousands of other applicants also used.
- Refusing analyst roles while waiting for a data scientist title, which usually delays entry by a year or more.
- Neglecting writing, then losing offers at the communication round despite strong technical scores.
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.
Ready to start?
Bought once, with what Udemy calls lifetime access. Udemy's price swings between its list price and a sale price, sometimes within days — check it on the day rather than trusting any figure you read, here or anywhere else.
Frequently asked questions
How long does it take to become a data scientist?
From no coding background, plan on nine to eighteen months of consistent part-time study before you are competitive for entry-level roles. People already working with data, such as analysts, financial modelers, or engineers, often make the transition in six to nine months. The variable that matters most is not study hours but how quickly you start producing finished projects on real data.
“Finished” is the operative word in that last sentence. A half-explored notebook teaches you the tool and proves nothing; a project taken through to a written conclusion — here is the question, here is what I found, here is what I am not sure about — teaches you the job and is the thing an interviewer can respond to. Most people who stall have several of the first kind and none of the second.
Can I become a data scientist without a degree?
Yes, though the path usually runs through an analyst role first. Employers substitute evidence for credentials when the evidence is strong: readable code on public repositories, projects using messy real data, and clear written explanations of your reasoning. Degrees still carry weight for research positions, regulated industries, and roles requiring visa sponsorship.
Visa sponsorship is the exception worth knowing about early, because it is not a preference an employer can waive. Immigration rules in several countries tie skilled-worker routes to formal qualifications, so a company that would happily hire you on evidence may be unable to. If relocation is part of your plan, check that constraint before deciding a degree is optional — it is the one case where a portfolio genuinely cannot substitute.
Is Python or R better for data science?
Python is the safer default because it appears in far more job listings and extends naturally into machine learning and production code. R remains excellent for statistics, and stays common in academia, pharmaceuticals, and some research teams. Learn one properly rather than both superficially; the concepts transfer.
Let the industry you are aiming at settle it if you have one. Pharmaceutical and clinical research run on R deeply enough that arriving without it is a real handicap; technology companies run on Python and often have no R anywhere. If you have no target industry yet, Python is the option that closes fewest doors — which is the whole argument for it, rather than any claim that it is the better language.
Do data science certificates actually help you get hired?
They help at the margin. A recognized certificate shows structured effort and can get a resume past an initial screen, particularly for career changers, but no certificate substitutes for demonstrated work. The most valuable thing a good program provides is the enforced sequence and the capstone, which you should then extend into an original project rather than submitting as-is.
Extending the capstone matters more than it sounds because interviewers have seen the standard one many times. Every graduate of a given programme submits the same project on the same dataset, so it identifies which course you took and nothing else. Change the data, add a question the brief did not ask, or take it further than the marking scheme required — any of those turns a template into something only you did.
What is the difference between a data scientist and a data analyst?
Analysts describe what happened and why, mainly with SQL, dashboards, and applied statistics. Data scientists extend that to prediction, experimentation, and modeling, and are more often expected to write production-quality code. In practice the boundary varies by company, and analyst roles at large technology firms can be more technical than data scientist roles elsewhere.
That last point has a practical consequence for job hunting: read the responsibilities, not the title, and do not rule out a role because it says analyst. A senior analyst position at a large technology company can involve more modelling, more code and more autonomy — and often better pay — than a data scientist title at a company where the function is new. Our comparison sets out both paths.
Do I need to be good at maths?
You need solid applied statistics and comfort with algebra, not advanced pure mathematics. Understanding distributions, variance, sampling, and regression matters daily; deriving proofs does not, outside research roles. Linear algebra and calculus become relevant if you move toward deep learning, and both can be learned to a working level alongside coding.
The statistics that matters daily is mostly about not fooling yourself: whether a difference is real at that sample size, what your comparison group actually is, and which of your results are the product of looking at the data twenty times. Those are judgement skills built on a modest amount of theory, and they are what separates a useful analysis from a confident wrong one.
Is AI making data science jobs obsolete?
No, but it is changing the mix. Assistants now handle much routine querying, chart building, and boilerplate code, which reduces demand for purely mechanical work and increases the premium on framing problems, designing experiments, and validating results. Data scientists who use AI tools fluently while retaining statistical judgment are in a stronger position, not a weaker one.
Validation is the part that has become more valuable rather than less, and it is worth understanding why. A tool that produces analysis quickly also produces plausible wrong analysis quickly, and at higher volume — so the constraint moves from writing the code to knowing which results to believe. That is the same skill the role always rested on; there is simply more output arriving for it to be applied to.
Keeping this current. Course formats, prices, and certification exam fees change and vary by region. We review our guides regularly, and we always recommend confirming the specifics on the provider's official page before you enrol.