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Quick answer
DataCamp's Data Analyst in Python is a 36-hour career track of nine Python courses that runs from your first line of code through pandas, Seaborn and exploratory analysis to a three-course statistics block ending in hypothesis testing. Take it if you want to do analysis in Python from a standing start, with graded coding exercises in the browser. Know its gap before you commit: it teaches no SQL, which analyst jobs and both of DataCamp's analyst certifications ask for, and finishing it earns a completion record, not a certification. We score it 4.0 out of 5.
Where we would start, among the ones that pay us
This review's subject: DataCamp's Data Analyst in Python career track, Python from no code through pandas, visualisation, exploratory analysis and three statistics courses. It teaches no SQL, which analyst jobs and DataCamp's analyst certifications ask for.
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 currency and one gap: it teaches no machine learning or AI, 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.
An analyst does two things with data: gets it, and makes sense of it. DataCamp's Data Analyst in Python is about the second, in Python, and it goes further than most beginner programmes, as far as testing whether a result is real. What it leaves out is the first, and that gap is the question this review keeps coming back to.
What is it?
Data Analyst in Python is one of DataCamp's career tracks: a longer sequence of courses built around a job, as opposed to a short skill track on one topic. It holds nine required courses, every one of them Python, plus five optional guided projects and an optional skill assessment, all running in the browser. DataCamp lists the track at 36 hours, and the listed lengths of the nine courses add up to exactly that. When we read its catalogue entry on 26 September 2026, the content had last been updated on 2 September 2026, and the latest change DataCamp records is the addition of five projects. DataCamp's course pages publish no structured data a script can verify, so every DataCamp figure here is hand-recorded from its catalogue and carries that date.
We publish it as Beginner, and the label comes from the courses rather than from DataCamp's track badge, which it rescaled in September 2026. Four of the nine sit on the lowest rung of DataCamp's three-step course scale and five sit one rung up, at Intermediate: Joining Data with pandas, Exploratory Data Analysis in Python and all three statistics courses. None is Advanced. The first course, Introduction to Python, lists no prerequisites, and the track's description says that no prior coding experience is required. It genuinely starts from zero, and more than half of it is intermediate work.
Two relationships explain where it sits. Its first seven courses are the whole of DataCamp's shorter Python Data Fundamentals skill track, and all nine are part of the 90-hour Associate Data Scientist in Python career track. What it leaves out is as clear: no SQL, no spreadsheet or dashboard tool, no machine learning and nothing on AI. Everything below is drawn from DataCamp's own published course listings, read through its catalogue, not from first-hand study.
What you'll actually learn
The nine required courses of Data Analyst in Python, in the order DataCamp lists them, with the level DataCamp gives each course and its listed length — four Beginner and five Intermediate, 36 hours in all:
- Introduction to Python · Beginner · 4 hrs
- Intermediate Python · Beginner · 4 hrs
- Data Manipulation with pandas · Beginner · 4 hrs
- Joining Data with pandas · Intermediate · 4 hrs
- Introduction to Statistics in Python · Intermediate · 4 hrs
- Introduction to Data Visualization with Seaborn · Beginner · 4 hrs
- Exploratory Data Analysis in Python · Intermediate · 4 hrs
- Sampling in Python · Intermediate · 4 hrs
- Hypothesis Testing in Python · Intermediate · 4 hrs
The first two courses are Python itself: Introduction to Python covers the basics and introduces the packages data work relies on, and Intermediate Python adds charts in Matplotlib and a first go at pandas DataFrames. The next five are the analyst's working toolkit: Data Manipulation with pandas for importing and cleaning data, calculating statistics and plotting; Joining Data with pandas for combining tables; Introduction to Statistics in Python; Introduction to Data Visualization with Seaborn; and Exploratory Data Analysis in Python, which puts them to work exploring a dataset.
The last two are what set this track apart from its shorter sibling. Sampling in Python covers drawing conclusions from limited data, from random to stratified and cluster sampling; Hypothesis Testing in Python covers when and how to use t-tests, proportion tests and chi-square tests. With the introductory statistics course they make a three-course block that takes you from describing data to testing a claim about it, which is the step that turns a chart into an answer.
Five optional guided projects sit alongside the courses — Netflix films, New York school test scores, Nobel Prize winners, Los Angeles crime, and whether more goals are scored in women's or men's international football — each listed at an hour or less, with an optional skill assessment, Data Manipulation with Python. Do the projects: they are guided, but they hand you a dataset and a question rather than a single exercise, and the last is a complete hypothesis test.
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Try the AI Certification Picker →The details: cost, time, prerequisites
Cost. The track is not sold on its own. It comes with a DataCamp Premium subscription, priced by country — about $330 a year at US list price; DataCamp's pricing page shows the price for your country. All nine courses and all five projects are marked as paid content in DataCamp's catalogue, so the free Basic tier will not carry you through. DataCamp offers monthly and annual billing; compare the two on its pricing page against an honest finishing date rather than trusting a figure on any review site, this one included.
Time. DataCamp's figure is content time, not calendar time: at five hours a week the nine courses take about seven weeks, and at ten hours a week under four. The hours are course lengths as DataCamp lists them, and the projects come on top; we hold no data on how long learners actually take.
Prerequisites. None. DataCamp attaches no prerequisite track, the first course lists no prerequisite of its own, and the description says you start from scratch. The track teaches the Python it uses.
What it leads to: a completion record, and the certification question
Finishing the track gives you a completion record on your DataCamp profile. It is not an exam — nothing is proctored and nobody outside DataCamp signs it off — so treat it as a record that you did the work, not as a credential.
DataCamp links Data Analyst in Python to no certification, and the name invites a mistake. DataCamp's two analyst certifications both assess SQL: our guide to DataCamp's certifications describes the Data Analyst Associate as entry-level analysis in SQL and a visualisation tool, and the Data Analyst Professional as full analyst proficiency in SQL and R or Python. This track teaches no SQL, so on its own it prepares you for neither.
The certification its content matches, on our reading, is the Python Data Associate, which the same guide describes as data management and exploratory analysis in Python. At Associate tier a DataCamp certification is a timed skill assessment followed by a practical exam, which you can fail. DataCamp does not present the track as preparation for it, and we have not seen the exam, so treat that as our judgement rather than DataCamp's promise. We do not publish the certification's length, question counts, retake rules or whether it expires, because we could not verify them from a source we trust. If an analyst certification is what you want, add Associate Data Analyst in SQL, the track DataCamp built for the Data Analyst Associate certification.
How we checked this. We list the cost as DataCamp Premium subscription, priced by country — about $330 a year at US list price; DataCamp's pricing page shows the price for your country. Source: DataCamp's affiliate team, in writing on 21 September 2026: pricing is dynamic and geolocalised, with a US annual list price of $330. From Pakistan on 24 August 2026 the public pricing page showed $13 a month billed annually or $19 month-to-month, so the figure you are shown depends on where you are — treat any number here as a guide, not a quote. We re-check every price against the provider before each monthly review, and publish no figure we cannot source — where a provider prices regionally, we say so rather than quote a number that is wrong for most readers.
Pros and cons
✓ Pros
- Starts from zero: the first course lists no prerequisites, and the description says no coding experience is required
- A three-course statistics block, ending in hypothesis testing, that the shorter Python Data Fundamentals track lacks
- Five optional guided projects, the last a complete hypothesis test
- All nine courses are also required in the longer Associate Data Scientist in Python track, so nothing is wasted if you carry on
- Every exercise runs in the browser, so there is nothing to install
✕ Cons
- No SQL, which analyst jobs and both of DataCamp's analyst certifications ask for
- No machine learning and nothing on AI
- Finishing it awards a completion record, not a certification
- Five of the nine courses are Intermediate, so the second half is a real step up for a first-time coder
Who should take it (and who shouldn't)
Take it if you want to do analysis in Python and are starting from nothing, with the statistics to back up what you find. Take it if you already know SQL and want the Python side to go with it: together they are the analyst toolkit our data analyst career guide describes. And take it if data science may come later, because all nine courses are part of DataCamp's longer data-scientist track and nothing done here is wasted if you carry on. If you are aiming at an analyst job and have no SQL yet, the career guide puts SQL first, so plan a SQL track before or alongside this one.
Skip it if you already use pandas and run significance tests at work: most of it will be revision. Skip it if your goal is machine learning or AI: nothing here reaches a model, and DataCamp's Machine Learning Fundamentals in Python or the longer Associate Data Scientist in Python track goes there. And skip it if you need a credential a screener recognises on sight; a DataCamp completion record is not one. Our comparison of data analyst and data scientist roles helps if you are still choosing between them.
How it compares to the alternatives
The table sets Data Analyst in Python beside three other DataCamp tracks. All four are Beginner; three teach Python, and Associate Data Analyst in SQL, the one that teaches SQL, is the only row whose Coding cell differs.
| Course | Provider | Level | Time | Coding | Worth knowing | Enrol |
|---|---|---|---|---|---|---|
| Data Analyst in Python | DataCamp | Beginner | ~36 hrs | Python | Completion record; teaches no SQL | DataCamp → |
| Python Data Fundamentals | DataCamp | Beginner | ~28 hrs | Python | The first seven of these courses, without sampling and hypothesis testing | DataCamp → |
| Associate Data Analyst in SQL | DataCamp | Beginner | ~39 hrs | SQL | The SQL half of the analyst job; no course in common | DataCamp → |
| Associate Data Scientist in Python | DataCamp | Beginner | ~90 hrs | Python | All nine of these courses and fourteen more, machine learning included | DataCamp → |
Seven of Data Analyst in Python’s nine courses are also among Python Data Fundamentals’ seven:
- Introduction to Python
- Intermediate Python
- Data Manipulation with pandas
- Joining Data with pandas
- Introduction to Statistics in Python
- Introduction to Data Visualization with Seaborn
- Exploratory Data Analysis in Python
Only in Data Analyst in Python (two):
- Sampling in Python
- Hypothesis Testing in Python
Python Data Fundamentals is this track without its last two courses: the same seven courses, as the lists above show, ending before sampling and hypothesis testing. If you are sure you want analyst work, take this track, because those two courses are the step from describing data to testing it; if you are testing the water, the shorter track is a fair start, and its courses are the same courses, not copies.
Associate Data Analyst in SQL is the other half of the job rather than an alternative to it. It teaches SQL from a first query to window functions, with exploratory analysis, a reporting case study and short courses on statistics, visualisation and communication, and it shares no course with this track. Taken together, the two cover both halves of what our career guide asks of an analyst.
Associate Data Scientist in Python contains all nine of these courses and fourteen more, among them data cleaning, regression, experimental design and three machine-learning courses. It is the route for anyone heading for data science rather than analysis; at 90 hours it is a much larger commitment, and starting here first costs nothing. For the platform question underneath all of this, our comparison of DataCamp and Coursera goes further.
Is it worth it?
Yes, for the reader it is built for: someone who wants to analyse data in Python, starts from nothing and wants the statistics as well as the charts. For that reader the track is well made. It starts at zero, spends its middle on pandas and visualisation, ends on hypothesis testing rather than stopping at description, and checks your code along the way.
What holds it back is the SQL it leaves out. An analyst track without SQL teaches the second half of the job and not the first, and both of DataCamp's analyst certifications assess SQL, so a reader aiming at that job will need a second track. It also reaches no machine learning or AI, the subject of most of this site, and the completion record is not an exam.
We score it 4.0 out of 5. Scores on this site come from our six-factor methodology; the box below sets out why this one lands where it does, and our guide to certifications for data analysts shows what to add once the foundations are in place.
Why we score it 4.0 / 5
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 currency and one gap: it teaches no machine learning or AI, and no SQL, which both of DataCamp's analyst certifications assess. Finishing earns a completion record, not a certification.
4.3 / 5 how well it teaches3.0 / 5 what the certificate is worth
Provider facts for this entry were last checked on 2026-09-26.
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Frequently asked questions
Is DataCamp's Data Analyst in Python worth it?
For someone who wants to analyse data in Python from a standing start, yes. Its nine courses run from a first line of code through pandas, Seaborn and exploratory analysis to sampling and hypothesis testing, with graded coding exercises in the browser, and the statistics reach hypothesis testing. Its one serious gap is SQL: it teaches none, although analyst jobs and both of DataCamp's analyst certifications ask for it, so a reader aiming at that job will need a SQL track as well. Finishing earns a completion record, not a certification.
How long does Data Analyst in Python take?
DataCamp lists it at 36 hours, and the listed lengths of its nine courses add up to exactly that; the five optional guided projects come on top. That is content time rather than calendar time: at five hours a week it is about seven weeks of study, and at ten hours a week under four. We hold no data on how long learners actually take, so treat those figures as arithmetic rather than a promise.
Does Data Analyst in Python teach SQL?
No. All nine of its courses are Python, and none covers writing SQL queries. That matters because our data analyst career guide puts SQL first among analyst skills, and DataCamp's own analyst certifications, Data Analyst Associate and Data Analyst Professional, both assess SQL. If you want an analyst job, pair this track with a SQL track: DataCamp's Associate Data Analyst in SQL covers querying from a first SELECT to window functions and shares no course with this one, so the two fit together without repetition.
Should I take Data Analyst in Python or Python Data Fundamentals?
They share their first seven courses, so the question is the last two. Data Analyst in Python adds Sampling in Python and Hypothesis Testing in Python, which take you from describing data to testing whether a difference is real. If you are sure you want analyst work, take this track; if you are trying Python for data before committing, take Python Data Fundamentals first. The shared courses are the same courses, not copies, so nothing is lost if you move from one to the other.
Does Data Analyst in Python give you a certification?
No. Finishing it records a completion on your DataCamp profile; it is not an exam. DataCamp links the track to no certification, and despite the name it does not prepare you for DataCamp's analyst certifications on its own, because both assess SQL. On our reading, the certification its content matches is the Python Data Associate, which covers data management and exploratory analysis in Python and is a timed assessment plus a practical exam. Check DataCamp's certification page for the current requirements before you plan around one.
Can Data Analyst in Python get you a data analyst job?
Not on its own, and no single course can promise that. It gives you pandas, visualisation, exploratory analysis and statistics up to hypothesis testing, which is the analysis half of the job, but not the SQL our career guide calls the most tested analyst skill, and a DataCamp completion record is not a credential a recruiter recognises on sight. What turns it into something you can show is work: the guided projects written up, a SQL track alongside, and ideally one analysis of your own on a public dataset.