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Quick answer
The Data Science Course: Complete Data Science Bootcamp is 365 Careers' Udemy course: 32 hours 25 minutes across 524 lectures that start from no prior experience and promise statistics, Python with NumPy and pandas, Tableau, machine learning with statsmodels and scikit-learn, and deep learning with TensorFlow. Buy it if you want that whole map in one purchase and a completion certificate is enough; its requirements also ask for Microsoft Excel and an Anaconda install. For a longer path in the browser, DataCamp's Associate Data Scientist in Python track is the alternative. We have not scored it.
Where we would start, among the ones that pay us
This review's subject: 365 Careers' Data Science Course, about 32 hours from no experience through statistics, Python and machine learning, bought once on Udemy.
365 Careers sells this course on Udemy, and 365 Careers is also the company behind 365 Data Science, the subscription platform this site reviews separately. That makes the buying question unusually direct: one course bought once, a subscription from the same company, or a longer career track from DataCamp. This review is built from the course page as read in a browser on 25 September 2026, and from its full curriculum, all 64 sections opened, as read on 26 September 2026. It says so wherever those run out: the curriculum was read at section level, so this review names sections, not individual lectures.
What is it?
One Udemy course, sold by 365 Careers, which Udemy lists as its only instructor; Udemy styles the title “The Data Science Course: Complete Data Science Bootcamp 2026”. It is 32 hours 25 minutes of lectures, 524 of them, in 64 sections, taught in English with captions listed in 26 languages, and Udemy showed it as last updated in September 2026, though its description still calls it “The Data Science Course 2024”.
Udemy labels it All Levels. We publish it as Beginner, because the first requirement reads “No prior experience is required. We will start from the very basics”, and its opening sections are about what data science is before they are about any tool. It is not tied to an exam: the page claims to prepare you for no certification.
What you'll actually learn
The page gives two views of the course: six promised outcomes, and a curriculum of 64 sections, read in full on 26 September 2026. The lengths below are Udemy’s section lengths added up by part, so they are approximate.
The six promised outcomes, in the page's words
- “The course provides the entire toolbox you need to become a data scientist”
- “Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow”
- “Impress interviewers by showing an understanding of the data science field”
- “Learn how to pre-process data”
- “Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)”
- “Start coding in Python and learn how to use it for statistical analysis”
The 64 sections, part by part
- Part 1: Introduction, then seven sections on the field of data science: its disciplines, how they connect, their benefits, popular techniques and tools, careers, and common misconceptions. 8 sections, about 2h 28m.
- Part 2: Probability: combinatorics, Bayesian inference, distributions, and probability in other fields. 5 sections, about 3h 37m.
- Part 3: Statistics: descriptive statistics, inferential statistics and confidence intervals, and hypothesis testing, each followed by a practical example. 8 sections, about 3h 19m.
- Part 4: Introduction to Python: variables and data types, syntax, operators, conditional statements, functions, sequences, iterations and “Advanced Python Tools”. 9 sections, about 3h 12m.
- Part 5: Advanced Statistical Methods in Python: linear and multiple linear regression with StatsModels, linear regression with sklearn, logistic regression, cluster analysis, K-means and other types of clustering. 9 sections, about 4h 54m.
- ChatGPT for Data Science, then a case study: “Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis”. 2 sections, about 1h 52m.
- Part 6: Mathematics: one section of 11 lectures, 51 minutes.
- Part 7: Deep Learning: neural networks, one built from scratch with NumPy, TensorFlow 2.0, deep neural networks, overfitting, initialization, gradient descent and learning-rate schedules, preprocessing, classifying the MNIST dataset, and a business case example. 12 sections, about 4h 38m.
- Software Integration: one section, 30 minutes.
- A case study on absenteeism data: preprocessing it, applying machine learning to create an ‘absenteeism_module’, loading the module, and analysing the predicted outputs in Tableau. 5 sections, about 3h 21m.
- Appendices on additional Python tools, pandas fundamentals and working with text files in Python, then a bonus lecture. 4 sections, about 3h 44m.
So the course orients you and teaches probability and statistics before it asks you to code: Python starts in section 22, and the first 30 sections take about 12h 36m. Machine learning, in the page’s sense of regression and clustering with StatsModels and sklearn, is Part 5; deep learning with TensorFlow is Part 7, the longest part after it. Some tools the outcomes promise are small in the curriculum: Tableau is one section, the last of the case study, 6 lectures and 23 minutes, and pandas has a section title only as an appendix. None of the 64 titles names Excel, matplotlib or Seaborn, so the section list does not show where they are used.
The curriculum also holds what the outcomes leave out: the ChatGPT section and case study, and a Software Integration section. No section title covers building an application on a language model.
Read two outcomes as claims. “Impress interviewers” is a promise the page offers no evidence for. And the mathematics is not something “other courses don’t teach”: 365 Data Science's own Machine Learning Scientist track opens with Linear Algebra and Feature Selection and Math Foundation for ML. Part 6: Mathematics is a single 51-minute section, but that the course teaches the mathematics at all, not only the library calls, is still a point in its favour.
What the requirements ask for: Excel and Anaconda
The page lists three requirements, in full: “No prior experience is required. We will start from the very basics”; “You’ll need to install Anaconda. We will show you how to do that step by step”; and “Microsoft Excel 2003, 2010, 2013, 2016, or 365”.
- Anaconda installs Python and its data libraries on your own machine, and the course promises to walk you through it. You need a computer you are allowed to install software on; a locked-down work laptop may not qualify.
- Excel means Microsoft Excel, named by version, not any spreadsheet. None of the 64 section titles names Excel, so the page does not say which sections use it or what for. If you do not already have one of those versions, settle how you will get it before you buy.
- Tableau is named among the outcomes but not among the requirements. In the curriculum it is the last section of the absenteeism case study, “Case Study - Analyzing the Predicted Outputs in Tableau”, 6 lectures and 23 minutes, and the page does not say what you need to install, or sign up for, to follow it.
That is a real difference from the longest alternative below: DataCamp's Associate Data Scientist in Python track runs in the browser instead.
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 — from the same vetted list we rank from.
Try the AI Certification Picker →The details: cost, time, certificate
Cost. Sold per course on Udemy, bought once rather than subscribed to. This review prints no price: Udemy prices by country, so a single figure would be wrong for most readers. Check the price the course page shows you. Its includes box also lists “Optional: hands-on practice for an additional fee”; the page does not state that fee or say what the practice involves.
Time. Udemy lists 32 hours 25 minutes across 524 lectures, under four minutes each on average. Its includes box lists 32 hours of on-demand video, 88 articles, 130 coding exercises, 521 downloadable resources and 2 Role Plays, and the curriculum has 144 knowledge checks. That is the length of the content, not the work: the page gives no estimate of the time the exercises take, and a course that has you install Anaconda expects you to run code as well as watch it.
Certificate. Udemy's certificate of completion, its standard for a paid course. Accreditation was not checked and the page names no exam the course prepares you for. There are no practice tests, the knowledge checks show no question counts, and nothing on the page says the certificate depends on passing them. It shows that you finished a course, not what you can do.
Learner evidence. 162,263 ratings averaging 4.6 out of 5 from 818,885 learners, as Udemy showed them on 25 September 2026, on a course last updated that same month. That says the teaching works for most people who got far enough to rate it; it says nothing about how many reached the last of the 64 sections.
How we checked this. We list the cost as a one-off Udemy purchase, with no single figure published. Source: The course page, read logged out on 25 September 2026. Udemy prices each course by country and runs frequent site-wide sales, so a figure read in one place on one day is wrong for most readers; the course page shows the price for your country. 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 requirement is “No prior experience is required”, and the opening sections explain the field before any tool
- Covers the whole map in one purchase: seven parts run from probability and statistics through Python, regression and clustering to deep learning with TensorFlow 2.0, and a case study ends in Tableau
- Teaches the foundations before the code: probability and statistics are Parts 2 and 3, and Python starts in Part 4
- 162,263 ratings averaging 4.6 out of 5 from 818,885 learners, and updated in September 2026
Cons
- Needs setup the page leaves to you: an Anaconda install and a listed version of Microsoft Excel
- No practice tests and no question counts on its knowledge checks; the hands-on practice it lists is optional, for an additional fee the page does not state
- Its generative AI is one ChatGPT for Data Science section and a ChatGPT case study; no section title covers building on a language model
- The certificate is a Udemy completion record, tied to no exam, with accreditation unchecked
Who should take it (and who shouldn't)
Take it if you are starting from nothing and want to see the whole of data science — the field, probability and statistics, Python, machine learning and a first look at deep learning — before you commit to a longer path; if you have Excel and a computer you can install software on; or if you would rather pay once than keep a subscription running while you find out whether data science suits you. Our guide to becoming a data scientist shows where a first course like this fits.
Skip it if you need an assessed credential: nothing on the page ties the certificate to a score, and our list of vendor AI certification exams is where the assessed options are. Skip it if you already write Python and know your statistics, since the first 30 of its 64 sections are orientation, probability, statistics and introductory Python. And if your destination is building with language models, its generative AI is a ChatGPT section and a ChatGPT case study, and no section title covers building on a model; the AI Engineer Core Track on Udemy starts there, though it expects you to bring your Python with you.
One course or a 365 Data Science subscription?
365 Careers is behind both, and they are sold differently. The Udemy course is one purchase of one course. 365 Data Science is a subscription: you pay for access to its catalogue, which is organised into career tracks of ten or so courses aimed at one job title. Its pricing page states a free plan with course previews that needs no card, and a 30-day money-back guarantee on paid plans. We publish no 365 Data Science price, because its pricing page gave figures that did not agree with each other on the days we read it.
Of its career tracks, our catalogue holds two data-science routes:
- Machine Learning Scientist, 29 hours, which we publish as Intermediate. It opens with Linear Algebra and Feature Selection and Math Foundation for ML, then covers classical algorithms: Naive Bayes, K-nearest neighbours, decision trees and random forests, support vector machines, and ridge and lasso regression. No deep learning, and its page mentions no exam for the certificate.
- Senior Data Scientist, 34 hours, which we publish as Advanced. It covers the machine-learning process and then deep learning, in Deep Learning with Pytorch, Convolutional Neural Networks with TensorFlow in Python and Deep Learning with TensorFlow 2. Two of the ten courses its page lists are marked elective and one optional, and it promises a Certificate of Achievement with no exam mentioned.
For a beginner, the starting point decides it. The Udemy course asks for no prior experience; neither 365 data-science track we hold is pitched at a beginner. So they are less rivals than stages: the Udemy course is the one-purchase introduction, and a 365 subscription is for the next step, classical machine learning in depth or PyTorch and convolutional networks. Neither page we read says whether the course and the tracks share material, and this review does not assume they do.
Neither route's certificate is a strong signal. 365 Data Science advertises “accredited” certificates, but its own pages describe the bodies behind that word inconsistently, as our review of the platform sets out. If you expect to take one course and stop, buy one course. If you expect to keep going, a subscription from the same company covers the later steps; check the price its pricing page shows you in your own country first.
How it compares to the alternatives
Four routes into data science compared: this course and three career tracks, from 29 to 90 hours. The Udemy course is bought once and the other three are subscriptions; two of the four name deep learning, and we have scored only the two 365 Data Science tracks.
| Certification | Provider | Level | Time | Deep learning | How you pay | Rating | Enrol |
|---|---|---|---|---|---|---|---|
| The Data Science Course: Complete Data Science Bootcamp | Udemy | Beginner | ~32.42 hrs | TensorFlow 2.0, in Part 7 of its curriculum | Bought once | Not scored | Udemy → |
| Machine Learning Scientist (365 Data Science) | 365 Data Science | Intermediate | ~29 hrs | None in its ten courses | 365 Data Science subscription | 4.0 | 365 Data Science → |
| Senior Data Scientist (365 Data Science) | 365 Data Science | Advanced | ~34 hrs | PyTorch and TensorFlow courses | 365 Data Science subscription | 4.1 | 365 Data Science → |
| Associate Data Scientist in Python | DataCamp | Beginner | ~90 hrs | None in its 23 courses | DataCamp subscription | Not scored | DataCamp → |
DataCamp's Associate Data Scientist in Python is the like-for-like subscription route, and the longest here: 23 required courses and 90 hours, from Introduction to Python through pandas, cleaning and visualisation to a five-course statistics block and three scikit-learn machine-learning courses at the end. It runs in the browser, on a DataCamp subscription that DataCamp prices by country, at about $330 a year at US list price. Our review of the track sets out what it leaves out: no deep learning, no language models and no SQL course. Finishing it does not award DataCamp's Data Scientist Associate certification, which is a separate assessment.
Against this course, the DataCamp track is nearly three times as long. This course is shorter, bought once, and reaches further: its curriculum goes on to deep learning with TensorFlow 2.0 and a case study that ends in Tableau, neither of which the track covers. If you want to learn in the browser and can give it 90 hours, the track is the more thorough foundation; if you want a shorter overview that ends further along, on your own machine, the Udemy course fits better.
Is it worth it?
For a beginner who wants to see the whole of data science before choosing a direction, the page makes a good case: it starts from nothing, orients you in the field, starts the mathematics early, names a toolkit from statistics to TensorFlow, and carries a very large number of high learner ratings. Read in full, the curriculum is weighted to the foundations: the first 30 sections, about 12h 36m, come before regression and clustering, which take about 4h 54m, and deep learning about 4h 38m. What the course cannot give you is an assessed credential.
Buy it as a first course if you have Excel and can install Anaconda. Choose DataCamp's Associate Data Scientist in Python instead if you want a longer path in the browser, and treat a 365 Data Science subscription as a next step after this course rather than its rival. We have not scored it yet: until a course has been through our six-factor methodology, this page says “Not scored” rather than show a figure nothing supports.
Check price & enrol on Udemy →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
Is The Data Science Course by 365 Careers good for complete beginners?
Its page says it is: the first requirement reads “No prior experience is required. We will start from the very basics”, and we publish it as Beginner. Its first 21 sections explain the field of data science and teach probability and statistics before Python starts in Part 4.
What a beginner does need is equipment: a computer you can install Anaconda on, and one of the versions of Microsoft Excel the page lists.
Why does a Python data science course need Excel?
The page does not say. It lists “Microsoft Excel 2003, 2010, 2013, 2016, or 365” as a requirement beside the Anaconda install, but none of its 64 section titles mentions Excel, and neither do the six promised outcomes.
If you have no copy of one of those versions, open the sections on Udemy and look through the lecture titles for the ones that rely on it before you buy.
Is this the same as a 365 Data Science subscription?
No. Both come from 365 Careers, the company behind 365 Data Science, but this is one Udemy course, bought once, while 365 Data Science is a subscription to a catalogue of career tracks. The two data-science tracks in our catalogue are Machine Learning Scientist, at 29 hours, and Senior Data Scientist, at 34.
Neither page we read says whether those tracks share material with this course. Neither track starts from zero, so treat the Udemy course as the introduction and a 365 track as a possible next step.
Should I take this or DataCamp's Associate Data Scientist in Python?
Take this course for a shorter overview, bought once, that reaches further: its 64 sections run from probability and statistics through Python and scikit-learn to deep learning with TensorFlow 2.0 and a case study finished in Tableau, in 32 hours 25 minutes of lectures. Take the DataCamp track for a longer path: 23 courses and 90 hours, from Introduction to Python to three scikit-learn courses, with no deep learning.
The track runs in the browser; this course needs Anaconda and Excel on your own computer.
Does the certificate from The Data Science Course count for anything?
It is Udemy's certificate of completion, the standard one for a paid course. Accreditation was not checked and the page names no exam the course prepares for. The course has 130 coding exercises and per-topic knowledge checks but no practice tests, and nothing on the page says the certificate depends on passing any of them, so it tells an employer that you finished a course, not what you can do.
A project built with pandas, scikit-learn or Tableau says more. If you need a credential recruiters screen for, look at an assessed certification.
How long does The Data Science Course take?
Udemy lists 32 hours 25 minutes across 524 lectures in 64 sections. That is the length of the lectures, not of the work: alongside them the curriculum holds 130 coding exercises and 144 knowledge checks, and the page gives no estimate of the time those take, so plan for more than the lecture total.