Skip to content
#2 in Data ScienceUdemyData SciencePaid courseAll LevelsCertificate

The Data Science Course: Complete Data Science Bootcamp 2026 (Udemy.com)

Complete Data Science Training: Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning

Created by: 365 Careers

Last updated September 2026

Our take

Based on the ratings of 162,187 students, a sample of their written reviews and the syllabus, as the course stood in September 2026. No course pays to be reviewed.

365 Careers built this as a start-from-nothing route into data science, and it is ordered the way a university would order it. The first stretch covers the field itself, then probability, then statistics. Python only shows up around lecture 140, and regression, clustering and TensorFlow come after that. With 524 lectures, 274 quizzes and 130 coding exercises spread over about 32 hours, it suits someone with no background who wants the reasoning behind the models before the code. People who already have the stats will find the front half slow.

Students rate it 4.5 from more than 162,000 reviews, and nine in ten give four or five stars. The praise is consistent: the pace is measured, hard topics get broken into small pieces, and the quizzes actually help things stick. The complaints are just as consistent. Low-star reviews call it lengthy and scattered, with too much theory before anything practical. One reviewer says the coding exercises are fussy about the exact answer and don't match the videos. Another felt the math rigor of the statistics sections disappears once machine learning arrives, which turns into 'run this code' with less explanation behind it.

It was refreshed in September 2026, so nothing here is stale. Next to the Python for Data Science and Machine Learning Bootcamp in the same category, this is the pick for people who want a confidence interval explained before they run one. Treat it as an introduction rather than the complete bootcamp the title promises; the field is too big for 32 hours. For a patient beginner who won't skip the quizzes, it's a solid starting point, and an earlier CourseDuck review from 2021 found Q&A replies fast and reliable.

Best forBeginners who want the math and statistics explained before touching a machine learning library.
Skip it ifYou already know statistics and Python and want to build ML projects fast; the long theory front half will frustrate.

Pros

  • Probability and statistics get about seven hours of real teaching before any Python appears
  • 274 quizzes and 130 coding exercises; reviewers say the quizzes make the material stick
  • Pace is praised as neither too fast nor too slow for absolute beginners
  • Updated September 2026; an earlier CourseDuck review found Q&A responses fast and reliable

Cons

  • Long and theory-heavy up front; several low-star reviews lost interest before the practical parts
  • One reviewer says the coding exercises are strict about exact answers and don't match the video examples
  • Machine learning sections lean on running code without the math depth the statistics sections had
This course/ bootcamp is perfect for beginners wanting to learn Data Science. The pace is not too fast or slow. Also, the material is well explained.
Sade McClain, a student

Listed as all levels, but it truly starts from zero; anyone with a stats background can skip the first 140 lectures.

icon
What you will learn

  • 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
  • Perform linear and logistic regressions in Python
  • Carry out cluster and factor analysis
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Apply your skills to real-life business cases
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Unfold the power of deep neural networks
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Course content

29 sections · 524 lectures · 32 hours of video 274 quizzes, 130 coding exercises, 88 articles

  1. 1Part 1: Introduction 2 free previews4 lectures · 24 min
  2. 2The Field of Data Science - The Various Data Science Disciplines 2 free previews7 lectures · 6 quizzes · 39 min
  3. 3The Field of Data Science - Connecting the Data Science Disciplines1 lecture · 7 min
  4. 4The Field of Data Science - The Benefits of Each Discipline1 lecture · 1 quiz · 5 min
  5. 5The Field of Data Science - Popular Data Science Techniques12 lectures · 7 quizzes · 1 hour
  6. 6The Field of Data Science - Popular Data Science Tools1 lecture · 1 quiz · 6 min
  7. 7The Field of Data Science - Careers in Data Science1 lecture · 1 quiz · 3 min
  8. 8The Field of Data Science - Debunking Common Misconceptions1 lecture · 1 quiz · 4 min
  9. 9Part 2: Probability4 lectures · 4 quizzes · 23 min
  10. 10Probability - Combinatorics11 lectures · 9 quizzes · 43 min

Who it is for

The instructor says it suits

  • You should take this course if you want to become a Data Scientist or if you want to learn about the field
  • This course is for you if you want a great career
  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

What you need before you start

  • 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
  • Microsoft Excel 2003, 2010, 2013, 2016, or 365

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.5 out of 5higher than 46% of them
  • Reviews162,187#1 of 462 by number of reviews
  • Length32.4 hourslonger than 90% of them
  • Last updatedSeptember 2026more recently than 92% of them

icon
Quality Score

Content Quality
/
Video Quality
/
Qualified Instructor
/
Course Pace
/
Course Depth & Coverage
/

Overall Score : 90 / 100

icon
Course Description

The Problem

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.     

However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.    

And how can you do that?  

Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)   

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture  

The Solution   

Data science is a multidisciplinary field. It encompasses a wide range of topics.   

  • Understanding of the data science field and the type of analysis carried out  

  • Mathematics  

  • Statistics   

  • Python   

  • Applying advanced statistical techniques in Python   

  • Data Visualization  

  • Machine Learning  

  • Deep Learning  

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.   

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2024.   

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.  

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).   

The Skills

   1. Intro to Data and Data Science

Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?     

Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.  

   2. Mathematics 

Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.   

We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.   

Why learn it?  

Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

   3. Statistics 

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.  

Why learn it?  

This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

   4. Python

Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.

Why learn it?   

When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.  

   5. Tableau

Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.

Why learn it?   

A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.  

   6. Advanced Statistics 

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.  

Why learn it?  

Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.  

   7. Machine Learning 

The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.  

Why learn it?   

Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.  

**What you get**

  • A $1250 data science training program   

  • Active Q&A support  

  • All the knowledge to get hired as a data scientist  

  • A community of data science learners  

  • A certificate of completion   

  • Access to future updates  

  • Solve real-life business cases that will get you the job   

icon
Udemy Discount

The discount is applied through our link. Open the course from here and Udemy's current promotional price is applied at checkout on most courses, no code to type.

Some courses are excluded from Udemy's promotions. If the price does not drop, clear your browser cookies and use the button again.

icon
Instructor Details

365 Careers

365 Careers is the #1 best-selling provider of business, finance, data science and AI courses on Udemy. The company’s courses have been taken by more than 4,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.    

Currently, 365 focuses on the following topics on Udemy:    1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA

2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy

3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing

4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook

icon
More courses by 365 Careers

$199.99

$19.99

$19.99

$159.99

$199.99

$74.99

icon
Reviews

4.5

162,187 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Erick Ricardo Lopez Gomez on 7/14/2026

una gran cantidad del contenido son videos con IA, se siente profundamente impersonal y lento, obtengo mejores resultados literalmente copiando el tema de cada video y pidiendo a cualquier asistente que lo explique en texto de manera concisa y tomando notas de eso, lo leería muchísimo más rápido sin tener que esperar el tiempo de cada video, además de que el método de estudio no está al nivel del tema estos son videos extremadamente sencillos sin documentación que uno pueda leer, literalmente es sentarse a escuchar a una IA hablar mal y luego responder preguntas hechas con IA después de cada video, muy mala experiencia.

By Pieter van der Merwe on 7/9/2026

The course was quite good, I would just have appreciated a bit more of an in depth transition from the background stats work to actual ML and deep learning and the background of this. I feel like a lot of time is spent on trying to give you some stats background and then the overall ML topics are just dumped on you from a do this code framework, without maintaining the same level of background mathematics introductions.

By Nooruldeen Qutaiba Ismaeel on 7/7/2026

Excellent work, it contains most important things for learning computer science from zero to high level, even if some parts for me are hard to understand but I am working on it and repeating the section. thanks

By onje ndabeni on 6/30/2026

the course was very good ,everything was broken into understandable pieces ,the quiz questions really have a wonderful impact unto ones understanding and application of what has been taught, thank you 365 careers

By mario branca on 6/20/2026

yes it was, i got a degree in computer science but i wanted to learn without too much effort while i do other things. So I decided for this course, so i can remember some things about probability and statistics, and get into rhythm.

By Jeffrey Jones on 3/10/2026

Very well presented and delivered. Plenty of examples to watch/mimic, exercised to practice, and visuals to comprehend. I highly recommend this course for those wanting to learn data science from scratch.

By Shubham Singh on 2/23/2026

Content could be more structured, when starting linear regression, could have told there we need to use minmax scaler so that higher no/ weights are scaled accordingly

By Tuncay Şahin on 2/19/2026

Kendi öğrenme sürecimde özellikle içerik akışının sistemli olması ve egzersizlerin konuları pekiştirmesi benim için oldukça faydalı oldu. Öğrenilen bilgilerin uygulanabilir olması önemliydi. Veri bilimine geçiş sürecimde güçlü bir temel oluşturduğunu düşünüyorum.

By Manuel Vicente Garcia Rodriguez on 2/12/2026

This is a long course. It does deliver, though. It is _all_ the basics to get into Data Science. It covers it all. If anything, I would miss a little bit more depth in python visualisation, but there are so many flavours out there, that you are better off with a specific course on your tool of choice. One tiny detail is that if you take the exercises seriously, the course takes a lot longer than 32 hours. And yes: definitely you should take the effort to do all exercises and then experiment a little on the side, for a lasting learning. If you are serious about getting into Data Science, this is the course I'd recommend you.

By Alejandro Pérez Belando on 1/20/2026

All the concepts are clearly presented and is a very good starting point for those who want to start learning data science and machine learning. Thank you very much!

Showing all 10 reviews on CourseDuck

Read all 162,187 reviews on Udemy