17 Best Data Science Courses Online in 2025 [Free + Paid]

In this article, we share the 17 best data science courses in 2024. Whether you’d like to land a job as a data scientist or want to further your career by learning new skills, we’ve included data science courses for all skill levels that are both free and paid.

In 2024 and beyond, data science continues to be essential for modern businesses that want to capitalize on the hidden insights within their data, and taking one of the best data science courses is an excellent way to enter the field.

And when you consider that the Bureau of Labor Statistics reports an average salary of more than $100,000 for data scientists, taking one of the best data science courses can be lucrative for your career prospects.

If you’re asking yourself the question, how can I learn data science? One of the best approaches is to take one of the best data science courses. You can also combine this with building data science projects to round out your learning journey.   

Taking the best data science courses can also help strengthen your skills before taking exams for data science certifications.

So, if you’re ready, let’s dive into the best data science courses in 2024 to help you learn the skills you need to enter the data science job market.

Featured Data Science Courses [Editor’s Picks]

• [Udemy] The Data Science Course: Complete Data Science Bootcamp
• [Coursera] Data Science Specialization
• [DataCamp] Introduction to Data Science in Python

17 Best Data Science Courses in 2024

1. [Udemy] The Data Science Course: Complete Data Science Bootcamp

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Why we chose this course

Our findings show that this comprehensive data science class starts with an in-depth introduction to data and the field of data science before covering the essential components of modern data science, including statistics, math, and data visualization. You’ll also delve into machine learning and deep learning.

This course also includes a section on programming with Python to help you apply Python for linear regression, logistic regression, cluster analysis, and k-means clustering.

You’ll also be diving into Python’s rich ecosystem of data science libraries by getting hands-on with essential data science tools like Pandas, NumPy, Matplotlibs, and Seaborn. You’ll also learn to use Scikit-learn, TensorFlow, and Tableau to improve your data science skills. 

We also like that this course allows you to practice the Python concepts you’ve learned with real-life case studies. Overall, this is an ideal data science course for beginners as the instructors let you begin with the basics and build up as you progress.

Another aspect we really like about this course is that the creators maintain a vibrant community of data science students where you can interact with coursemates. They also have an active Q&A support forum where students can ask questions.

Pros

• Suitable for beginners
• Includes real-life business cases
• A vibrant community of students
• Active Q&A support

Cons

• None

2. [Coursera] Data Science Specialization

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Why we chose this course

Our findings show that this data science training is designed and taught by renowned professors from John Hopkins University. With over 280 hours of content, this data science specialization path has a comprehensive curriculum made up of 10 courses.

You will begin by learning how to use R and GitHub to manage your data science projects. And then, you will delve into how to extract usable data from the web, APIs, and databases. And also learn the principles involved in organizing data for analysis.

Other sections cover various topics like statistical inference, regression models, and machine learning concepts like overfitting, classification trees, and prediction functions. If some of these areas are new to you, consider including data science books in your study regime.

We also liked that the capstone project requires you to use real-world data sets to build a usable data product. And one of the requirements for the project is to create a presentation deck to showcase your findings. 

Another unique feature we discovered about the curriculum is that you will get graded data science quizzes and assignments with feedback from peers and instructors.

Pros

• It’s a comprehensive data science course
• Taught by Instructors from John Hopkins University
• Graded data science quizzes/assignments with feedback
• Capstone project you can showcase to potential employers

Cons

• The capstone project requires skills not covered in the course

3. [DataCamp] Introduction to Data Science in Python 

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Why we chose this course

This will be the ideal course for you if you are curious about data science but not yet ready to invest any huge time and effort to study. With just 4 hours of content, the entire curriculum can be completed in a day or two. And it will give you enough information to decide whether to take a more in-depth course.

Our findings revealed that the course instructor is an engineering manager at Google. Also, if you consider yourself a visual learner, this course is even more helpful as the video lessons include loads of colorful images and illustrations. 

The course provides a concise introduction to data science in Python. It begins with lessons on the basics of Python. And then, you will learn how to load data in pandas and plot data with matplotlib. For the final section, you will practice creating three plot types: scatter plots, bar plots, and histograms. 

Hackr readers can also access an exclusive 25% discount on the annual Learn Premium and Teams subscriptions.

Pros

• Ideal for complete beginners
• Video lessons include many images and illustrations
• Very short course (about 4 hours)

Cons

• The course is quite brief, so it doesn’t go into as much detail as others on our list

4. [TripleTen] Data Science Bootcamp

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Why we chose this course

Next up on our list is a fully-fledged online bootcamp in the form of the Data Science Program by TripleTen.

This really stood out to us for being a comprehensive 8-month journey into data science for students from diverse technical backgrounds. Plus, you don't need any prior experience in math, stats, or coding, which is really impressive.

This program is structured to transform you into a highly paid professional by covering a vast array of subjects from Python and Pandas fundamentals to advanced topics such as Machine Learning and Neural Networks.

It's also great to see that you'll complete 16 portfolio-worthy projects to boost your portfolio and help you gain real hands-on skills. I'm also really impressed by their externship concept, where you'll carry out a range of real-world projects at real companies.

These are the types of detail that will make your resume really stand out from the crowd.

This program also emphasizes the development of both technical and soft skills, preparing you for the professional world with abilities like time management, teamwork, and industry-specific practices.

The overall curriculum is divided into sprints, simulating a real-world tech company environment, and it's recommended you spend around 20 hours of study per week.

But, if you can put in the time, this boot camp also includes a dedicated employment preparation component featuring career mentoring, a Career Prep Course, Career Acceleration Externships, and Post-Offer Career Support, ensuring you are well-equipped for your job search and early career stages.

You also get the added benefit of TripleTen's money-back guarantee, where they'll refund you the course cost if you can't land a job six months after graduating.

But given their impressive 86% employment rates, with students landing roles with major tech companies like Tesla, Google, and Spotify, this program really sets itself apart if you're really dedicated to breaking into the world of data science from a standing start.

Pros

• In-depth data science education with 16 practical projects
• Balances technical skills with essential soft skills
• Real-world, sprint-based learning approach
• Extensive career support and mentoring

Cons

• None

5. [Coursera] Data Science Fundamentals with Python/SQL

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Why we chose this course

Our analysis of this online data science training revealed that it is designed to equip you with the skills you need to tackle advanced data science projects. Through our research, we discovered that this course is taught by senior data scientists from IBM.

The course is made up of five mini-courses. By the end of this first course, you will have a working knowledge of data science tools such as Jupyter Notebooks, R Studio, and Watson Studio.

The second course will teach you how to use Python for data science. It covers data structures, calling APIs, and using libraries like Pandas and NumPy. You will then work on a data science project in the third course, where you will be required to identify patterns and trends from a real-world data set. 

The fourth and fifth mini-courses cover statistical analysis techniques and SQL for data science. Some of the topics you will learn are hypothesis testing, descriptive statistics, probability distribution, regressions, and data visualization.

Pros

• Taught by senior data scientists from IBM
• Hands-on exercises with real-world data sets
• Gain a working knowledge of various data science tools

Cons

• Some slides contain spelling mistakes  

6. [edX] Harvard Professional Certificate in Data Science

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Why we chose this course

This professional certificate in Data Science is offered by the Computer Science faculty of Harvard University on edX. It starts with an introduction to the basics of R programming. And also includes lessons on data visualization, bayesian statistics, probability, data wrangling, linear regression, inference, and predictive modeling.

After completing this course, you will know how to use data science tools like Tidyverse and ggplot2. The exercises will also give you practical experience using Unix/Linux, RStudio, Git, and GitHub.

We also like that this course includes a section on machine learning where you will use the data science techniques you’ve learned in previous sections to build a movie recommendation system. 

There's also a final capstone project that requires you to build a data product that you can include in your portfolio to demonstrate your skills to potential employees.

This course requires no prior knowledge of data science or programming, making it ideal for complete beginners. Also, there is an active community of students from around the world you can network with. And it’s also easy to get help when stuck on something.

Pros

• Taught by instructors from Harvard University
• An active community of students
• Build projects for your portfolio

Cons

• Course duration may be too long for some

7. [Udemy] Data Science A-Z: Hands-On Exercises and ChatGPT Bonus

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Why we chose this course

Based on our observations, this is one of the best data science programs online. The course is created by Kirill Eremenko and his team. Kirill formerly worked at Deloitte and has taught over 2 million students on Udemy. 

We like that the content is divided into four sections to cover data visualization, modeling, data preparation, and communication. By following the course sections in sequence, you’ll learn the core skills of data science, including cleaning and preparing your data for analysis, creating basic visualizations, modeling your data, and curve-fitting.

You’ll also go in-depth into data mining with Tableau, along with how to build models with linear and logistic regression. You’ll also learn how to use a Cumulative Accuracy Profile (CAP) to assess your model. We also appreciated that the instructor mimics real-life business scenarios when teaching you about data preparation. 

Other key areas covered in this include SQL programming for data science, business intelligence tools, and the importance of ETL pipelines for data science, both pre and post-transformation.

At the end of this course, you also benefit from detailed lessons in communication, including tips and tricks on presentation and storytelling in data science. We really like this, as at their core, data scientists are storytellers, making these essential skills.

Pros

• Includes data science assignments with solutions
• Suitable for both beginners and advanced learners
• Learn data science presentation skills
• Uses real-life datasets

Cons

• None  

8. [Educative] Grokking Data Science

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Why we chose this course

Based on our experience with other educative courses, we know that this data science course is 100% text-based. This makes it an ideal choice for those who prefer to learn by reading. Our research also revealed that the creator of this course is a senior software engineer at Microsoft. 

In this course, you will learn Python for data science, data visualization, and the fundamentals of statistics with topics like probability, Bayesian statistics, and machine learning algorithms. And you will also learn how to use popular Python libraries like Pandas, Numpy, and Matplotlib. 

The course also includes sections on machine learning where you will learn about machine learning algorithms and evaluating a model. There is also an end-to-end machine learning project, where you will learn about exploratory data analysis techniques, data processing, and fine-tuning parameters among others.

Each section of this course includes quizzes with answers and challenges to help you practice the concepts you learn. The final part of the course provides tips for landing a high-paying data science job and overcoming imposter syndrome.

Pros

• Ideal for those who prefer learning by reading
• Created by a senior engineer at Microsoft
• No IDE setup is required for coding practice
• Each section includes quizzes with answers

Cons

• Not ideal if you prefer video lessons

9. [Udacity] Data Science Program

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Why we chose this course

Udacity’s Data Science Nanodegree program provides a hands-on approach to learning data science. This program will help you master topics such as natural language processing (NLP), running pipelines, transforming data, building models, designing experiments, and deployment.

Some of the projects you will build in this course include a recommendation engine, a disaster response pipeline, and a final capstone project of your choosing. 

Also, as part of the curriculum, you will be required to publish a data science blog post to practice your communication and data visualization skills.

You will also complete several lessons designed to help you develop software engineering skills that are essential for data scientists, like creating unit tests, code review, building, and using classes. 

Our research revealed the instructors for this course include senior data engineers from top tech companies such as Google and Netflix. You will also have access to career services, including GitHub portfolio review and LinkedIn profile optimization, to help you land a data science job and prepare for data science interview questions.

Pros

• Hands-on approach to learning data science
• Created by Senior Data Engineers from Google and Netflix
• Suitable for intermediate and advanced learners
• Build projects for your portfolio
• Access to career services

Cons

• None

10. [Turing College] Data Science Career Program

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Why we chose this course

As we evaluated this data science bootcamp, we realized the enormous value of working with professionals in the field. Turing College offers certifications for their graduates, but the real value comes from the one-on-one mentorship, interview prep, and help finding a job with your new skills.

This deep learning bootcamp is fully online, and it offers real-world benefits after you graduate. We rarely see a data science program boast these types of hiring rates. It ranks highly for our recommended deep-learning courses.

Pros

• More than 96% of Turing College graduates get a job within 6 months of graduation
• Includes a special focus on deep learning
• Helps students prepare a portfolio of their own work
• Mentorship from industry professionals
• Help with interview prep and salary negotiations

Cons

• Because it's a data science bootcamp, it has a higher cost than others we discuss

11. [StackSocial] The A to Z Data Science & Machine Learning Bundle

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Why we chose this course

With this data science bundle, you get 7 separate courses, allowing you to curate your own data science learning journey. We also like that the StackSocial platform includes a note-taking area under the videos to help you keep track of important aspects of new or challenging topics. 

If you’re brand new to data science and programming, two courses focus on the fundamentals of Python and R. If you start with Python, you get a 6-hour introduction to Python fundamentals before covering the basics of NumPy, going in-depth with Pandas and briefly covering visualization with Maptlotlib.

Alternatively, there’s a comprehensive and practical 22-hour course on the practical side of data cleaning, processing, wrangling, manipulation, and visualization with R. You’ll also cover topics like vector coercion, data frames, R markdown, and more. 

If you have Python experience, there’s a 1-hour course on NumPy skills for data scientists and a nearly 10-hour course on using the Streamlit library to create data science and machine learning apps. Here you’ll learn to integrate with Matplotlib and Plotly and create an NLP application with hugging face transformers. 

Something we really like is the 15 hours of course material on applied probability and statistics for data science, as these are essential skills to pursue a career in data science. 

These use a code-oriented approach with Python and NumPy to teach stats and probability fundamentals like random values, spread, central tendency, one-hot encoding, Bayesian inference, regression, and more.

To cap things off, there’s a 5-hour course on Deep Learning with Keras, a high-level neural network library that runs on TensorFlow. Expect to learn about artificial neurons, activation functions, optimization and loss functions, classification, and more.

Pros

• Suitable for beginners with no experience in programming or data science
• Covers the two dominant languages for data science, Python and R
• Note-taking areas under each video to keep track of challenging areas
• Includes course material on statistics and probability concepts

Cons

• Lack of test exercises compared to others in our list

12. [LinkedIn Learning] Data Science Foundations

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Why we chose this course

This data science training begins with an overview of what data science is, before exploring the place of data science in artificial intelligence, machine learning, and deep learning.  Our research also discovered that the instructor is also the founder of DataLab.

Some of the topics you will learn in this course include Bayes theorem, unsupervised learning, supervised learning, mathematics for data science, and interpretability methods. You’ll also get an overview of generative methods in data science like generative adversarial networks(GANs) and reinforcement learning. 

You will not only delve into the technical aspects of data science, but this course will also teach you about the ethical and responsible use of data. You will explore concepts like bias, explainable AI, security, and legal considerations in data science.

At the end of each chapter, there is a quiz to help you gauge your level of understanding of the lesson presented in that chapter. This course is suitable for beginners as the instructor makes no assumption of prior knowledge in data science.

Pros

• Suitable for beginners
• Concise videos with clear explanations
• Includes quizzes for each chapter

Cons

• Lack of community

13. [Simplilearn] IBM Data Scientist Program 

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Why we chose this course

Our research revealed that this IBM-partnered program offers a comprehensive learning package that includes live online classes, hackathons, webinars, and AMA sessions. 

We like that the live classes give you direct access to instructors, some of which are senior data scientists and engineers from IBM.  This also gives you a unique opportunity to interact with other students.

The course is designed to help you master job-critical skills like supervised and unsupervised learning, hypothesis testing, data mining, clustering, linear and logistic regression, data wrangling, data visualization, and more. 

Some exciting projects you will build for your portfolio are a model to predict diabetic patients, a sales performance module, and a user-based recommendation model among others.

By the end of the course, you will be familiar with programming languages like Python, R, and Scala and also have a working knowledge of data science tools like Apache, Tableau, Spark, HBase, Sqoop, Hadoop, and Flume.

Pros

• Live access to instructors
• Participate in IBM hackathons
• An active community of students
• Learn Python, R, and Scala

Cons

• The cohort-based schedule may not be flexible for some

14. [Simplilearn] Data Science Full Course 2024

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Why we chose this course

In this course, you will learn what data science is, what data scientists do, and a step-by-step guide on how to become a data scientist. 

The instructors also provide in-depth explanations of various terms like artificial intelligence, machine learning, and deep learning and the differences between them. 

Our findings also show that this course covers essential topics like model building, distribution in statistics, Bayes theorem, machine learning algorithms, deep learning neural networks, and binomial distribution. 

You will also get an overview of useful libraries like TensorFlow, Numpy, Scipy, Pandas, and Matplotlib.

This course is a great option for beginners and those who want to polish their data science knowledge for an upcoming interview. There is even a section dedicated to helping you prepare for common data science interview questions along with a tutorial on creating a resume.

Pros

• Ideal for complete beginners
• Includes a section on data science interview prep
• An in-depth explanation of key terms
• Free and easily accessible on YouTube

Cons

• No certificate of completion

15. [Springboard] Data Science Prep Course 

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Why we chose this course

Our findings revealed that this course is a preparatory data science course for aspiring data scientists. No prior knowledge of data science or programming is required, and taking this course will equip you with the knowledge and skills you need to tackle more advanced data science courses. 

It’s made up of eight parts that will walk you through fundamental data science concepts, including programming and its importance in data science, Bayes theorem, and conditional probability. 

By the end of the course, you will also have a working knowledge of data science tools like Numpy, Pandas, Anaconda, Jupyter Notebooks, Git, and GitHub.

As part of the course, students will also work on an app project to solve a real business problem using data from Google and Apple. 

Another advantage of taking this course is the one-on-one mentorship with mentors from renowned tech companies like Uber. You will also have access to the data science career coaching program and a vibrant peer community.

Pros

• Build an app to solve real business problems
• Access to a vibrant peer community
• One-on-one mentor support

Cons

• None

16. [Edureka!] Data Science for Beginners 

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Why we chose this course

This will be a great choice for beginners looking for a free course to get started with their data science journey. After explaining data science, the instructor provides a comprehensive roadmap for aspiring data scientists.

This training features over 11 hours of content, covering important topics such as the confusion matrix, Bayes theorem, the Bellman equation, and inferential statistics. 

You will also learn advanced ML and DL topics like regression, KNN algorithms, decision tree algorithms, reinforcement learning, and TensorFlow code basics. 

Our research also revealed that the curriculum includes many use-case sections, which give you the opportunity to put concepts into practice. You also get interview prep and help with creating a data science resume. 

Pros

• Suitable for complete beginners
• Includes a section on data science interview prep
• Includes a comprehensive data scientist roadmap
• Free and easily accessible on YouTube

Cons

• No certificate of completion

17. [Codecademy] Data Science Foundations

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Why we chose this course

Codecademy’s data science foundations course begins by teaching you the principles of data literacy before moving on to topics like the fundamentals of statistics for data science and communicating data science findings. 

Based on our observations, this course also teaches you about exploratory data analysis (EDA) techniques and data wrangling, culminating with lessons on popular Python tools like Pandas and Matplotlib.

We like that the curriculum takes a project-based approach, as you’ll be building 34 mini-projects to help you practice the theory you’re learning. 

You’ll also work on two major projects to include in your portfolio, including a project to sort and analyze U.S. medical insurance costs and another to interpret data about endangered animals. These are great ways to get to grips with real-world data problems.

Pros

• Build projects for your data science portfolio
• Ideal for complete beginners
• Learn best practices for communicating data science findings
• Includes quizzes at the end of each unit

Cons

• No access to instructors to ask questions

How to Choose the Best Data Science Course in 2024

When choosing the best courses for data science, you’ll want to find one that matches your personal learning goals while blending data science theory with practical skills. 

When reviewing the best course to learn data science, we considered the following criteria and recommend you do the same:

• Accreditation and Reputation: We emphasized online courses for data science from reputable institutions and online learning platforms.
• Curriculum and Topics Covered: We evaluated course curriculums to ensure they covered essential Deep Learning concepts.
• Practical Exercises and Projects: We looked for courses that included hands-on experience, whether via practical exercises or projects.
• Instructor Expertise: We looked for course instructors with relevant practical knowledge and industry experience.
• Student Reviews and Testimonials: We analyzed reviews and testimonials from previous students to gauge the overall learning experience.

Do I Need To Know AI To Become a Data Scientist?

One thing is for sure: we're all slowly becoming acquainted with the various forms of AI in our professional and personal lives.

But do you need to be an AI wizard to excel in data science in 2024? Interesting question: let's dive deeper.

In the constantly evolving landscape of data science, the integration of artificial intelligence has become increasingly significant, and there is no doubt about that.

And while data science and AI are distinct fields, their intersection is becoming hard to ignore.

As you probably already know, data scientists are tasked with the responsibility of extracting actionable insights from data.

This involves not just the manipulation of structured and unstructured data but also the application of advanced analytical techniques. AI, particularly in the form of machine learning, plays a pivotal role in this process.

This enables us as data scientists to create predictive models that can learn from data, thereby enhancing the accuracy and effectiveness of their analyses.

Understanding AI principles, especially machine learning algorithms, can also immensely benefit us as data scientists.

This knowledge allows us to automate complex data processes, optimize predictive models, and implement solutions that are both innovative and efficient.

And while not every data science role will demand deep expertise in AI, a foundational understanding is becoming increasingly important in the field.

If you're curious to learn more about the transformative power of generative AI across various industries, I'd highly recommend attending DataCamp's free digital conference, RADAR: AI Edition in June 2024.

This event is a fantastic opportunity to discover how businesses and individuals can unlock their full potential with AI. You'll hear from industry leaders like Megan Finck, the Global Head of Talent Acquisition (Data & AI) at Boeing, and Sadie St Lawrence, the CEO of Women in Data. Nnamdi Iregbulem, Partner at Lightspeed Venture Partners, and Eric Seigel, Founder of Machine Learning Week and bestselling author, will also share their insights.

Additionally, Carolann Diskin, Senior Technical Program Manager at Dropbox, and Julien Simon, Chief Evangelist at Hugging Face, will discuss the future of AI and its applications. Don't miss this chance to learn from experts about the latest advancements and strategies in AI.

Wrapping Up

And there you are, the 17 best data science courses in 2024, including a range of data science courses for beginners and experienced pros alike. Whether you’re just starting out in your data science career or want to level up your existing skills, we’ve included a range of data science courses to help you achieve your goals.

Happy learning!

Want to enhance your data science skills with Deep Learning? Check out:

Coursera’s Deep Learning Specialization with DeepLearning.AI

Frequently Asked Questions

1. What Course is Best for Data Science?

The best data science courses online depend on various factors like your goals, skill level, and preferred style of learning. We’d recommend reviewing each of the data science courses on our list, but if you’re a beginner, perhaps start with Udemy’s data science training course, and if you’re more experienced,  Udacity's data science nanodegree is a solid option.

2. What Course is Best for Data Science Beginners? 

It’s not possible to select a single course from the best data science courses for beginners, as this depends on your previous experience, preferred learning style, and career goals. That said, we’ve included a range of excellent beginner courses, including excellent options like Coursera’s data science fundamentals.

3. Does Data Science Require Coding?

Yes. Data science requires you to have a grasp of programming languages like Python and R to manipulate and analyze datasets, build models, and create machine learning algorithms.

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