If you want to become a data scientist, there’s no better time than now. The U.S. Bureau of Labor Statistics projects a 35% growth in employment for data scientists from 2022 to 2032, with about 17,700 job openings expected annually over the decade​
But here’s the catch—while this demand skyrockets, finding the right path to becoming a data scientist can be challenging. Do you invest in a college degree, plunge into an intensive data science bootcamp, or start with a free online course? In this blog, we’ll look at some of the best free data science courses available for those on a budget or just starting out.Â
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Free Data Science Courses
Here are our top picks for the best free data science courses.
Springboard
The Springboard Data Analytics course takes students from various backgrounds and levels of expertise through the ins and outs of data science. It has a holistic approach and includes 25 learning units designed by industry experts.
The course is particularly special because it helps students integrate what they learn within their projects and teaches them essential tools and programming languages such as Python, Excel, or Tableau. The students are encouraged to practice their new job-ready skills through real-world projects to be prepared for a job after graduation.
RATING
4.6/5 @ Course Report
FEATURES
- Covers important topics including data visualization, machine learning, probability, and statistics
- Students engage in a special case study, applying learned concepts to real-life scenarios
- Teaches visualization techniques to transform structured data into insightful visuals
- Includes an introduction to the foundations of machine learning
- Includes insights into effective data presentation techniques
SUPPORT
Both the paid data science bootcamps and the free courses from Springboard give students access to industry experts and dedicated career advice and guidance to help them navigate job markets or improve their data science resume. When you complete the course, you get a certificate that can be added to your portfolio.
IBM on Coursera
Coursera is a platform that hosts a variety of valuable courses, and the IBM Data Science course is no exception. It requires no prior experience and has a distinct approach that prepares students for career opportunities in data science in as little as five months.
It’s taught by renowned instructors, such as Dr. Pooja, Romeo Kienzler, and Joseph Santarcangelo, who bring a ton of knowledge and experience to the program. Some students have provided feedback that while the visualization component of the course is not the best, the rest of the program is beginner-friendly and includes an extensive range of topics, from Python programming and big data to machine learning and data mining.
RATING
4.6/5 @ Coursera
FEATURES
- Learn tools and languages like Python, SQL, Github, and Rstudio
- Apply skills relevant to data-driven decision-making to real-world projects and build a data project portfolio
- Learn at your own pace with a schedule that accommodates 10 hours a week over 5 months
- Engage with Jupyter Notebooks and data mining tools
SUPPORT
Due to the nature of the platform, the IBM course gives you access to a variety of support channels. You’ll get to earn a shareable certificate to add to your LinkedIn profile and be able to earn up to 12 college credits upon completion.
freeCodeCamp
freeCodeCamp hosts a series of 5 video courses on YouTube, spanning over 20 hours of content. These courses are structured to cater to both beginners and those looking to deepen their knowledge in specific areas, such as:
1. Learn Data Science Tutorial – Full Course for Beginners. This course, instructed by Barton Poulson, is a non-technical introduction to data science. It covers data science essentials, focusing on coding, math, and statistics in applied settings. The course emphasizes inclusive analysis using all available information and explores the high demand for data science skills in the job market.
2. Python for Data Science – Course for Beginners. A beginner-focused course that lays the foundation of Python programming in the context of data science. It starts from the basics, covering installation and variables, and progresses to complex data structures and data science packages in Python.
3. Statistics – A Full University Course on Data Science Basics. Monica Wahi’s course explores the science of uncertainty, the technology of extracting information from data, and the application of statistics in healthcare and public health.
4. Build 12 Data Science Apps with Python and Streamlit – Full Course. Led by Chenin Ahmad, this course teaches how to build 12 interactive data-driven web applications using Python and the Streamlit library.
5. Tableau for Data Science and Data Visualization – Crash Course Tutorial. This tutorial focuses on Tableau, a key tool in data science for data exploration and reporting.
RATING
NA
FEATURES
- Courses are taught by experienced professionals in the field
- Over 20 hours of content combined
- Each course provides an in-depth exploration of its topic
- Learn at your own pace with no set schedule
SUPPORT
No direct support is offered since the courses are hosted on YouTube
UC Davis On Coursera
Another data science course on Coursera, UC Davis’ “SQL for Data Science,” is an exceptional program that gives you a foundational understanding of SQL, an essential tool for data science. It teaches students to retrieve and work with data efficiently and has a practical approach to teaching SQL.
It starts with the basics and progressively guides students through more complex queries, helping them make informed decisions and build technical skills in filtering, sorting, summarizing data, and creating analysis tables.
RATING
4.6/5 @ Coursera
FEATURES
- Focuses on real-world applications of SQL in data science
- Approximately 18 hours of content with a self-paced learning structure
- Earn a certificate that can be added to LinkedIn and resumes
- Includes 15 quizzes
- This course is a part of the “Learn SQL Basics for Data Science Specialization”
SUPPORT
On completion of this course, students get a certificate. They are able to engage with the instructor through the platform and collaborate and interact with the community of fellow learners.
MITx: The Analytics Edge
The MITx: The Analytics Edge course is taught by well-renowned instructors from leading institutions and explores the power of data analytics through real-world examples – from game-changing strategies of Moneyball to IBM Watson and Netflix’s recommendation algorithms.
Students engage and apply analytics methods such as linear regression, logistic regression, and clustering to diverse datasets. They also have access to structured lecture videos, quick questions to reinforce learning, and hands-on recitations.
RATING
4.6/5 @ Class Central
FEATURES
- Learn through real-life examples like eHarmony, Netflix, or Moneyball.
- Hands-on experience with R.
- Expert instructors from MIT, Stanford, or the University of Michigan’s Ross School of Business.
- Interactive learning materials.
- Final exam and award recognition.
SUPPORT
Students can expect guidance through lecture videos, immediate feedback, and continuous support to navigate the course successfully.
MathWorks On Coursera
Yet another course on Coursera, MathWorks’ “Practical Data Science with MATLAB Specialization,” teaches skills in analyzing, visualizing, and modeling data using MATLAB, a powerful tool used in software engineering and data science. Despite the fact that MATLAB is not used as frequently as before, it remains a popular choice among researchers and academics for its specialized capabilities in things like numerical analysis or algorithm development.
Students will learn to analyze large datasets, calculate statistics, and create and evaluate machine learning models. The course assumes some background in basic statistics and spreadsheets, so it’s ideal for professionals with expertise in technical fields.
RATING
4.8/5 @ Coursera
FEATURES
- Can be completed in 2 months with a 4-hour weekly commitment
- Includes predictive analytics projects analyzing weather costs, predicting flight delays, and building machine-learning models
- Taught by experienced professionals from MathWorks
SUPPORT
The course provides free access to MATLAB for the duration of the specialization and allows students to use interactive learning tools and assignments to complete the course successfully. You can also earn a shareable certificate and have direct guidance from the instructors.
Kaggle – Get The Best Data Science, Machine Learning Profile
“Kaggle – Get The Best Data Science, Machine Learning Profile” is a course on Udemy that explores the Google-owned platform that is essentially a community for data scientists and machine learning practitioners.
You’ll learn everything about Kaggle’s ecosystem through a combination of on-demand video lectures, articles, and hands-on projects, and you’ll learn how to navigate competitions, datasets, and collaborative projects with ease.
RATING
4.6/5 @ Udemy
FEATURES
- Apply what you learn through practical exercises and real Kaggle competitions
- Covers topics such as Python, data science, and machine learning
- Learn at your own pace with 3 hours of on-demand video
- Engage with the community
- Earn a certificate of completion
SUPPORT
The course offers a lot of support, including a Q&A section.
University of Michigan On Coursera
If you want a certificate from a reputable university, the University of Michigan’s data science course on Coursera is the perfect choice. This course, taught by H.V. Jagadish, provides profound insights into the ethical and privacy implications of data science.
What makes this course special is its focus on the broader impacts of data science on society and the balance between technological advancement and ethical responsibility. It explores topics such as data ownership, privacy, informed consent, and fairness in the field.
RATING
4.7/5 @ Coursera
FEATURES
- No previous experience is necessary to take this course
- Approximately 14 hours to complete over 3 weeks at a self-paced rate
- Includes 10 modules and 9 quizzes for an engaging learning experience
- Addresses current ethical challenges in the aftermath of large-scale data breaches
SUPPORT
Throughout the course, you’ll have regular assessments to test and reinforce your understanding of the material. After you complete the course, you’ll be awarded a certificate from the University of Michigan.
John Hopkins On Coursera
This course is perfect for those wanting to start their career path in data science through a renowned institution. It’s a ten-course series taught by leading John Hopkins professors and covers the entire data science pipeline.
You’ll learn how to use R for data cleaning, analysis, and visualization and how to navigate the data science process from data acquisition to publication. The specialization also focuses on using GitHub to manage data science projects, perform regression analysis, and understand least squares and inference using regression models.
RATING
4.5/5 @ Coursera
FEATURES
- Teaches how to use GitHub for managing data science projects
- Designed to be completed in about 7 months at a pace of 10 hours per week
- Enhances essential skills in machine learning, R programming, and regression analysis
- Includes a capstone project that involves working with real-world data
SUPPORT
Similar to other popular online courses on Coursera, students will get a shareable certificate after completing the course and are able to get direct guidance from instructors.
What Makes A Good Free Data Science Course? Our Criteria Explained
At Springboard, along with our team of data science experts, we’ve established four critical criteria to evaluate these online data science courses effectively. Let’s look into what these are and why they’re important.
- Curriculum. A well-structured curriculum should cover a broad range of essential topics, from popular programming languages like Python or R to more advanced subjects such as machine learning and data visualization. It’s important to make sure the course offers a balanced mix of theory and practical application. Look for courses that include hands-on projects or real-world case studies, as these provide valuable experience in applying theoretical knowledge. Also, check if the course updates its content regularly.
- Instructor expertise. Are they experienced data scientists with a track record in the industry or academia? Do they have a knack for teaching complex concepts in an understandable way? Skimming through some of the course videos or reading about the instructors’ backgrounds can give you a sense of whether their teaching style aligns with your learning preferences.Â
- Reviews and ratings. While reviews and ratings are valuable, they should be taken with a grain of salt. A thorough course evaluation involves reading both positive and negative reviews to get a balanced understanding. What works for someone might not suit you and vice versa. Consider aspects like course difficulty, pacing, and the relevance of course material in the reviews.Â
- Skills taught. While hard skills like programming, statistical analysis, and data management are non-negotiable, soft skills such as problem-solving, critical thinking, and effective communication are equally important. Make sure the course offers a skill set that prepares you for all the challenges of a data analytics career.
Making The Most Out Of A Free Data Science Course
To truly benefit from these courses, check out what questions to ask before enrolling.
What can you expect to learn in a data science course?
In a data science course, you can expect to tackle a variety of specific technical topics, each having a key role in your understanding of the field. Some of the areas you might explore include:
- Programming languages such as Python and R, which are essential for data analysis and manipulation;
- Skills in tools like Matplotlib or Tableau to present data in a visually appealing way;
- Understanding statistical methods and probability to interpret data and make predictions;
- Basics of machine learning algorithm, including supervised and unsupervised learning techniques;
- Techniques for transforming and cleaning data to make it more suitable for analysis;
- Handling large datasets and performing complex queries using SQL.
How can you implement what you learn in a data science course?
The true value of a data science course lies in your ability and willingness to apply what you’ve learned in a real-world context.
After completing a free course, you can always advance your skills through more specialized paid courses or certifications. To make the most of them, you should contribute to open-source data science projects, participate in online competitions like Kaggle, or develop your own projects.
Free Data Science Courses FAQs
We answer your most frequently asked questions.
Can I Teach Myself Data Science?
Yes, you can. There are so many resources out there that it’s almost impossible to fail if you are consistent and desire to learn. The key is to take a structured approach—start with the fundamentals and progressively move on to more complex topics.
Are Free Data Science Courses Worth It?
They are definitely worth it, especially if you’re a beginner or want to test your skills. They provide a strong foundation without any investment from your side.
Do I Need Coding For Data Science?
Yes. Most data science roles require proficiency in programming languages. While it is possible to perform certain data analysis tasks using software with graphical interfaces, coding provides greater flexibility and control.
Can I Learn Data Science If I’m Bad At Math?
Yes, you can learn data science even if you’re bad at math. Many tools used today have abstracted these complexities, making it easier for those without a strong math background to perform data analysis. You should, however, have a basic knowledge of statistics and algebra, especially if you’re looking for senior-level roles.
How Can A Beginner Learn Data Science?
A beginner can learn data science from scratch by starting with the basics and gradually building up their knowledge and skills. Begin with introductory courses in programming (Python or R), statistics, and data analysis. Pick any of the entry-level courses above, and when you’re ready, move on to more advanced courses. Don’t forget to practice and participate in hackathons.
How Long Does It Take To Become A Data Scientist?
The time it takes to become a data scientist depends on both personal and professional circumstances, such as your starting point, learning pace, the depth of skills you wish to acquire, and the demand for data scientists at any given time. Generally speaking, it should take anywhere from 6 months to a year of consistent study and practice.
Since you’re here…
Curious about a career in data science? Experiment with our free data science learning path, or join our Data Science Bootcamp, where you’ll get your tuition back if you don’t land a job after graduating. We’re confident because our courses work – check out our student success stories to get inspired.
