8 Best Data Science Courses for Beginners [2023 Guide]
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The demand for data scientists continues to outpace the supply. A study by Quanthub found that, in 2020, there was a shortage of 250,000 data scientists and three times as many data science job postings as job searches. And the demand for data scientists is only continuing to grow. The U.S. Bureau of Labor Statistics projects that there will be a 36% increase in demand for data scientists in the next decade.
It’s safe to say that, if you pursue a career in data science, your skills will always be in demand. Data science salaries are likewise growing—it’s common for even entry-level positions to offer six figures.
If that all sounds appealing to you, then you may be wondering—how do I become a data scientist? You aren’t the only one wondering this. Because data science is such a new and emerging field, there isn’t a set path to becoming a data scientist, at least in the same way that there is for other high-paying professionals, like doctors and lawyers.
In an effort to educate the data scientists of tomorrow, a number of online data science courses have emerged. But with so many courses to choose from, and with so many different kinds of courses available, it can be difficult to know which one is right for you.
That’s why we’ve created this guide. Below, we’ll tell you all about the eight best data science courses for beginners, and what each offers so that you can decide which course is right for you. Ready? Then let’s get started.
What Is a Data Science Course?
A data science course teaches beginners the theoretical concepts of data science. You’ll also learn about the data science process, including math and statistical analysis, data cleaning and staging, data interpretation, data visualization, and techniques for communicating data insights in an enterprise setting. More specialized courses focus on advanced topics, such as building recommendation engines using neural networks.
8 Best Data Science Courses for Beginners
Below, we’ve selected a mix of beginner and advanced courses suited to learners of different abilities. Beginner-level data science courses are designed for those with a more limited technical background (some coding experience and familiarity with basic statistics) or those with no prior experience. Advanced courses help you brush up on skills like advanced machine learning, big data analytics, database management, and more. Choose the right course according to your skill level, career path, and budget.
Springboard’s Data Science Career Track is a 6-month, fully online, self-paced program that prepares graduates for entry-level roles in data science with 1:1 mentorship, career services, and a job guarantee. Students will build job-ready skills with 14 mini projects, multiple capstone projects focused on realistic data science scenarios, and an advanced specialization project that suits their career goals. Most students devote 15-20 hours a week to completing the course.
Springboard’s Data Science Career Track has been ranked “Best Data Science Bootcamp” by Course Report two years in a row. Springboard has a 4.63 star rating out of 5 on Course Report (based on 1,284 ratings), a 4.69 rating on Switchup (based on 1,341 ratings), and a 4.5 rating on Career Karma (based on 523 ratings).
Springboard’s 500+ hour curriculum features a combination of videos, articles, hands-on projects, and career-related coursework. Students will start by learning Python programming, and master techniques for data wrangling and data storytelling. You’ll also learn about inferential statistics, which helps data scientists identify trends in datasets. Students will also learn advanced machine learning techniques and how to use common data science tools, including Spark, MapReduce, NoSQL, and more. By working on real-world projects, students will also acquire soft skills such as identifying a client’s business problem, acquiring and exploring relevant data, using machine learning to make predictions, and creating real-world business impact through data storytelling.
This course is best for students with a technical or non-technical background who are looking to switch careers, gain hands-on experience working with real-world datasets, and build a job-ready data science portfolio. Students need some coding experience with a general-purpose programming language (eg: Python, R, Java, C++) and comfort with basic probability and descriptive statistics. If you don’t meet these requirements, you can enroll in the beginner-friendly Data Science Career Track Prep course, which teaches the fundamentals of data science, Python, and machine learning.
$9,900 if paid upfront; or $1,890 month to month (up to 6 months).
Offered by Udemy, this course features over 21 hours of material contained in a series of 219 lectures. Students who complete the course will earn a certificate of completion. The course offers a series of pathways that let students decide what concentrations to focus on based on their career goals.
This course is rated 4.6 stars out of 5 on Udemy.
This course is designed to teach students the most essential data science skills and prepare them to handle difficult datasets to mirror real-world conditions, including corrupted data, anomalies, and irregularities. Students will learn how to clean and prepare data for analysis, perform basic data visualizations, and model their data. Expect to learn how to:
- Perform data mining and basic visualizations in Tableau
- Clean data and look for anomalies
- Use training and test data to build robust models
- Install and navigate a SQL server
- Create a logistic regression
- Apply three levels of model maintenance to prevent model deterioration
Related Read: Top 13 Best Data Visualization Courses
This course is for anyone who wants to learn core theoretical concepts in data science without having to code.
$84.99 or $29.99 per month after a 7-day trial
Created by IBM, this course takes approximately 4 months to complete at a suggested pace of 5 hours per week. The course introduces learners to what data science is and what data scientists do.
This course is rated 4.7 stars on Coursera based on 11,244 ratings.
This course is designed to give beginner data scientists a foundation for more advanced learning. Students will learn fundamental data science concepts, how these techniques are applied across various industries, and how to kickstart their careers without prior knowledge of computer science or programming languages. The course teaches concepts like big data, statistical analysis, and relational databases, as well as industry-recognized tools such as Jupyter Notebooks, RStudio, GitHub, and SQL. Learners will also complete labs and projects to tackle data science problems using real-world data sets.
Get To Know Other Data Science Students
This course is best for beginners or aspiring data scientists who want to learn more about the data science field and what opportunities exist.
Coursera charges $39-$89 per month for access to Specializations.
Offered by Udemy, this self-paced course takes approximately six weeks to complete and is taught by Caroline Buckley, manager of intelligent customer interactions (ICI) at Ford Motor Company. The course is offered as part of Udacity’s Data Analyst Nanodegree.
This course is rated 3.7 stars out of 5 on ClassCentral.com based on 6 ratings.
The course teaches you the entire data analysis process, from posing a question and formulating a problem statement to exploring data and wrangling it into a usable format. Learners will receive a rudimentary introduction to statistical analysis tools. The final session is a hands-on project where students will use their skills on a recommended dataset. Students should be comfortable with programming in Python and familiar with Python concepts like classes, objects, and modules. Students who don’t have this prior knowledge can take Udacity’s Introduction to Python Programming.
This course is best for beginner data analysts who want to learn how to use data analysis tools. Learners should be comfortable programming in Python and familiar with Python concepts like classes, objects, and modules.
Created by the University of Michigan, this collection of five courses teaches learners how to use popular Python toolkits to implement statistical, machine learning, and text analysis techniques to generate insights from data. This course takes approximately 5 months to complete at a pace of 7 hours per week and is part of the Master of Applied Data Science degree available on Coursera.
This course is rated 4.5 stars out of 5 on Coursera based on 24,808 ratings.
By the end of the course, students will be able to conduct an inferential statistical analysis, enhance a data analysis with applied machine learning, and discern whether a data visualization is good or bad. Students will learn plotting, charting, data representation, and social network analysis. This course is intended for learners who have a basic Python programming background and want to apply statistically, machine learning, information visualization, and social network analysis techniques with popular Python toolkits such as Pandas, Matplotlib, scikit-learn, NLTK, and Network to gain insight into their data.
This course is designed for learners with some programming experience who want to level up their data science skills.
Coursera charges $39-$89 per month for access to Specializations, which are a series of related courses designed to help you master a specific topic.
Offered by Udemy, this self-paced course includes 8.5 hours of on-demand video, and teaches learners how to use Tableau to create a range of data visualizations, including bar charts, maps, scatterplots, and interactive dashboards. Students will also learn how to prepare, organize, and analyze data by creating data hierarchies, adding filters, and using advanced data preparation tools in Tableau.
This course is rated 4.6 out of 5 stars on Udemy based on 83,050 ratings.
Students will learn how to use Tableau to help with business decision-making, such as investigating customer purchase behavior, sales trends, or production bottlenecks. You’ll learn the entire process of converting raw data into compelling visualizations using Tableau. Each section provides a new dataset and exercises that will challenge students to apply the concepts they’ve just learned. The course also covers advanced features of data preparation in Tableau 10, including table calculations, treemap charts, and storylines.
This course is designed for working data analysts or data analytics students who want to up-level their skills in Tableau.
$199.99 or $29.999 per month after a 7-day trial.
Created by Harvard University, this course focuses on machine learning applications in data science and can be completed in 8 weeks at a pace of 2-4 hours per week. Students will learn about training data and how to use a data set to discover potentially predictive relationships. The course is taught by Rafael Izirrary, professor of biostatistics at Harvard University.
This course is rated 4.3 stars out of 5 on Class Central based on three reviews.
This course teaches the most popular machine learning methodologies for building predictive models. Students will learn machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will also learn how to tune machine learning models and avoid overtraining. This course is part of the HarvardX Professional Certificate Program in Data Science. Students are recommended to take the preceding courses in the series as prerequisites.
This course is for data scientists who want to deepen their understanding of machine learning.
This course can be audited for free. Learners can also upgrade for $99 to receive graded assignments and exams, a certificate of completion, and unlimited access to course materials.
Offered by DeepLearning.AI and Stanford University, this three-course program teaches fundamental AI and machine learning concepts for beginners. The program is led by AI visionary Andrew NG, co-founder of Coursera and founding lead of Google Brain, who has done pioneering work in building self-learning computers. This course can be completed in three months at a suggested pace of 9 hours per week.
This course is rated 4.9 stars out of 5 on Coursera based on 3,222 ratings.
This course provides a broad introduction to machine learning, including supervised learning and unsupervised learning. You’ll learn how to build ML models with NumPy and scikit-learn, build and train a neural network with TensorFlow to perform multi-class classification, and build recommender systems with a collaborative filtering approach. This beginner-level course requires students to have basic coding knowledge (for loops, functions, if/else statements), and high-school level math (arithmetic and algebra).
This course is best for learners with basic coding knowledge (for loops, functions, if/else statements) and high-school level math who want to learn the fundamental concepts of machine learning.
Coursera charges $39-$89 per month for access to Specializations.
How Do You Choose a Data Science Course?
Choosing the right data science course is a matter of personal fit. Before doing your research, think about what you’re interested in learning, what you want to achieve, and how much time and money you are able to invest in your education. There are many subtopics in data science and courses designed for learners of all abilities. Having a clear picture of your needs before doing the research will help you pinpoint the right course for you. Here are some considerations to make:
The type of data science course you choose depends on your career objectives and current skill level. If you have some experience with general-purpose programming, statistics, or data analysis, short courses are a great way to improve your skills in an area of weakness. Most courses require students to be familiar with coding in Python and have comfort with basic statistical techniques. On the other hand, students who are trying to switch careers, especially those with a non-technical background, will benefit from a more hands-on, long-term program like Springboard’s, which provides support services such as 1:1 mentorship, career coaching, access to the Springboard community, and the opportunity to create portfolio-ready projects to show prospective employers.
Course content and duration are other key considerations. Some courses are more advanced than others. Introductory data science courses will teach you the real-world applications of data science and key analytical techniques. These courses are a great way to gauge your interest in pursuing a data science career or lay the theoretical foundation for more advanced learning. Be honest with yourself about your current skill level.
Next, consider the quality of the course content. Does it cover the entire data science process, from data cleaning to analysis and visualization? Does it use popular open-source programming tools and libraries? Is there a good combination of theory and application? Also, consider how many hours of content the course offers.
If you have some programming knowledge, are currently learning data science, or already working as a data professional, choose a course that focuses on specific skills in data science, such as how to use Tableau for advanced data visualizations, how to use machine learning to build recommendation engines or artificial intelligence. Ideally, you want to choose a course focused on the programming language of your choice so you can implement your new knowledge immediately.
Determine your budget for pursuing further education. Does your employer provide a learning & development stipend or tuition reimbursement? Verify what types of programs or institutions are approved by your employer. If you’re paying for the course out of pocket, consider whether you can afford to pay upfront. Using a monthly payment plan may be more expensive in the long run, especially if unexpected circumstances keep you from completing the course on time.
Some MOOCs (Massive Open Online Courses) can be audited for free, but you may wish to earn a certificate to show prospective employers your skills.
Also, verify whether the course provides limited or lifetime access to learning materials. The downside of free courses is you generally lose access to course material within days or weeks of completing the program. Consider your needs and career objectives when determining which course will provide the best value for money. If you are attempting to switch careers, you may benefit from enrolling in a data science bootcamp rather than pursuing self-directed learning or taking individual courses in a piecemeal fashion. Bootcamps offer mentorship, career coaching, and a structured, comprehensive curriculum.
Always check the reviews before enrolling in a course. A high number of positive reviews indicates students can vouch for the success of a program. Pay close attention to student feedback and look out for red flags. Don’t rely solely on reviews posted on the course provider’s website. Look to third-party review sites such as Course Report, SwitchUp, Trustpilot, Career Karma, and more for unbiased reviews.
If possible, connect with students who have completed the program and ask them to share their thoughts on the program and what outcomes they achieved post-graduation. Also, check if the course offers statistics on student success rates (i.e. enrollment vs. completion rate, further education, and job placements, if applicable). Statistics that have been compiled and/or verified by a third party are generally more trustworthy.
Schedule and Flexible Learning Options
Most data science courses are designed with working professionals in mind. If you have a full-time job, you’ll want to select courses that can be completed in just a few hours a week. Also, consider if the course is self-paced or if you have to log on for live classes at a specific time. Most short courses are asynchronous and consist mostly of pre-recorded lectures. There’s less interactivity, but this is useful if you’re simply trying to brush up on a specific skill.
Data science is an ever-evolving field. Courses should be updated regularly (at least once a year) to reflect changing industry standards, new technologies, and programming frameworks. Any datasets used for assignments or referenced in course material should be current and up-to-date.
Quality of Instructors
Pay close attention to instructor credentials before enrolling in a course. See how many years of experience they have and determine whether their professional background directly relates to the course content. Course material developed by data science professionals is more likely to be aligned with industry standards.
Most courses have an introductory video you can watch before enrolling. Are the instructors engaging and personable? Do they explain complex topics in simple terms? Another factor to consider is how much interaction you’ll have with instructors versus time spent on self-study. Are there any live classes? Is there a discussion board where you can post questions? Are there graded assignments and, if so, are these assignments graded by the instructors, teaching assistants, or peers? Look for opportunities to have your work reviewed by a seasoned professional.
Prioritize courses that offer opportunities to complete hands-on projects that touch on the entire data science process and use real-world datasets. Are there assignments built into each module so you can apply your knowledge before moving on to the next topic? Assignments should test your comprehension of theoretical concepts and provide exercises to apply your new skills. Graded assignments are the best way to improve your skills because you can receive feedback on your work. A strong portfolio lets you demonstrate your value to an employer and also provides ample discussion material during the interview process.
Beware of courses that offer peer-graded assignments or overemphasize multiple-choice testing because you won’t receive the feedback you need to expand your skill set. On top of practical projects offered during the course, is there any career assistance, or internship/job placement opportunities?
A certificate of completion evinces your skills. While some MOOCs can be audited for free, they do not culminate in certification, so you have no way of proving to an employer that you completed the course.
Making the Most Out of a Data Science Course
Most students find it helpful to pursue additional learning above and beyond the course material. Being active in the data science community, working on personal projects, and practicing your skills on structured and unstructured datasets will help you build the technical and soft skills employers seek the most. Here are some other things to consider if you want to make the most out of your data science course:
What Should You Expect To Learn in a Data Science Course?
Data science courses should teach you the entire data science process, from finding and identifying viable datasets to data cleaning, building and tuning machine learning models, and data visualization. Remember that an estimated 80% of a data scientist’s time is spent cleaning data, so make sure you have a solid foundation in preparing data for staging. If you have no prior experience with popular programming languages, choose a course that provides you with an introduction to Python or R.
The course should also impart statistical techniques for analyzing data and extracting insights from large datasets. Math is foundational to working in data science—especially statistics and probability. You must learn basic mathematical concepts like variance, correlations, conditional probabilities, and Bayes’ theorem. You should also master popular analysis techniques such as cluster analysis, regression, time series analysis, and cohort analysis. The course should teach you how to use popular analytical tools like Hadoop, Hive, and Pig to extract valuable information from a dataset.
Another key aspect of data science is understanding relational databases so you can write query commands to retrieve and store data. Data scientists must become fluent in SQL, one of the most popular database query languages. You need to understand how relational databases work and learn the specific query commands to retrieve and store data. Data science courses should teach learners about industry-recognized data science tools, such as SAS, Apache Spark, D3.js, MATLAB, and more.
Related Read: 10 Best SQL Certifications To Grow Your Skillset
A high-quality data science course will also teach you how to use supervised and unsupervised machine learning algorithms such as linear regression, logistic regression, decision trees, and the Naive Bayes algorithm. You should have opportunities to work with both structured and unstructured data to reflect real-world conditions.
Finally, you need to learn how to think like a data scientist. Ultimately, data science is about storytelling. The crux of your work is to communicate your findings to non-technical audiences using language and data visualizations, make recommendations, and shape business decision-making based on your findings. One of the best ways to learn these soft skills is by receiving mentorship from an industry expert who can share tips on how they were able to succeed and help you understand the day-to-day realities of working as a data scientist. Do your research to find the perfect data science course that checks all of these key boxes.
What Will My Schedule Look Like?
Most online courses are self-paced, requiring anywhere from 2-10 hours of study per week. If you decide to enroll in a data science bootcamp instead of a short course, you might need to devote more study time. The average student completes Springboard’s Data Science Career Track in six months at a pace of 20 hours per week. Completion time depends on whether or not you are working full-time and have other commitments outside of your studies.
How Can You Implement What You Learn?
Keep your knowledge fresh by contributing to open-source projects on Github, creating personal projects for your portfolio, volunteering, or finding an internship. Continue practicing your skills using real-world datasets on Kaggle, Google Dataset Search, Data.Gov, Datahub.io, and more. For inspiration, read through documentation or community discussion forums from past data science competitions to find tips on how to work through a problem on your own. Once you feel ready, participate in hackathons on Kaggle, HackerEarth, Machine Hack, and other communities. This will teach you how to work with other engineers or data scientists and tackle real-world problems.
You can also get involved in a data science community to discuss the latest industry news, attend events, access content such as blogs, whitepapers, and webinars, and network with industry professionals. The IBM Data Science Community, Data Science Central, Open Data Science, and Kaggle are just a few examples of highly active data science communities.
FAQs About Data Science Courses
We’ve got the answers to your most frequently asked questions.
Do You Need a Degree for a Career in Data Science?
No. While having a relevant college degree in computer science, math, statistics, or an IT-related field can help, many organizations hire data scientists who don’t have a degree. Employers increasingly value hands-on experience more than college credentials.
Candidates who don’t have a degree can prove their mettle by pursuing industry-recognized certifications, completing a data science bootcamp or series of data science courses, and building a portfolio of real-world projects that show experience with the end-to-end data science process.
Are There Any Prerequisites To Take a Data Science Course?
Data science courses targeted toward beginners generally have no prerequisites. However, most advanced courses require learners to have some experience with an object-oriented programming language such as Python, R, or C++ and comfort with basic statistical models and mathematics.
Students with no experience in either can opt for Springboard’s Data Science Career Track Prep course before enrolling in the Data Science Career Track.
Is a Certificate Worth It for a Data Science Course?
Yes. A certificate provides tangible evidence of course completion and an easy way for employers to evaluate the baseline skills required for a data science role. Displaying an industry-recognized certificate on your LinkedIn profile and resume will make you more attractive to employers.
Is a Data Science Course the Same as a Bootcamp?
No. Data science courses tend to be short-term (ranging from several weeks to a few months), while a bootcamp takes about 3-9 months to complete. Courses tend to focus on a narrower curriculum and emphasize the theoretical concepts of data science rather than guiding students through the end-to-end data science process.
Unlike bootcamps, most courses offer limited interaction with instructors and do not offer support services such as 1:1 mentorship, career coaching, and job placement opportunities. Bootcamps are designed to help learners acquire job-ready skills required for an entry-level data science position while providing mentorship from industry experts.
Since you’re here…Are you a future data scientist? Investigate with our free step-by-step guide to getting started in the industry. When you’re ready to build a CV that will make hiring managers melt, join our 4-week Data Science Prep Course or our Data Science Bootcamp—you’ll get a job in data science or we’ll refund your tuition.