The global economy is facing seismic shifts and traditionally steady jobs no longer offer the stability they once did. As many people now look to diversify their skills, data analytics is proving a compelling solution. This once-niche discipline is rapidly expanding into all areas of the modern economy—from IT to healthcare and finance, the sciences, construction, and more. Plus, beyond the core technical skills, data analytics roles increasingly require creative, analytical thinkers; from those with a meticulous eye for detail to others capable of seeing the bigger picture.
In short, data analytics is for everyone who wants to give it a try.
In this post, we explore 14 of the best data analytics training courses available right now. Whether you’re a career shifter or a recent graduate, we’ll look at everything you need to get the basics down. Times are tough and not everyone has money to spare, so we’ve included some options: data analytics training courses you can find for free online; paid online bootcamps, and, for the committed; a sample of full-time graduate programs.
- Beginner: Free data analytics training courses
- Intermediate: Paid data analytics training courses
- Expert: College-based data analytics training courses
Ready to find a data analytics training course that’s right for you? Then let’s dive in.
Want to get an idea of what data analytics entails before jumping into a training course? Check out this video with Will!
Beginner: Free data analytics training courses
If you’re new to data analytics and want to explore some of its themes and tools before forking out for a more comprehensive training program, here are five great free training courses you might want to consider:
Best for: Those wanting to get a quick taster of the world of data analytics
Cost: Free, but with the option to enroll into the Data Analytics Program afterward.
Completion time: 5 days, self-paced
Our free, five-tutorial data analytics short course is ideal if you want a digestible introduction to data analytics. When you sign up to the platform, you’ll get access to five hands-on lessons delivered by email—each focused on a separate step of the data analytics process. The course provides a broad view of data analytics, setting you up to explore the topic further if you choose.
This short course covers everything to get a broad overview of the field: from the different types of data analytics roles, to a summary of tools and skills you’ll need to develop if you pursue a career in the field. You’ll also have a few opportunities to get a hands-on experience with the basics of the data analytics process.
Best for: Those seeking an all-round, high-level taste of data analytics.
Cost: Free, with optional upgrades for certifications or add-on training.
Completion time: 5 weeks, 2-3 hours of study a week.
Led by expert professionals from IBM, this self-paced course covers all the need-to-know information about data analytics across 10-15 hours of video lectures. The introductory course offers a straightforward explanation of what data analytics involves, including the different steps in the process. Although very high-level, it covers both the practical elements of the topic (such as various types of data structure and file formats) as well as career-related content. For example, the course clearly outlines the difference between data engineers, data scientists, and business intelligence roles, while also exploring different career paths.
You won’t get any in-depth training here on the tools and software that data analysts typically use. However, you will enjoy a whistle-stop tour through the major big data platforms used to gather, mine, analyze and visualize data. Overall, a great all-around introduction to get any beginner started.
Best for: Those who want to learn the basics of SQL.
Cost: Free (but expect regular plugs for their paid courses!)
Completion time: About 30 hours of self-paced learning across 4 weeks.
Relational databases are one essential building block of data analytics, and mastering SQL is necessary for effectively managing them. This free course from Udacity covers all the basics of SQL, gradually building your knowledge across five standard and two advanced lessons. Starting with an introduction to SQL, you’ll learn the key commands for querying a database. You’ll proceed to work with multiple tables, picking up additional functions on the way via practical examples.
Finally, you can expect to move on to more advanced techniques, such as how to quickly query data across giant data lakes. We love the step-by-step approach of this course, which ensures that learners follow along with it, rather than being thrown right in at the deep end and being expected to swim.
Best for: Those who want an introduction to Python programming.
Completion time: About 20 hours of self-paced learning.
This beginner-friendly introduction to Python is suitable for all, the only prerequisite being the basic math skills that anyone should have from school. Split across five modules with a final exam, the course introduces the basic concept of Python and how it’s used, before progressing to its core data analytics functionality. Like the SQL course, it builds on knowledge as it goes. You’ll start with entry-level content, like how to define variables and set conditional statements, all carried out within Cognitive Class’ Jupyter Notebook sandpit (which is also beneficial since data analysts often use this software).
Once you’ve picked up the standalone functionality, the course shifts focus to one of Python’s many popular data analytics libraries, pandas, which is commonly used for data analysis, data cleaning, and machine learning tasks. While you’ll still have plenty to learn after completing the course, it offers a concrete foundation to build upon.
Best for: Those looking to unleash the statistical power of MS Excel.
Cost: Free, so long as you complete it within the 7-day trial period.
Completion time: Approximately 12 hours of learning.
Another course from IBM via Coursera, this data analytics training course focuses on everyone’s favorite spreadsheet software, MS Excel. Spread across 9 modules, the first three focus specifically on MS Excel, starting with a beginner’s introduction to spreadsheets. Next, it progresses to topics such as how to insert, filter, and sort data.
Finally, more complex themes emerge, including an exploration of Excel’s most valuable data analytics functions, (like how to create visualizations and dashboards). Modules 5 to 8 focus on the R programming language and aren’t exclusively Excel-focused. However, R can be used with Excel, and is a useful tool in its own right, so you may decide to continue on.
While you’ll need to complete the R modules to gain a certification, you can easily skip them if you’re using Coursera’s 7-day free trial. The whole course is capped off with a quiz-based assessment. But again, you can skip this if you want to access the rest of the course content for free.
Best for: Those who want to dive deep into machine learning.
Cost: Free if you complete it within Coursera’s 7-day trial period.
Completion time: Approximately 61 hours of learning.
Machine learning isn’t a topic beginner data analysts need to master but it’s so fascinating we couldn’t resist including one course on our list! This particular one is also delivered by Andrew Ng who is kind of a big deal in data science circles. Although not technically free—and at 61 hours of learning, a squeeze to complete within a 7-day trial—we reckon it’s worth completing at least the introductory module. This is only 42 minutes of learning and covers the basics of machine learning theory, such as the difference between supervised and unsupervised learning.
If you progress, however, you’ll dive into the technical stuff, such as multivariate linear regression, logistic regression, and how to train neural networks. While this stuff is not for beginners, it’s a great option if you’re a math nut who finds the potential of artificial intelligence intriguing.
2. Intermediate: Paid data analytics bootcamps
Done dabbling and want to invest in the no-nonsense skills you’ll need for an entry-level data analytics role? Online data analytics bootcamps are becoming increasingly popular for this. While these types of data analytics training courses don’t always come cheap, they’re much more affordable than full degrees and are usually flexible enough to fit around your schedule. Some even come with job guarantees, making them a much safer investment. Here are five top courses to consider:
Best for: Beginners looking for comprehensive training, mentoring, and career support.
Cost: $6,900 USD (via payment plan, or with a discount if you pay upfront).
Completion time: 5 months studying full-time (30-40 hours a week) or up to 8 months studying part-time (15-20 hours a week).
Do you know that data analytics is the path you want to take? Do you also want the guarantee of a job at the end of your course? Then look no further than CareerFoundry’s Data Analytics Program. Regardless of background, the course is designed to take learners from beginner to job-ready in 8 months. While longer than some data analytics training courses, it’s fully comprehensive, covering the tools, skills, and processes you’ll need in detail.
To effectively prepare you, CareerFoundry’s Data Analytics Program uses a project-based curriculum to get you working hands-on from the very start in a professional environment. It offers unrivaled mentoring from active industry professionals and the Career Services team offers job coaching.
Once you’ve completed the course, you’ll not only be skilled-up—you’ll have a portfolio of projects, a polished resume, and be ready for interviews. And if you don’t have a job within six months of completing the course? You get your money back, making CareerFoundry about as safe an investment as you can get.
Best for: Non-data analytics professionals looking to supplement their skills.
Cost: $3,950 USD (with employer sponsorship options).
Completion time: 10 weeks part-time or one week intensive.
As data analytics starts to permeate all sectors, you might decide to supplement your present role (e.g. in marketing or finance) rather than changing careers completely. In this case, General Assembly’s data bootcamp is aimed at busy professionals looking to upskill in their current roles. This flexible, self-paced course covers all the essential functions of Excel and SQL, along with the need-to-know aspects of data visualization and software tools like Tableau.
It includes 40 hours of learning, project coursework, and a final assignment with individualized feedback and guidance from an expert instructor. You’ll get a completion certificate, too, ideal for showing off your newfound skills to your employers or on LinkedIn. While the course costs just under $4,000 USD, General Assembly welcomes employer sponsorship, so it’s definitely an option worth considering.
Best for: Those who want a good selection of specific (and affordable) modules.
Cost: $25 USD per month.
Completion time: From 12-25 hours of learning per course.
Unlike many course providers, Datacamp specializes specifically in data science. The best part about their courses is the number available. Rather than providing a single generic course, you can sort Datacamp’s offering by the technology you want to focus on. From spreadsheets in Excel to data analysis using Python and R, specialized industry software such as Tableau for data viz, or Microsoft Power BI (an industry staple for many big companies), the choice is yours.
For $25 USD per month, Datacamp’s Career Tracks are also very affordable, and their bitesize courses cover everything from collecting and cleaning data, to theories like statistical inference, and even highly-specific training such as how to analyze genomic data. You’ll learn by doing, working on real projects, and collecting rewards as you go. And if you’re looking to shift into a specific role, Datacamp’s dedicated career service can help you select the best set of courses to get you on the right track.
Best for: Career changers with existing experience.
Cost: $10,140 USD or $8,500 USD if you pay upfront.
Completion time: 420 hours across 8 months(15-20 hours per week).
Already got some experience using office, design, or programming tools? Then Springboard’s Data Analytics Bootcamp offers the additional technical skills you need to take things up a notch. Going beyond mere data analytics, the course also focuses on areas where employers find the most significant competency gaps—things like critical thinking, problem-solving, and communication.
The Springboard bootcamp aims to develop these so-called ‘soft skills’ while helping you devise a job search strategy. They even provide career support for six months after completing the program. This aftercare is particularly practical as it includes things like interview tips and negotiating salaries.
Unlike other courses, you will require a little experience to enroll, and it’s also one of the pricier options on our list. However, like CareerFoundry, Springboard offers a job guarantee, with a full refund if you still don’t have a job six months after graduating.
Best for: Those looking for flexible and affordable career-focused modules.
Cost: $24.50 per month for an annual subscription.
Completion time: Depends on the modules you choose.
Another flexible and affordable option is Dataquest’s Data Science Learning Paths. Reflecting the increasingly broad career paths available in data analytics, Dataquest offers 70+ courses on topics ranging from Python to R, SQL, Excel, and much more. These modular courses can be compiled in any way you wish, but Dataquest also provides 5 pre-structured career paths to help prepare you for specific roles in areas like business analytics or data engineering. They offer an additional 12 paths for specialized skills development, too, in areas like machine learning, data pipelines, or statistics and probability. Check out their full list of 70+ courses here.
At the lower end of the price range, Dataquest’s offering doesn’t come with mentoring. But for those who are happy studying under their own steam, it’s a great way to build skills. They also regularly add new courses, keeping things industry-relevant.
3. Expert: College-based data analytics programs
If you already work in the field you might be looking to jump the tracks from data analytics into more senior data science roles. If so, a master’s degree is usually a minimum prerequisite. You’ll find countless options available, varying widely depending on your location and particular area of interest. These aren’t for beginners but to give you a flavor, here are three possible options from North America. Check local institutions first, though.
If you’ve got business specifically in mind, MIT’s MBAn program is a 12-month master’s course delivered by some of the leading minds in data science. It will fully prepare you with the skills necessary to drive smarter business decisions and solve some of the biggest problems that companies face.
Aimed at engineers, mathematicians, physicists, computer programmers, and other high-tech professionals, the program’s core focus is on machine learning; how this emerging technology is currently being used, and what its future applications are likely to be. This includes all the practical aspects and theory you’ll need to become an expert on the topic. As a prerequisite, you’ll either need an undergraduate degree in a related subject or significant hands-on experience in the field.
If you prefer not to hone in purely on data science for business, the University of Toronto’s MSc in Applied Computing is a good example of a course that diversifies a data analyst’s skills for all industries. Concentrating on computer and data science, applied math, and quantum computing, this is one of many such degrees aimed at preparing tomorrow’s data scientists for the high-tech economy. Four semesters over 16 months, this particular course includes an applied-research internship with one of the university’s employer partners (something to consider looking out for if you want hands-on, practical experience).
Once again, all applicants need to have completed (or be in the process of completing) a relevant undergraduate degree with certain grade expectations, which is also a common requirement for this type of course. Although this is just one example, you’ll find many similar MScs available in locations all over the globe.
All master’s degrees in data-related fields require some expertise in math. However, Stanford’s Master’s in Statistics focuses specifically on mathematical topics and how these can be applied to an individual learner’s specific area of interest. On the Stanford course, core modules include probability theory, stochastic processes, and applied statistics. Once complete, students will then apply these to their elective area of interest via additional modules of their choosing.
Elective topics are diverse, ranging from the biological sciences to computation, mathematical engineering, computer science, economics, operational management, and much more. Stanford’s course is about 15-18 months, but students have three years to complete it, making it a very flexible and career-focused option.
While these are just three of many master’s degrees out there, they should offer a taste of just how niche you can go with your data career over time.
From broad-stroke tutorials for beginners to career-focused bootcamps and niche university specializations, there are many courses available for would-be data analysts to choose from. The options might seem staggering at first but don’t be put off. One of the best things about data analytics is that you can carve your career in any way that appeals. There’s no single prescribed path that you have to take. We hope this list of data analytics training courses gave you a good idea of the scope that’s available to you, no matter what stage of your career you’re currently at.
To learn more about a potential future career in data analytics, check out this free, 5-day Intro to Data Analytics short course or read the following introductory guides for more: