DataCamp vs Dataquest: Which is better for data science?
These platforms are great tools for any data scientist at the beginner or intermediate levels looking at upskilling. Both of them offer different learning pathways using Python, R, and SQL. They've also got an extensive range of courses, covering (but not limited to) machine learning, statistics, database engineering, data visualization, and web scraping.
What will I learn?
In case you're wondering if these platforms can teach you everything you need to be a data expert, the short answer is probably not. That said, they both offer you an excellent foundation for a lot of the skills you'll be using, meaning that these tools are a great way of supplementing content from other learning providers.
What about certificates?
If you think that working through everything on one of these sites is going to land you a job straight off the bat, then you're setting yourself up for disappointment. The pair offer certifications for working through their content, but the sad reality is that most employers don't put a lot of stock in certificates earned online. While unfortunate, this is understandable; most online learning providers haven't got industry accreditation and there usually isn't a verification process to make sure people are who they claim to be. Coursera and edX are making strides in verifiable data science certificates, though.
Despite their shortcomings, certifications are a great way of demonstrating that you're committed to your self-improvement. These platforms can also help give you the skills and inspiration you need to build a project portfolio; your portfolio is probably one of the first things a potential employer will look at (not your listed skills and education). So don't view platforms like these as unimportant for job hunting.
Dataquest is an online learning platform dedicated to teaching users data science. It contains various career and skill paths, allowing you to pick a learning plan tailored to your individual needs.
Looking at how content is structured, each exercise uses a body of text to explain what the section covers. It also gives a description of any accompanying datasets and the problem you'll be solving. Unlike DataCamp, there are no instructor videos included to explain concepts more in-depth. If you're someone that prefers a more independent approach to learning, skimming through content to focus on areas you need more help with, then this platform may be right for you.
A significant strength of Dataquest is that it doesn't spoon-feed you the solutions for more complicated problems. As a result, the lack of guidance requires you to use other tools such as Stack Overflow to work your way through exercises when you get stuck. While you could argue that this is a criticism of the platform, in reality, it is mimicking the process that developers go through in real life when working on complex problems. Most experienced programmers will tell you that they don't waste time trying to memorize complicated syntax and that the real skill lies in knowing how to quickly find the code that you need to plug into your scripts.
Projects are at the core of the Dataquest learning experience and are integrated directly into the exercises themselves; each course has a guided project at the end of it. This factor helps Dataquest stand out from its competition; lots of online platforms offer projects. With many providers, these can feel like an afterthought. There's nothing compelling users to take the time to work through projects in between courses to consolidate their learning. With Dataquest, users need to complete projects to mark courses as completed before moving on, forcing them to commit time to project work.
A common complaint of Dataquest is that its learning content for R and SQL could be better. The Python learning material is excellent, whereas the subject matter for R and SQL isn't to the same high standard. However, we could argue that prioritizing Python makes sense. It's the most useful language for a budding data scientist to learn because of its versatility. The 2020 Kaggle ML and Data Science Survey revealed that 78% of the data professionals surveyed said they use Python regularly, demonstrating the importance of Python for data science. However, when asked about SQL and R, the number of respondents using these languages was 38% and 21%. The chart below shows the full survey results for this question. So despite the importance of Python, these numbers show that any learning platform aiming to be a one-stop-shop for data science needs to have significant Python, SQL, and R offerings.
Pricing and Certification
The free offering is approximately 1/3 of the total website content, with the free plan offering you limited access to lessons, practice problems, and community features. Upgrading to a premium plan will give users full access to these areas while also unlocking the projects section of the site, giving you access to additional projects (on top of those included in the lessons).
Looking at pricing, Dataquest uses annual plans, coming in at $588. However, they regularly offer a fantastic 50% discount for the membership, bringing the price down to a reasonable $294.
Dataquest offers premium members a certificate for every course or path that they complete. Premium users also get access to one-on-one office hours with data scientists. The office hours could arguably be much more valuable than getting certificates, as you could use these sessions to gain insight into the data science industry, along with some pro tips to help with landing your dream job.
Similar to Dataquest, DataCamp is an online learning platform designed to teach data skills and it offers you a range of different career and skill paths. However, on DataCamp, these paths are called tracks.
DataCamp starts off courses with an instructional video explaining the concepts you'll use in the accompanying exercises. In addition, all the DataCamp instructors are either experts in their field or educators at the master's/Ph.D. level. With such an impressive pedigree of instructors on board, DataCamp could be an excellent option for you if you're more of a visual learner.
This platform is much more comprehensive in terms of its SQL and R offerings than Dataquest. DataCamp offers tailored tracks for both of these languages, along with a broad range of Python courses.
DataCamp also has a fun, gamified interface, offering experience points after completing exercises. Unfortunately, DataCamp doesn't use XP for much other than as an indicator for your total progress. You can also earn XP by doing daily practice challenges, which are great for days where you don't have time to commit to your learning. You can complete these quickly whenever you've got a spare 5 minutes. You can also complete challenges and courses via the DataCamp app on your phone (shown below), a feature that Dataquest is missing.
One thing that DataCamp is criticized heavily for is that the exercises only require you to fill in the blanks. This style is helpful because exposure to the pre-written scripts can be good for seeing the correct way to code. It also saves you time on coding stuff that is irrelevant to the lesson at hand. Unfortunately, this reduces the challenge of the exercises, which can also reduce content retention. Building solutions from scratch is how you'll do it in the real world, so missing out on this is a massive limitation of the platform. The recent addition of projects rectifies this issue to a degree, allowing you to work through real problems, either guided or unguided. Unfortunately, unlike Dataquest, completing projects isn't required for course completion.
Pricing and Certification
In terms of what's in the free package, you can access the first chapter of each course for free. Free membership also allows you limited access to practice challenges, limited project access, and unlimited access to practice assessments. A standard membership unlocks all of the courses and practice challenges, along with access to the data science certification assessment. For full project membership, users will need to purchase the premium membership.
The pricing is a little cheaper than what Dataquest is offering. With annual billing, you can purchase a standard membership for $300 or a premium membership for $400. Similar to Dataquest, they also regularly offer deals on paid memberships. For example, when writing this article, a 50% discount was available, bringing the total premium price down to a very appealing $200.
DataCamp offers members certificates of achievement for any courses they've completed. They can also earn the DataCamp data science certification by working through six timed assessments, a coding challenge, and a case study.
Maths and Statistics
For data scientists, a strong foundation in maths and statistics is essential. Statistics is the backbone of every single machine learning algorithm, so you need a solid understanding of statistics to know what's going on behind the scenes in a machine learning model. For beginners, both platforms cover the fundamentals of statistics and probability. They also cover averages, variability, conditional probability, and hypotheses testing. Dataquest has a slight edge regarding the core mathematics offered, with some linear algebra and calculus courses. However, DataCamp makes up for this with its statistics content, taking a more in-depth look at some of the methodology used in statistical inference. DataCamp generally takes a slower approach in this department, allowing you more time to absorb the knowledge.
The math for both is more than enough to get you to the intermediate stage. If you're looking to take your maths and statistics knowledge to the advanced level, Dataquest and DataCamp give a good foundation, but you'll need to look elsewhere to supplement their content.
Both platforms cover all that maths needed to get to the intermediate phase. Overall, DataCamp wins this one; some of the concepts are very complicated if you're not big on maths, so the slower approach can be a big boon for users that need more time.
Both platforms tick the boxes for k-nearest neighbors, k-means clustering, linear regression, logistic regression, decision trees, random forest and naive bayes. Coupled with the algorithms is a more in-depth description of the underlying statistics, which goes hand-in-hand with the standalone statistics content offered separately. The algorithms are covered well, providing more than enough understanding for you to start applying them to your models. DataCamp also includes support vector machine (SVM), whereas Dataquest is missing this.
They also both have courses for getting you started on Kaggle (pictured below), which is a fantastic learning resource for ML in its own right. Both have got an introduction to neural networks, although this content could be more comprehensive. There are courses out there entirely dedicated to deep learning as it's a vast subject, so to a degree, it's understandable that their deep learning offerings are slightly lackluster.
Dataquest and DataCamp cover the most common machine learning algorithms. The point for machine learning goes to Dataquest; they focus on one algorithm at a time. They're all followed up with a guided project to help consolidate your knowledge, so Dataquest swings this one.
If you're more interested in data engineering, the platforms cover the entire ETL process. For data structures, the offering is solid, with loads of content demonstrating the best practices for interacting with your data. The SQL offerings shine here, helping you build a bridge between SQL and your Python or R knowledge. Both have courses for parallel and cloud computing, along with an introductory Spark course. DataCamp also includes a course on data lakes and warehouses. Unfortunately, the pair of them lack in the web scraping department, this is usually a massive part of the ETL process and both platforms cram everything into a few short courses.
As mentioned previously, the SQL and R content is also much more substantial on DataCamp. SQL is essential for data engineers, so this one is a no-brainer.
The entire ETL process is there for both, but there isn't much on web scraping. SQL and R are much better on DataCamp. DataCamp is the winner in this department, with the data engineer career track being much more comprehensive than anything offered on Dataquest.
Like the maths/statistics offering, the data visualization content is more than enough to get you to the intermediate level. All of the basics are covered, giving you an idea of how to plot different graph types. Dataquest also has follow-up courses that teach you how to use data for storytelling, a valuable skill for anyone working in the data industry. Alternatively, DataCamp offers some more advanced visualization options, such as visualizing geographical data.
You could argue that omitting more complex graph types is to Dataquest's detriment. Still, the usual aim of data visualization is to communicate knowledge to a non-technical audience, so simpler is often better.
Dataquest focuses more on covering basic graphs, whereas DataCamp moves onto more complex graph types. However, for visualization, simpler is usually better, so Dataquest is the stronger of the two for this area.
As mentioned in the intro, both platforms offer certifications for working through their tracks and paths. But, unfortunately, on their own, these certifications aren't likely to land you a job in the industry. With that in mind, working through one of these platforms could be a massive part of your journey to that job offer you've been coveting. In addition, the certifications show that you're committed to your self-improvement. Who wouldn't want to work with someone like that?
Furthermore, the platform projects can also help inspire you to get started on your project portfolio. In the absence of experience, projects are the best way to demonstrate your competencies and they can also make a great discussion point in an interview. You could host your portfolio on GitHub, Kaggle, or even your website. By including portfolio links in your applications, employers can see firsthand what your capabilities are.
The community sections on both websites (shown below) can also be a big boost if used correctly. They can get you networking with other data professionals, who can give you more project inspiration, career advice and great tips for improving your code.
Dataquest and Datacamp are both great platforms for sharpening your data science skills. In addition, they both have pretty substantial free offerings, so if you're thinking about trying one out, why not work through some of the free content to see if the platform is right for you? Likewise, if you're on the fence between the two, then try completing a free course on each website and see which one feels like a better fit for you.
Neither of these will give you what you need to walk into your dream job or be an expert in the field, but they can help you build a stronger foundation if you're at the beginner or intermediate level. There's no substitute for experience, so start working on projects you can use to replace paid employment on your CV; this will help your employment search massively.
Despite their shortcomings, both platforms come very highly rated. Furthermore, their rates are very competitive compared with some of the other data science courses you can find online, so if you invest in a paid membership for one of them, then there's a good chance that you won't regret it!
Which is better?
In my opinion, Dataquest is an excellent platform for getting started with data science. I'm a big fan of text-based content; my preferred learning style is skimming through information and focusing on the key points, making sure I take in what I need. While laborsome, I'm pretty happy to fill in the gaps with some research in another tab for more complicated concepts. However, I feel like the interface could be a bit more exciting at times, as it isn't much different from that of some of Dataquest's free counterparts, such as HackerRank. The content offered by Dataquest is much better, but the interface doesn't reflect that.
I'm a much bigger fan of DataCamp's gamified interface, even if it feels like they're missing a trick with their XP system. I also really like the wide variety of tracks (63 total compared to 16 total for Dataquest), which tailors to the learning goals of users better. However, despite the positives of the platform, I'm struggling to overlook their fill-in-the-blank approach. Combined with the XP system, it allows you to quickly work through problems and have fun while you're doing it, meaning that you'll soon be soaring through your chosen track. However, the point of the platform is to learn and if you're quickly jumping from one exercise to the next without thinking through the challenges properly, then there's a good chance that a lot of what you're learning isn't going to stick.
If I had to pick one of these platforms, the text-based content on Dataquest swings it for me. The content isn't as fun to work through as it is on DataCamp, but my goal is to learn, so I need to prioritize whichever platform is better suited for that. Ultimately the decision is yours and you shouldn't take my word for it; I stand by my previous recommendation that you should give both platforms a go yourself before deciding which one you like best.