Dataquest

TL;DR: The Dataquest curriculum is well curated and laser focused for maximum value and efficiency. You progress at your own pace along a clear and logical path, without wasting time on mind-numbing instructional videos or searching around the internet to figure out what to study next. The entire learning experience is integrated in a single platform where you use interesting real-world data to immediately apply and practice each new skill at every single baby step along the way. Larger guided projects, interspersed frequently throughout the curriculum, allow you to synthesize concepts and solidify your knowledge while also building up a portfolio of projects to showcase your new skills.   The details:   As an environmental scientist, I've been using Python the past few years for data crunching and scientific computing. The data analysis parts of my job are by far my favourite, so now I'm wanting to transition into a career in data science. I was looking for an online course to help me level up my skills in the following ways:   a) work on projects with data from a wide variety of domains outside my little niche of academic research, b) add some new tips and tricks to my Python toolbox, and c) learn the essential concepts and skills in areas where I have minimal or no experience (such as databases, APIs, and machine learning).   Dataquest is the perfect learning experience to accomplish all of these objectives! I could have cobbled something together from a mishmash of free internet resources, as I did when I was initially teaching myself Python, but that was a very time-consuming and inefficient experience I did not want to repeat. When I discovered Dataquest and started trying it out, I could tell that it was exactly what I needed to make the most of my limited free time for studying. Below are some highlights of my experience and impressions so far.   The curriculum is well curated and laser focused for maximum value and efficiency. The Dataquest team has done an outstanding job of identifying the knowledge a data scientist needs (including crucial non-Python skills such as SQL, the command line, and Git), and putting together a thorough and focused curriculum. You learn the material you need to know--always through the lens of its application to data science--without getting sidetracked into rabbit holes and irrelevant tangents.   The entire learning experience is contained within one platform where you use interesting real-world data to immediately practice each new skill at every single baby step along the way. You don't have to constantly disrupt your flow and waste time bouncing back and forth between videos, reading materials, online interactive exercises, and coding on your own local computer - everything is all integrated into one seamless experience in the Dataquest UI.    And can I get an amen to ZERO instructional videos? For me personally, instructional videos are usually an epic waste of time. I find that the material is always presented either too slowly (and there's no way to save time by skimming it as you would when reading), or too quickly so that I'm having to constantly pause to scribble notes or look up things that weren't explained thoroughly enough. On top of all that, by the time a video ends, I've usual forgotten at least half of what I've "learned" because I wasn't practicing any of it along the way. The Dataquest approach is sooooooo much better! Read a bit, do a bit, read a bit, do a bit, and so on, so that you're constantly practicing and reinforcing the concepts. I think videos can be great for ideas, inspiration, and the occasional walk-through of some finicky process, but not as the primary means of teaching an extensive curriculum, so I was absolutely thrilled to discover an online course that does not rely on instructional videos.   You progress at your own pace along a clear and logical path. The curriculum is structured as an overarching path broken down into a logical progression of individual courses, each of which is broken down into smaller missions and projects comprised of a series of about 5-15 short steps. This structure makes it really easy for me to learn the material at my own pace, within the constraints of a busy and ever-changing schedule. Even if I only have a spare 20 mins for a study session, I can still complete a few bite-sized pieces and actually accomplish something worthwhile in that short time. When I get super busy and decide to take a week or two off of studying to focus on other priorities, the Dataquest curriculum structure makes it very easy to quickly jump back in after a hiatus.   You build a project portfolio as you're learning. Larger guided projects, interspersed frequently throughout the curriculum, accomplish a dual objective of allowing you to synthesize concepts and solidify your knowledge while also building up a portfolio of projects to showcase your new skills. For me, these projects are also a great way to accomplish my objective of working with data from a wide variety of domains outside my field, without having to waste time scouring the internet for random datasets to work with.   One minor comment on projects and portfolios - starting with the very earliest, beginner lessons in the Dataquest curriculum, they encourage you to showcase your projects in a public portfolio for prospective employers to see. While I love the idea of integrating a career-oriented portfolio into the learning experience, I might be a little more cautious about telling students to publicly display some of their very early work before they've learned some key skills such as vectorized operations instead of loops, Pandas instead of vanilla Python for tabular data, etc. Put all your projects on Github, for sure, but you might not want to publicly highlight and/or point people towards your very earliest projects, because you will soon be learning much better (and more industry standard) ways of implementing these analyses. As you progress further into the curriculum, you could always revisit an early project that you enjoyed, update the code with the new skills you've learned, and then highlight the project by pinning the repo on your Github profile. Otherwise, you could just let your early projects gradually descend into obscurity at the bottom of your Github repo list, as you keep adding new ones that better showcase your awesome new skills.   To sum up, my first four months of an annual Dataquest membership have been excellent. I've been learning a lot and enjoying all the fun data and neat projects. The curriculum is top notch and the learning experience is structured to help you progress efficiently and make the most of your time. I highly recommend this course to anyone wanting to learn data science!

I used Dataquest since 2019 and I double my income in 4 years and became a Data Scientist. That’s pretty cool! I’m sure other platforms could have also done the same thing but this is my experience. I don’t have any negative things to said and I’m pretty hard to satisfy so they are definitely doing the right things. I still have access to their platform since I purchased a lifetime membership years ago. I’ve finished the Data Science career pathway and now I’m running out of courses to take within DQ. I do wish they invest more in AI courses, e.g., large language models, deep learning, AI agent development, etc. Hopefully, someone will see this post and start creating more advanced course in AI. Long story short, great platform you won’t regret it!

I've been using Dataquest for a while now, and I think it's a fantastic resource for anyone aiming to break into data analysis or data science field. What really stands out to me is how easily it fit into my study schedule, everything I need is right there, making it easy to keep up with my learning goals without feeling overwhelmed. 

One of the best aspects of Dataquest is how interactive the courses are. Rather than just passively watching videos or reading, you're constantly engaged with exercises that reinforce what you've learned. 

The hands-on projects specially, are a game-changer. They’re challenging, it takes time and effort, but that’s exactly what makes them so valuable because they really push you to apply your knowledge in practical, real-world scenarios.

Although I haven’t had to reach out to customer support much, the few times I did, I was impressed by how quickly and kindly they responded. It’s very comforting just to know there's a supportive team ready to help if I ever need it.
Overall, the feeling I have is that Dataquest isn't just about acquiring knowledge, it's about knowledge and also truly understanding and applying it, which has made a significant difference in my learning experience, before Dataquest I didn't knew how to apply a lot of things and didn't have a portfolio, and this has changed a lot since I started to use the platform.
I highly recommend it to anyone serious about advancing their skills in data science.

I have been following online courses about Data Science and Machine Learning for a long time. I discovered the Dataquest proposal in its early days, yet in 2017. As a Professor at the UFRN (Natal)/Brazil, I would like to promote the teaching of Data Science courses locally in our university. Right away, the didactic presented by Dataquest was differential compared to other proposals from ed techs in the same field. Focusing on text-based materials rather than videos came close to what I was looking for. The idea and the strategy of problem-based learning were very inspired and motivated me to adapt it for our local. The results were spectacular and conquered and encouraged the diffusion of knowledge about data science throughout our university. 

When I started to learn Data Science more and more I realized how much breadth of knowledge is necessary.
I don't have a CS degree background and have been doing data analysis and BI development in the last 3 years but that gives me only SQL and a few proprietary BI language, however to be a good performing data scientist technically is required a lot of knowledge across various areas and I soon felt lots of gaps, hence the reason I embarked in the Data Science Python path.
By looking at the curriculum it was what I needed to fill those gaps like, Linear Algebra, Calculus, Command Line, Git besides the core Pandas, Matplotlib and Machine Learning.
Regarding the content I think I hesitated not having videos, because I am very visual learner, but in the end was the BEST think for me, because as you take the course you are forced to get your hands dirt and learn by doing through repetition.
The platform is awesome, the explanation, graphics and examples are very well done and start simple and progress to more challenging exercises pushing you to learn a lot.
I am impressed with me how much I progressed.
The community is very supportive and the forum and chat help a lot and also gives you opportunity to help others as you become more knowledgeable with experience.
I push to finish the total curriculum in 2 months and it was worth it. There is progress % and completion which keeps track of your hours and completion. I set a weekly target of 20h a week and it was the best thing I did.
I recommend buying the monthly subscription as it pushes you to finish early and move on to the next thing and to practice. I know that if I signed up for the year procrastination would kick in.
Point of improvement is only one. The IDE can be VERY VERY VERY Slow to the point of irritate you and your progress only gets recorded if you use their online IDE. The Jupyter Notebooks integrated in the platform works fine. As fast as local, but the "IDE" to complete the exercises suck big time.
Despite all that, I high recommend.

 

I am learning at dataquest, data science track. I have just finished basic python programming and some way to intermediate python programming and going forward to finish the data science track. The way dataquest is delivering the lessons are based on the principles of learning by doing, no videos or ppts. The learners are interacting with the content through coding.  So far, from what I have seen, the missions are based on real life problems. For these real-life problems and challenges they give us a guideline to go through and I believe this is the best way for adults like me to learn.

Des.

 

Being a Biomedical Scientist, I am currently looking for a career transition where I can be more involved in data driven projects with ML and other cutting edge technologies. My first concern is that I have no background in coding or any data analysis experience with any programming languages. After looking into several online courses about data science, I felt that Dataquest provided the most comprehensive curriculum. What I liked the most about it is that everything I need to know (from basic python, to pandas, ML, statistics, SQL etc.) is set into a logic order into learning paths. This makes the learning experience much more enjoyable as I don't have to worry about knowledge gaps or what skill to learn next.

Their interactive platform allowed me to learn by doing coding exercises in python myself, which is indeed much better (atleasy from my perspective) than watching Pros as they do all the coding. Basically their teaching method consists in explain a concept with diagrams and examples and then make you apply it on ongoing projects. This helped me develop agility writing code and familiarity with Python's backend logic. In addition, they provide challenges and projects where you can test your skills.

Possible improvements:

There are some features that they could add. First, I wish they did some summaries at the end of each module, as it helps students to review as they practise. This is something they seem to be currently working on, as the first couple modules do come with summaries. Hopefully they will have these recaps made for all modules very soon. In their blog, I have also found some cheat-sheets that contain the most used methods and formulas for packaged like numpy and pandas, which is very handy when doing projects.   

I also wish they had videos every now and then. Sometimes, reading all the explanations and diagrams can become a bit boring and tiresome, so in my opinion having some demonstration videos would make the platform so much better, specially videos on "do-along projects" where instructors do projects as they explain their thinking logic.

All in all, I recommend this platform to anyone who is looking to get started into data science or related fields. Their members are very helpful, honest and quick to reply enquiries.

I've been on my way to become a data scientist since for a couple of years now. I've been studying on many sites, took several courses on Coursera and even on Udacity. While overall quality of those online courses are pretty much satisfying, I never had a feeling that what I've learnt is enough, so I kept delaying applying to job interviews as data scientist.

What I find great about Dataquest is that this platform is extremely versatile, packed very densely with information and have great opportunities to practice skills that I feel is important to practice. The whole process is semi-self-paced (you are setting your own pace and try to keep up with it, or do not engage in such stuff at all), and I could skip all topics that I already knew (e.g. the basics of Python).

The most important part of studying here is so called "Guided Projects". In those projects I was able to explore various topics on pretty much real data in Jupiter Notebooks, the whole process of going through those projects is absolutely gorgeously structured. In every project I've done for now I felt like I was doing real data science.

For now I'm not finished yet, but judging by the content that I've completed already and the content ahead I think it would be my last data science course before I'll actually build up enough knowledge to go to a job interview as a real data scientist.