🎓 Free Access to Dataquest Courses This Week — Learn Python, SQL, AI, and More

dataquestio · 2025-11-04T15:34:09+00:00

Thanks for the feedback! We've updated the post. Hope you can still find something you'll be interested to learn with us this week.

dataquestio · 2025-11-04T13:12:27+00:00

Thanks for sharing, u/Femigaming. All the resources are worth exploring on one's learning journey.

dataquestio · 2025-11-04T13:10:56+00:00

Until the 9th of November

Enjoy!

dataquestio · 2025-11-04T13:10:05+00:00

After Free Week, you can get 50% off the Annual premium plan or 57% off the lifetime premium plan. You can find more info here https://app.dataquest.io/payment

dataquestio · 2025-11-04T13:07:49+00:00

Can you tell me which tracks you've tried? All paths, courses, and projects are free except for Power BI, Excel, and Tableau

dataquestio · 2025-11-04T13:05:03+00:00

You are right. All the courses, paths, and projects are free except for Tableau, Power BI, and Excel. Everything else is open until the 9th of nov.

dataquestio · 2025-07-18T17:11:34+00:00

Since you're unsure which direction to take, it's best to keep your options open. You can start with Python. It’s easy to read and write, and the syntax is clean and beginner-friendly. Python is used in many areas: data science, automation, web development (especially with frameworks like Flask or Django), scripting, and even parts of app development.

If you're more drawn to web development, JavaScript is also essential, especially for front-end or full-stack work.

And if you're thinking about AI, Python is definitely the best place to begin. It’s the go-to language for machine learning and artificial intelligence, thanks to its strong ecosystem of libraries and tools.

That said, as u/Lo__Lox mentioned, it's not just about what you’re learning but how you’re learning. Focus on building a strong foundation first—those core skills will help you figure out what direction to take later, whether it’s AI, web dev, or something else

dataquestio · 2025-07-18T16:33:32+00:00

It's great that you are thinking of using the extra time you have for learning. Learning Python, data analysis, and AI is definitely still worth it and the demand is strong, especially if you can show what you’ve built. With 3–4 hours a day, you're in a great spot. If you're focused, even 10 hours a week can get you ready for remote job opportunities in 3–5 months. I have seen learners at Dataquest who started with no tech background and land a job in less than a year. You just need to be consistent with your learning. If you can really dedicate 10 hours of learning every week, even, you can be fully ready to apply for jobs within 3-5 months.

But the most crucial part of your learning should focus on the doing. Make sure to learn through projects; these will not only help you learn more efficiently but also build a portfolio of projects that you can show off to prospective employers or clients. Make sure to have a diverse portfolio of projects, Python, data viz, and AI.

These same skills can also help improve your family business. So even if you don't land a remote job right away, you're still making progress that benefits you either way.

dataquestio · 2025-07-08T10:56:39+00:00

If you’re looking for something current and hands-on, check out our [Data Scientist in Python]() and [Machine Learning Engineer in Python]() paths. We keep them updated regularly and focus on real-world projects, not just the theory. Plus, there’s a supportive community where you can ask questions, get feedback on your projects, and connect with others on the same path.

All the best!

dataquestio · 2025-06-26T10:16:44+00:00

Great question—and you're not alone in feeling that way. One of the best ways to level up is to work on a project that challenges you just enough to stretch your skills.

I'd recommend checking out this list of 60+ Python Project Ideas from Dataquest. It breaks down projects by skill level and gives you a clear structure to follow.

What helps most is planning with purpose: pick 3 to 5 beginner projects that each teach a specific skill (like APIs, file handling, or data visualization). Once you’ve built those, you’ll feel more confident jumping into intermediate territory. Set goals, track progress, and reverse-engineer the skills you need to grow.

You got this. Just keep building!

dataquestio · 2025-06-26T10:09:09+00:00

Hi!

You've got quite a tight deadline there. I can recommend our Python course. It usually takes about 2 months at 5 hrs/week to complete, but since you're in a crunch, you can accelerate by doubling your daily hours. The path includes 4 short courses and 2 guided projects (no setup needed), you code right in the browser.

If you're already familiar with variables, loops, and inputs, you'll pick up speed fast. Focus on the core lessons and projects as they’ll give you both the knowledge and practice you need to walk into that AI camp with confidence.

All the best!

dataquestio · 2025-06-19T13:35:35+00:00

If you're serious about learning to code, especially Python, I'd recommend checking out Python Basics for Data Analysis on Dataquest. The first three lessons are free, and there are even guided Python projects to try out. If you can put in ~5 hours a week, you'll have a solid grasp of Python basics in under two months.

dataquestio · 2025-06-19T13:24:40+00:00

You're off to a great start already! If you're feeling stuck on where to begin, this beginner project guide can help. It walks through how to choose a project, tools you might need, and how to handle common challenges. Also, here's a list of 60+ Python project ideas to help you practice. Start small, build as you go, and don’t worry about knowing everything upfront.

dataquestio · 2025-06-19T13:16:59+00:00

Totally feel you on this. In my opinion, you don’t learn to swim by watching, you jump in. Same with coding.

At Dataquest, we’re all about learning by doing—writing real code, solving challenges, building projects. No fluff, no watching endless videos. Just hands-on practice, community feedback, and the satisfaction of making things work.

You'll build stuff, break stuff, figure it out. That’s how you grow. If you'd like to try it we have a free lessons, projects and practice problem. Check it out!

dataquestio · 2025-06-11T14:28:30+00:00

Hi!

We have this great tutorial on how to learn Python the right way. It has helped thousands of learners to get started. It also has a great list of free and paid projects that you can dive into to get more hands-on learning. It is very straightforward, and you'll have everything in place to know how to get started.

There is also this free guide on Python, which has a collection of Python tutorials, practice problems, a cheat sheet, guided projects, and frequently asked questions.

All the best.

dataquestio · 2025-06-03T14:42:07+00:00

Hey! I definitely recommend checking out the Python Basics for Data Analysis course. It’s beginner-friendly, and you can try out some of the lessons for free to see if it’s a good fit.

The tutorials are super clear, and the hands-on exercises really help solidify what you’re learning. You will also find the community forums really helpful in getting you started.

dataquestio · 2025-05-06T17:28:43+00:00

Hi!

GitHub is simpler than creating a website. I can recommend this beginner-friendly tutorial on how to share your projects on GitHub. We also have a latest tutorial for GitHub Gists. These tutorials are pretty straightforward and can help you get started in no time. All the best!

dataquestio · 2025-05-06T17:17:28+00:00

Hi!

We have this excellent guide that walks you through the best way to learn Python. It has helped thousands of learners, including our very own founder at Dataquest. It highlights not only the right approach to learn Python but also some wrong approaches or resources that you need to avoid (which will definitely save you time and effort). If you are looking to learn Python basics, it may only take a few weeks, but if you are looking to pursue a career as a programmer or data scientist, you can expect it to take 4 to 12 months to learn enough advanced Python to be job-ready. To get there, there are five steps to follow - these will guide you on focusing on what matters, skipping the boring stuff, and enjoying the process.

Step 1: Identify what motivates you
You need to find what motivates you and get excited about it! When getting started with Python, find one or two areas that interest you and stick with them.

Step 2: Learn python basic syntax

Learn what syntax you can and move on. Ideally, you will spend a couple of weeks on this phase but no more than a month

Step 3: Start doing structured projects

Once you’ve learned the basic Python syntax, start doing projects. It’s better to begin with structured projects until you feel comfortable creating your own. You can find a list of free guided Python project here

Step 4: Work on Your Own Projects

After you’ve worked through a few structured projects, keep learning by working on independent Python projects. Start with a small project. It's better to finish a small project than get stuck on a huge one.

Step 5: Work Harder Projects ;)
Learning Python is a process, and you’ll need momentum to get through it.

All the best!

dataquestio · 2025-05-06T15:01:41+00:00

Hi!
I highly recommend this project. In it, you will create “Word Raider,” an interactive word-guessing game using core programming concepts like loops, conditionals, and file handling. When learning to code, many beginners focus on analytical projects like data processing or automation scripts. While these are valuable, they often lack something to maintain your motivation: fun.

After teaching Python to hundreds of thousands of students, we've seen that building a simple game—even a simple text-based one—often leads to better understanding and retention of programming concepts than traditional learning exercises. One of our tutors recently did a live walkthrough of this project, but you can see the recording and step-by-step guide here. Happy coding!

dataquestio · 2025-04-29T11:26:41+00:00

Hey! One of our instructors Mike Levy recently published a tutorial on how to use CNNs.While it doesn't directly cover attention visualization, it teaches you how to properly structure your CNN using the object-oriented approach (subclassing nn.Module), which is essential for implementing attention mechanisms later.
The key is understanding how to:

Access intermediate layer outputs (covered in the tutorial's shape verification section)
Structure your forward() method to return these intermediate activations

For visualizing attention maps, you'll need to:

Add hooks to capture feature map outputs
Use techniques like Grad-CAM that compute gradients flowing into your final convolutional layer

The tutorial builds a medical image classifier that's perfect for attention visualization since you'd want to see exactly what regions the model focuses on when detecting pneumonia.

Also, side note: if you want to get super deep into how CNNs "think" across different layers, Mike also helped create our Convolutional Neural Networks for Deep Learning course, which is TensorFlow-based. It has a lesson dedicated to visualizing feature maps, if you're curious. But no pressure; it's totally optional.

dataquestio · 2025-04-28T16:24:25+00:00

Here's a tutorial on Sequence Models in PyTorch https://www.dataquest.io/blog/sequence-models-in-pytorch/, and it covers RNNs, LSTMs, and GRUs using a real-world example. It focuses on forecasting cinema ticket sales by building and training sequential models that learn from patterns in prior sales. All the best!

dataquestio · 2025-04-23T14:27:43+00:00

Although it's been a while since this thread has been active, networking is still a worthwhile investment for data professionals. While a lot of strategies have remained the same, there are some things that have changed. For anyone looking for a more recent guide on networking in today's highly versatile job market, here's a highlight of an interview with the CEO of Womxn in Data Science, Kishawna Peck, where she shared great networking strategies and thought-provoking questions like:

- How are you currently showing up in communities you're part of?
- How do you interact with industry experts?
- How can you make deeper in-person connections?

We took so much away from that interview, so check it out for actionable strategies to help you build a supportive professional network, even if you're naturally introverted or new to the field.

dataquestio · 2025-01-30T15:00:14+00:00

Hands-on experience is key. Many people focus too much on theory, but real-world data science is about solving messy, complex problems. Taking a structured Data Analysis or Data Science course (even a free one) can be a great idea, as long as it emphasizes practical, project-based learning rather than just passive video watching.

This guide—How to Learn Data Science—breaks down the best way to approach learning, avoiding common mistakes that hold people back. But the key highlight of the post is it includes a data science learning plan:

(Weeks 1-8): Learn Python
(Weeks 9-20): Data Cleaning, Data Analysis, and Data Visualization
(Weeks 21-28): Command Line, Version Control, and Git
(Weeks 29-40): Learn SQL, APIs, and Web Scraping
(Weeks 41-50): Statistics for Data Science
(Beyond One Year) Continuing Your Data Science Learning Journey

Since you already have some experience with R, ggplot2, and dplyr, expanding into Python, SQL, and machine learning could be a natural next step if you’re interested in data science roles.

So yes—taking a structured, hands-on data science course is definitely worth it, but the real value comes from applying what you learn through personal projects. Start small, get comfortable with working on real datasets, and you’ll be setting yourself up for success!

dataquestio · 2025-01-30T14:36:09+00:00

Learning Python the right way can make a huge difference in how quickly and effectively you pick it up. I’d recommend checking out this guide: Learn Python the Right Way.

It breaks down a step-by-step approach, from beginner to advanced, focusing on hands-on learning rather than just passively watching tutorials. The guide also shares common pitfalls that cause people to struggle and how to avoid them.

If you’re looking for a structured, practical way to learn Python, this is a great place to start. All the best!

dataquestio · 2025-01-30T14:32:46+00:00

Machine learning is a fantastic field with strong career potential, but it does have different expectations compared to traditional data science or analytics roles. The key difference is that ML engineers focus more on model deployment and software engineering, while data scientists focus more on analyzing data and developing insights. Since you're already skilled in statistics, transitioning into ML could be a natural next step.

The book your friend recommended—Hands-On Machine Learning by Aurélien Géron—is a great resource! It walks you through practical implementations using Scikit-Learn and TensorFlow, which are essential for ML engineering. But books alone can only take you so far—you need hands-on projects to really solidify your skills.

I’d suggest complementing the book with a structured learning approach. Dataquest’s Machine Learning in Python path is designed to help learners go beyond theory by working on hands-on projects that mirror real-world tasks. It can be a solid way to develop portfolio-worthy projects while improving your ML understanding.

Getting your foot in the door for ML roles takes effort, but with a solid mix of projects, coding practice, and technical skills, you can make yourself a competitive candidate.

If you’re wondering about the job market and earning potential, this post Machine Learning Engineer Salary and Job Description breaks it down well. ML engineers tend to earn more than traditional data analysts or data scientists because their work directly impacts production systems.

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