How to Learn Data Science in 2024 (AI Edition)
Generative AI and using it to modernize workflows will continue to dominate the conversation in 2024 as it did in 2023. So, the question becomes, what skills will employers want in the future, and does it make sense to learn data science and programming skills still? The answer is a resounding yes. All companies will become software companies as more workers rely on AI tools to improve their productivity and keep up with their competition. While Generative AI will make some aspects of programming and data analysis easier, complexity will grow, and thinking like a data scientist will be more important than ever. So, how do you learn data science? Let's discuss five reasons why most learning methods cause people to fail. Then, I will cover the correct learning method, which worked for me as I transitioned from a history teacher to a machine learning engineer.
Why do most data science learners fail?
High barriers to entry
Many would-be data science learners give up because they think learning data science is too expensive. Bootcamps and certification programs can come with huge price tags that scare people off. Many learners don’t realize there are more affordable ways to learn.
Affordable courses don't guarantee success. Many online courses contain dry video lectures or recordings of others writing code. This isn’t engaging, so learners end up tuning out.
As a result, these courses have completion rates of 5% to 15%. Not great odds.
Life gets in the way
Many data science courses have rigid schedules. Courses begin in a particular week, end in another, and may require live sessions at specific times.
People who are learning online often have jobs, families, or other obligations. That can make keeping up with those schedules a challenge.
The difficulty curve is off
If courses are too easy or too hard, most learners will eventually fall off the wagon. One common problem in online data science programs is that they’ve been built with pre-existing courses stuck together. One course might seem too easy, but the next is too hard. This happens because these courses weren’t built together.
Lessons don’t feel relevant
People tend to stick with their studies when they feel like they’re making progress toward their goals. But many data science courses are focused on lectures and drills, rather than doing real-world data science work. This can leave learners feeling like they are treading water.
The right way to learn data science
Before I founded Dataquest, I taught myself the skills required to go from working in a non-technical job to working as a machine learning engineer.
Through that experience — and through the experiences of the hundreds of thousands of learners who’ve gained data science skills on Dataquest over the last eight years — I’ve come to understand a lot about what works and what doesn’t when it comes to learning data science.
And it’s all built into the Dataquest experience.
Dataquest’s “learning loop”
For learners to be successful, we need to feel like we’re making progress. The importance of this can’t be understated. We need to feel like we can immediately use the skills we’re learning.
That’s why Dataquest is hands-on. You’ll be writing and running real code and working with real datasets from day one.
In our side-by-side learning platform, you’ll read about a concept on the left side of the screen, then be challenged to write and run real code to apply what you’ve learned on the right side.
This simple learning loop repeats through every single one of our courses. You learn something new and apply it to a real data science problem. Each screen builds on the previous screen and leads into the next one. That means that as you’re learning, you’ll know you have grasped the material because you’re using it to do real data science work.
You’re not watching lectures. You’re not filling in the blanks or answering multiple-choice questions. You’re writing and running code exactly like you will in a real data science job.
Tailored course paths
An important part of this approach is that our courses are carefully sequenced to ensure no gaps. One course always leads to the next one, and each has a very specific goal in mind.
For example, here are some of our career paths:
- Data Scientist
- 35 courses
- 26 projects
- No prerequisites
- Data Analyst
- 25 courses
- 20 projects
- No prerequisites
- Data Engineer
- 19 courses
- 16 projects
- No prerequisites
Many of our students love these paths because they contain everything they need to know to obtain a position. Yeah, really! To become a data analyst, every ounce of information you need is within the Data Analyst career path.
Plus, there are no prerequisites. Anybody can do it!
You can get started for free by clicking on any of the links above.
Fun, real-world projects
While all of our courses get you working hands-on with real data, we also know that it’s critical to synthesize the skills as you learn.
That’s why most of our courses end with guided projects that challenge you to answer real data science questions using the skills you’ve learned in previous courses.
These projects are fun learning tools that help cement your new knowledge, but they’ll also help you when it’s time for your job search, as you can include them in your project portfolio. (Hiring managers love it when you do this, by the way.)
Example projects in the data science path include:
- Profitable App Profiles for the App Store and Google Play Markets — Work as a pretend data analyst for a company that builds mobile apps. You’ll use Python to provide value through practical data analysis.
- Exploring Hacker News Posts — Work with a dataset of submissions to Hacker News, a popular technology site.
- Exploring eBay Car Sales Data — Use Python to work with a scraped dataset of used cars from eBay Kleinanzeigen, a classifieds section of the German eBay website.
- Finding Heavy Traffic Indicators on I-94 — Explore how using the pandas plotting functionality with the Jupyter Notebook interface allows us to explore data using visualizations quickly.
- Clean and Analyze Employee Exit Surveys — Work with exit surveys from employees of the Department of Education in Queensland, Australia. Play the data analyst role and pretend the stakeholders want answers to important data questions.
- Star Wars Survey — Work with Jupyter Notebook to analyze data about Star Wars movies.
Why it works: real success stories
This approach worked for me, and it’s working for Dataquest students too!
You don’t have to take my word for it, though. Here’s what some recent learners have to say about their Dataquest experience:
Dilara, Data Scientist at Perfist: “After I completed my Dataquest courses, I decided I should start looking for a job or an internship. I was hired as a data science trainee in **ten days**, and got promoted in four months.”
Rahila, Technical Data Scientist at Clinfocus: “Dataquest won my heart with the in-browser coding experience. I had encountered many platforms and tried the free coding experience with others, but Dataquest [...] was a better way to learn.”
Viktoria, Data Scientist at Fractal Analytics: “Things like statistics and programming are not easy to learn. But Dataquest explains them more clearly than all other resources [and is] understandable even for a person without specialized education.”
And you don’t have to take their word for it, either! Dataquest is free to start, so dive in, start learning,, and see how the Dataquest difference can help you.