The internet is overflowing with free and affordable data education resources. But it’s nearly impossible for a beginner to navigate the options, let alone commit to months of self-driven learning. Most people end up spending tens (or hundreds) of thousands of dollars on a degree or bootcamp instead.
I propose a solution to this problem using a curated curriculum and a paid community:
- Have someone passionate about data and education technology (me) find the best resources and combine them into a coherent curriculum.
- Build a community of learners eager to break into data science and help them flourish together.
This is Not a Real Data Science Degree, the best bang-for-your-buck method for learning data skills in the digital era of education. This post provides a general overview of the curriculum and the community.
Note: Not a Real Degree is learner-supported. Some of the resources I recommend may be affiliate links, meaning I receive a commission (at no extra cost to you) if you use that link to make a purchase.
The TL;DR version:
- Motivation: To create a curriculum made up of the best online courses, books, etc. and build a community to compete with universities and bootcamps. I did this in 2015, and am updating my picks after five years of experience in the EdTech industry.
- Just show me your picks: Sure! Curriculum. Curriculum explainer.
- Prerequisites: Basic arithmetic and high school algebra.
- Target role: The analyst-machine learning expert hybrid.
- Languages: 85% Python, 10% SQL, 5% R.
- Two terms: Term 1 covers basic data analysis. Term 2 covers machine learning and advanced data analysis topics.
- Time commitment: Each term is roughly 65 days, where one day contains 4–5 hours of focused learning. You set your schedule and location.
- Price: Varies since some resources require a subscription. As little as $249 for those who complete the curriculum in six months, and $375 for a year.
- Community: I’m creating a community so you don’t have to learn alone. Parts of the community will be paid ($8/month or $67/year) so members are invested and engaged. Join the waitlist!
- Why I’m doing this: To help democratize education. I chose an affiliate revenue model blended with a paid community to make maintaining the curriculum and building the community my full-time job.
- Next steps: scroll to the very bottom!
My name is David, and I created this curriculum for my former self.
Back in 2015, I dropped out of school to create my own data science curriculum. I bet on two things:
- The courses on global EdTech platforms could be cobbled together to create a program better than the one at my local university.
- Blogging about what I learned via the projects I created could effectively replace my university’s “trusted” credential and a GPA.
I Dropped Out of School to Create My Own Data Science Master’s — Here’s My Curriculum
With the recent advances in affordable, reputable online education, going back to college/university seems…
I ended up being right, or lucky. I learned more efficiently, saved $30,000+, and gained freedom of location and schedule. The bet struck a note with many and my curriculum went viral. That temporary internet fame caught the interest of three EdTech companies, and over the next few years, I shaped curricula and taught courses for them.
Since then, I’ve received many messages asking what my latest recommendations are, and unfortunately, I didn’t make the time to come up with a thorough answer.
Until now. This new curriculum is my original curriculum, but:
- smarter (I’ve learned a lot since 2015),
- with new content from 50+ expert instructors (the data education industry has proliferated)
- with better learning tools (software is eating the world),
- and with a paid community (they work).
The future of education is already here, and this curriculum and community is my implementation of it.
But how will I get a job without a “real” degree?
When you finish this program you won’t get a certificate from a “trusted” institution, and you may even get filtered out of some job applications. This outcome would be a blessing since companies that don’t embrace technology (educational, in this case) tend to fare poorly in the long run. You’re better served to find a company that properly values discipline and the desire for learning, or starting your own.
Find a company that properly values your discipline and desire for learning, or start your own.
Data-forward organizations don’t subscribe to credentialism. These are the places you want to be.
This program starts from the beginning. No prior knowledge beyond basic arithmetic is required, though high school algebra (as taught in Khan Academy’s Algebra I) is helpful.
Becoming an analyst-ML expert hybrid
The top trophy hire in data science is elusive, and it’s no surprise: “full-stack” data scientist means mastery of machine learning, statistics, and analytics.
In this curriculum, you’ll learn the analytics and machine learning pieces of the puzzle, making you what Cassie calls an analyst-ML expert hybrid. Per Cassie, “Analysts accelerate machine learning projects, so dual skillsets are very useful.”
I targeted this analyst-ML expert hybrid role for two main reasons:
- Completing a curriculum for a smaller (but still broad) hybrid role is a more attainable goal for most learners.
- The online resources available for learning analytics and machine learning are better than those for pure statistics right now.
Most “data science” programs make the same choice when presented with this dilemma. Teach enough statistics so students can grasp analytics and machine learning, but don’t make them PhDs in the subject. An example is UBC’s Master of Data Science, which would be my choice if I wanted to pursue the traditional education route.
This role is in high demand in most industries because data is transforming the world. Through self-directed projects (more on those shortly), you can tailor the program to target an industry that you’re passionate about.
Python-focused, plus the basics of SQL and R
For this curriculum, you’ll focus on learning data skills in Python. The content split by language is:
- 85% Python
- 10% SQL
- 5% R
Why? The data science ecosystem in Python (a.k.a. PyData) is vast and expanding, and the most advanced machine learning tools in the world are written in Python.
You’ll learn SQL (a domain-specific language for databases) because it is used everywhere. Nearly every data role requires SQL basics.
I chose to include R because 1) the R courses I recommend teach statistics well, and 2) I’ve found it useful having some R skills to complement my Python skills. If you ever need to use R for a specific task or job, you’ll be able to get up to speed in relatively short order.
The curriculum lives in this public Airtable. There are two terms to the program:
- Term 1: Data Analysis
- Term 2: Machine Learning & More
where “More” refers to additional, more advanced skills and tools for data analysis.
This two-term structure was chosen for two reasons:
- Those who wish to be a data analyst (a.k.a. Data Science’s Most Misunderstood Hero) can take Term 1 and have a clear endpoint for their learning.
- It’s nice to break up the curriculum into smaller pieces. Take a little vacation in between and treat yourself.
Learn → Frame → Assess → Create
Each term is then broken into multiple chunks, where each chunk contains a series of content in the following order:
- Learn: Primary learning materials, which include online courses, books, and tutorials. These resources are where you’ll learn a new skill or tool.
- Frame: Blog posts or YouTube videos that allow you to frame the skills you just learned in the broader data science industry.
- Assess: Adaptive tests that assess your grasp on these new skills. Your scores will be part of your digital transcript you’ll create at the end of the program to prove what you learned.
- Create: A self-directed project. You’ll use your new skills to create something unique on a subject that you’re passionate about. Your projects will also be a part of your digital transcript.
This process is based on standard learning design principles, though I place greater emphasis on the Frame step. I explain this process and why I chose it in more detail in this curriculum explainer.
In that curriculum explainer, I also outline the individual courses/books/etc. I selected.
Since I’m not employed by any company, I can be more objective about the individual resources I recommend. My only goal is to create the best possible curriculum.
A nice feature of me not being employed by any company is that I’m able to be more objective about the resources I recommend. My goal is to create the best possible curriculum from everything available on the internet, rather than from the catalog of my employer only.
Each term is roughly 65 days, where one day contains 4–5 hours of focused learning. Those estimates put us around six months for the entire curriculum if you study four to five days per week. This timeframe is comparable to popular data science bootcamps.
The program is adaptable to your schedule, so part-time over a longer period works just as well. You’re also welcome to just take Term 1, where you’ll gain the skills to be a data analyst.
Though it should go without saying, there is no physical requirement for this program. Your campus is where you bring your computer. 🎒
The price of the curriculum is determined by the individual resources within the curriculum. Some of them are free, some of them are behind a paywall. I estimate that the learner that completes the program in six months will spend as little as $249. Those who complete it in twelve months will spend around $375. Price varies with number of months because some resources require a subscription.
To put those numbers into context, most bootcamps are currently 10-50 times more expensive. Most master’s programs are 100 times more expensive. We’re talking about saving tens (or hundreds) of thousands of dollars.
Access to the full community experience is $8/month or $67/year. More on that next.
Parts of the community will be paid. Why? As Dru Riley writes in Trends #0045:
🔍 Problem: Free communities tend to be noisy without a barrier to entry.
💡 Solution: Paid communities use strategic friction to build high-signal environments. Members are invested and engaged. Community builders are aligned with members and able to invest in great experiences.
A Not a Real Degree membership is $8/month or $67/year and gets you:
- Not a Real Degree project guides
- Q&A and community commentary for projects
- Exclusive content, including articles, live streams, and podcasts
You can help shape the community. Suggest ideas in our community platform, I’ll set up what people want most, and we’ll stick with what works. I’ll also contribute to the community as a learner to continually test and improve the experience.
The community will launch soon. Join the waitlist!
Why I’m doing this
My main incentive is to help democratize data education. I believe:
- Access to high-quality education is a privilege, limited by wealth, class, and geography.
- Improving that access has an outsized impact on society.
- Education technology improves that access the most.
My goal is to make curating this curriculum and building the community my full-time job because this is where I can provide the most value to the world. I chose an affiliate revenue model blended with a paid community with that goal in mind:
- Affiliate revenue: Some of the resources I recommend are affiliate links, meaning I receive a commission (at no extra cost to you) if you use that link to make a purchase.
- Paid community: Access to the full community experience is $8/month or $67/year.
Interested in taking the program? Here are your next steps: