If I had to choose the most brutally difficult course in my entire self-learning journey, it would undoubtedly be the Udacity Self-Driving Car Engineer Nanodegree (SDCND).

Born in 1996 with only a Diploma in Business Studies, I had zero Computer Science (CS) background. I had never taken university-level advanced math or higher physics. Yet, I not only conquered this Autonomous Driving program—a course that leaves many CS master’s students struggling—but also became one of its top early graduates globally.

Here is the story of my “unconventional” path, and why the Udacity SDCND was the hardest—and coolest—course I’ve ever taken.

1. A Business Student’s Unconventional Start in Code

My passion for programming wasn’t sparked in a university lecture hall.

During my years studying Business, I realized I was far more fascinated by code and technology than business theory. Lacking formal resources, I turned entirely to self-directed learning:

  • Scrappy Foundations: I learned computer basics on Alison and picked up coding syntax on SoloLearn and W3Schools. Curious about what “real CS majors” learned, I reached out to my high school classmates in university, asking for their course syllabi and lecture slides. I’m still deeply grateful for how generously they shared them with me.
  • Learning by Doing: After building a foundation in web development, I started using WordPress to build websites for relatives and friends. Word spread, and I eventually took on freelance gigs creating official websites for local small businesses and manufacturing companies.

Right before graduating, I researched industry trends and noticed a recurring pattern: the best software engineers are ultimately self-taught.

Given my family’s financial situation at graduation, I decided against pursuing a traditional degree path and chose to commit entirely to self-learning. It was a lonely road from day one—my family couldn’t understand my choice, nor did they support me spending hours staring at a screen “messing around with code.”

Facing skepticism and cold shoulders, I bet everything on online learning resources.

2. From Web Dev to Autonomous Vehicles: My Self-Taught Tech Tree

Without a prestigious computer science degree, Massive Open Online Courses (MOOCs) became my ultimate weapon.

I built my own step-by-step learning roadmap, soaking up knowledge across Coursera, edX, and Udacity:

🎯 My learning path at that time：

Front-End ➔ Full Stack ➔ Android Development ➔ Data Analyst ➔ Machine Learning ➔ Self-Driving Car

I tackled one domain after another—starting from simple front-end pages to building full-stack applications and Android apps, then mastering Python data analysis and machine learning.

Then I came across the Self-Driving Car Engineer Nanodegree (SDCND) launched by Udacity founder Sebastian Thrun. Autonomous driving was at its absolute peak of technical excitement.

Back then, Udacity operated on a highly selective, application-based cohort model. With tech leaders racing to develop production-ready autonomous vehicles, over 11,000 applicants vied for just a few hundred spots—a daunting ~2–5% acceptance rate.

I applied immediately and was thrilled to be accepted into the second global cohort (November 2016). Surrounding me were brilliant peers hailing from elite tech giants like Google, Apple, Microsoft, Amazon, and AT&T, as well as top-tier institutions like MIT, Stanford, CMU, Princeton and NUS.

Learning alongside such extraordinary talent pushed me to my limits. After months of late-night coding and complex robotics projects, I officially graduated as part of the inaugural class of Udacity’s Self-Driving Car Engineers.

References & Further Viewing

Related Videos & Media

  1. Oliver Cameron (CEO, Voyage & Former Udacity SDCND Lead) on MIT Self-Driving Cars: https://www.youtube.com/watch?v=-j0tc0Y1CIE

  2. Stanford AI Lab & Sebastian Thrun - DARPA Urban Challenge 2007 Documentary: https://www.youtube.com/watch?v=dHHppGy3p_c

  3. Udacity Self-Driving Car Engineer Nanodegree Overview: https://www.youtube.com/watch?v=JkqmOUNbNfY

3. Why Udacity SDCND Was the Hardest Challenge for Me in 2017

If web development was about building user applications, SDCND in 2017 was hardcore low-level logic fused with cutting-edge algorithms. It demanded exceptional proficiency in C++ and Python, alongside heavy mathematical and physical modeling across three core pillars:

  1. Computer Vision: Using OpenCV and deep learning to extract lane lines and detect traffic signs with extreme precision on image matrices.
  2. Sensor Fusion: Implementing Kalman Filters and Extended Kalman Filters (EKF) to fuse real-time noisy data from LiDAR and Radar. The mathematical derivations were unforgiving.
  3. Localization & Path Planning: Particle Filters, A* search algorithms, and control theory (PID control and Model Predictive Control). Every project required precise vehicle control inside simulated environments.

Coming from a background with only a Diploma in Business Studies, the learning curve wasn’t a ramp—it was a vertical wall. I had to learn multivariate calculus, linear algebra, and physics on the fly while simultaneously optimizing performance in C++. To bridge the massive gap, I bought physical math textbooks and ground through supplemental courses on DataCamp and Khan Academy late into the night.

At barely 20 years old, while my peers were enjoying traditional university life, I was the complete outlier. I had no safety net, very little money, and virtually no support system. Back in 2017, spending days locked in my room trying to “self-teach self-driving cars” sounded like a joke to the people around me. No one truly understood what I was building toward, and the isolation was heavy.

Yet, I gritted my teeth and kept going simply because I had no other choice. My days were defined by dozens of open Stack Overflow tabs, dense academic papers, and local C++ compiler errors. Every rejected project review tested my mental resilience to its absolute limit, but each resubmission pushed my engineering rigor to a level I never knew I was capable of achieving.

4. Academic Rigor: Master’s-Level Recognition (NZQA)

The sheer depth and academic rigor of this curriculum were far beyond standard online tutorial certifications.

In fact, national qualifications authorities recognized this groundbreaking level of education. The New Zealand Qualifications Authority (NZQA) formally evaluated micro-credentials and assessed the Udacity Self-Driving Car Engineer Nanodegree as equivalent to a 60-credit course at Level 9 (Master’s level) on the New Zealand Qualifications Framework (NZQF).

🏛️ Official Recognition: New Zealand officially welcomed micro-credentials into its national qualification framework, evaluating top-tier industry programs like the SDCND to match the rigors of postgraduate and Master’s-level university studies.

Reference: New Zealand Government Release — New Zealand welcomes first micro-credentials

5. Why the First Cohort Carried So Much Weight

The modern, revamped iterations of SDCND have lowered the barrier to entry significantly, but back then, the original curriculum took around a full year of grueling effort to get from admission to graduation. Being part of those inaugural batches meant stepping into a world with exceptionally high stakes and prestige:

  • Direct Collaboration with Tech Giants: This wasn’t just theoretical coursework. Led directly by Sebastian Thrun (“Father of the Self-Driving Car”), the program was co-developed with global pioneers and automotive leaders including Mercedes-Benz, NVIDIA, DiDi, Uber/Otto, and BMW. The curriculum was designed to mirror real-world production stacks, meaning graduation credentials carried immense weight across the autonomous driving ecosystem.
  • A High-Filter Talent Pool: The original admissions process had a strict screening mechanism requiring detailed applications, code evaluations, and background reviews. Most of my peers in that initial group held Master’s or Ph.D. degrees from top-tier universities worldwide in Computer Science, Electrical Engineering, or Robotics, or possessed years of professional hardware/software engineering experience. Many of my classmates were graduates from elite institutions like Stanford, MIT, and Carnegie Mellon—meanwhile, I entered with no formal CS background and a business diploma.
  • Fast-Track Industry Pipelines: Thanks to deep partner involvement, early graduates received priority interviews and green-lit fast tracks at industry leaders like Waymo, Tesla, Cruise, Baidu Apollo, and DiDi. The vast majority of early alumni went on to become core R&D pillars at top AV startups and tech giants, solidifying the prestige of the initial SDCND badge.

6. Accelerating Ahead: Graduating with Batch 1

Although I was admitted as part of Batch 2, I poured nearly every waking hour into the coursework, pulling endless late-night sessions to complete the hardcore projects at breakneck speed.

My effort paid off: I caught up with the schedule and graduated alongside the very first global cohort (Batch 1)!

Because of this accomplishment, I received a personal invitation from Udacity founder and former Google Self-Driving Car pioneer Sebastian Thrun to attend the official graduation ceremony in the United States alongside graduates from around the world.

For a self-taught practitioner in their early 20s, it was an incredible validation and honor.

7. Regrets and Reflections: A Clear Conscience

Reality, however, brought its own constraints.

In my early 20s with limited financial means, I simply couldn’t afford the flight and accommodation to the US. To make matters harder, my family still didn’t understand online education or autonomous driving, seeing it as an impractical pursuit.

Ultimately, I couldn’t attend that graduation ceremony across the ocean.

Do I have regrets? Absolutely. But do I regret the journey? Not for a second.

Going from a business student copying HTML snippets on W3Schools to a self-taught engineer implementing sensor fusion in C++ proved something invaluable:

  • It completely reshaped my learning methodology—proving that with the right drive, no technical ceiling is unbreachable.
  • It gave me unshakeable technical confidence—if I could master self-driving algorithms, no new technology or framework could intimidate me again.

Self-directed learning can be a lonely road marked by doubt and real-world hurdles. But once you write that first line of code and keep going, every late night and effort spent becomes the strongest armor you carry forward.