Masters Or No?
The reality is that I applied to the Berkeley program and received a rejection. I didn’t have the most successful university career, and now, a decade or more later, I am returning with completely different ambitions — from English Literature to Machine Learning. That’s my goal, and that’s what I am going to work towards.
After discussing with the admissions team, I’ve come up with the following plan:
- Mathematics
- Coding
- Projects
Mathematics
I found an awesome course a few months back by Deeplearning.AI, entitled “Mathematics for Machine Learning.” I’ve already started and am going through the Linear Algebra portion. Matrices and vectors — I’m going to spend my days going through this, followed by calculus and, finally, statistics. In fact, it was Lex Fridman’s podcast with the French Computer Scientist Yann Lecun that made me realize just how important statistics is to Machine Learning. So, I’m definitely excited to get into this course.
Coding
I’m still not 100% sure how I’ll tackle it. The University of Michigan, who I also spoke to, has an online course on Python 3, which apparently is quite important for students seeking acceptance into their Master’s program.
I also feel that simply going through everything that I am doing will also give me the requisite skills. However, Berkeley made it quite clear that a programming course is needed. So, with that being said, I believe I’ll end up taking that Python course or another similar one that I come across that is notable.
Projects
This is fun. Now that I’m joining the DataTalks Machine Learning Engineering course, they require 2–3 projects. Some of the project ideas I had utilize sensitive data, so I suppose I won’t be able to do those. But I really wanted to do something that utilizes financial data for my current role. If not, I have some awesome ideas around health data that my family inspired me to explore after some conversations around health professionals and their schedule. I’ll have to come back to this! Regardless, I will have 2–3 projects completed by the end of the year for this program anyway.
All in all, I have some forward momentum. Now it’s all about keeping it going. I’m looking into some accountability coaching and mentorship as well, but I’ll save that for another post. For now, The Rookie Data Scientist needs to get back to work.
Arthur Naseer Richards
The Rookie Data Scientist
