We build a recommender system from the ground up with matrix factorization for implicit feedback systems. We then deploy the model to production in AWS.
Part of the General Assembly Data Science Bootcamp Series
I enrolled in a Bootcamp
I’ve been trying to teach myself Data Science for a while now using countless resources available online, many of them are actually free!
And I have started a few times already, what typically happens with me is I start off with a blast of enthusiasm, which powers me for the first few weeks or so. I got to learn individual concepts this way, this is how I actually learned Python, as well as some statistics.
However, I have never really reached a stage where I completed an end-to-end Data Science project. Somehow after a few weeks, the novelty and the enthusiasm wears off.
And I guess the fact that I’m going at it alone and no one to talk to about it and nothing to apply it to, it just falls off silently. My focus just dies off.
And believe me, I tried. Several times.
It’s not you, it’s me.
Albert Einstein defines insanity as doing the same thing over and over and expecting a different result. Now, I’m not really sure if in fact he said that, however the statement has merit and makes a lot of sense. So I’m going to take his word for it.
So I thought of a few options:
In the end, I chose General Assembly’s 10 week bootcamp for a few reasons.
- It will be delivered as live online sessions and in my timezone in Sydney.
- It will have a real living and breathing instructor.
- I will have a cohort that I can work and learn this with.
- I will have a finite 10-week window to complete it.
It is a significant investment in time and $ however, skin in the game right? At the very least, this will motivate myself to complete the process to the end once and for all!
What I want to get out of it
I simply want to get to the end of this course having completed the capstone project to the best of my ability. I have full confidence that I can combine my full stack development knowledge with all that I would have learned in this course. I am particularly keen in developing exciting and engaging visualizations. I want to be able to tell a compelling story with technology and Data Science.
Week 1 - Introduction & Python, Terminal & Git
Before your first meeting, GA has this Pre-work that you need to complete. Basically getting you ready for the intense 10 weeks ahead. Nothing to sneeze at - 11.5 hours of Python and Statistics. Solid grounding of what lies ahead.
The first meeting was exciting, I got to meet my instructor, an entrepreneur/CTO at an early stage VC startup with more than a decade of experience in data, analytics & ML. A very personable and humble person that you cannot help but like.
I got to meet my cohort, my learning buddies for the next 10 weeks, a pretty diverse bunch - one from Europe, a few from Singapore and the rest from Sydney and Melbourne, and of different stages in their career. I was enthralled as I was listening to them as they introduced themselves. To say I was excited was an understatement.
Mostly discussed basics in Python with lots of exercises, and I like the Zoom breakout rooms, where you get to do some pair programming, no better way to cement those concepts right? Making sure that development environment is all sorted.
Jupyter Notebook, check. Enterprise GitHub account, check. Learning mindset, check.
The 10 week bootcamp is a 3 hour session (6pm to 9pm AEDT) twice a week (Mondays and Wednesdays) and this will continue until it ends on the 22nd December 2021. A total of twenty 3-hour meetings, and I would guess countless more hours of homework.
OK heads down Jose let’s do this.
- Jupyter Notebook: An Introduction
- Python Introduction
- An introduction to Git: what it is, and how to use it
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