My first week...

Here I am, writing this second post in my #66daysofdata, sitting in the back of my brother-in-law's car after having spent the weekend at the beach. Obviously things like family, household and work will always take up most of our days, but there are a thousand excuses not to do something. However, as for this first week I can proudly announce: mission accomplished. These past 7 days, I found myself consistently spending five minutes or more refreshing known data science concepts or learning new things. Here's how that went:

Safety net

First of all I knew I would need a safety net that offers some sort of small bite-size daily exercises, lessons or tests of 2 minutes or less, that I could fall back on in case I couldn't find the time to engage in a larger activity. I've been a consistent Duolingo user since January 2022 and I feel 2-minute 'lessons' are a great way to keep you engage with whatever you want to get better at, be it practicing a language, getting more familiar with chess or data science (in my case, all three of them apply). This safety net I found in DataCamp 's practice module of their mobile app. The exercises are a great way to refresh certain coding concepts. So each week I'll update you all with a table containing what course I focused on for that day and how much XP I gained. At the end of my journey I hope I can look back on a whole lot of experience!?

Summary table week 1:*

* note that the XP is per day, not cumulative

Recommender systems & Neural Networks

Now, with this practicality out of the way: let's get into what I did last week. I started of by writing down what projects and concepts I really want to get the hang of. On top of that list were 2 data science techniques I know next to nothing about (on the moment I wrote the list, that is). These 2 techniques are, as the subtitle spoils: recommender systems and neural networks.

So, on Monday, Tuesday and Thursday I read through the slides of the first class of the course 'recommender systems' at UGent. This is a course I didn't take, but I did download the contents out of curiosity back when I was a student. This first course introduced the matter and was all about taxonomy (the kinds of systems there are). For example, I learned the differences between collaborative filtering systems and content-based systems. A very interesting topic. One goal of these 66 days is to build a useful recommender system I can use, so if anyone has any ideas, there's a comment section below, or send me a DM!

On Wednesday I had no time to spare to read or code, so Datacamp's safety net already helped me out!

On Friday, I started watching a YouTube series on Neural Networks by The Coding Train (playlist can be found here). I really enjoy how Daniel Shiffman breaks down the complex mathematical structure into understandable video's. For now, I've seen 4 video's in this series, but I'm very familiar with his other work, I love watching this enthusiastic guy code! I really look forward to what is to come in this neural network series and build one myself!

As for this weekend, I took the time on Saturday to start practicing data wrangling on Kaggle, using a LOL-esports dataset once more. This project will probably be used and built upon later though. Today, on this ungodly hot Sunday, I only did my Datacamp exercises in the car on the way home.

And that was it! Week one, completed! I can honestly say I'm glad I took on this challenge and I look forward to keep learning! See you all next week!