The first year of Statistics & Machine Learning

Now that my first year of studies is coming to an end I want to give you a bit of a recap of my experience and tell you what you can expect in first and second semester of the Statistics & Machine Learning program. 

The first semester

This is an exciting time. Everything felt fresh and new and I was excited to start working on my master’s degree. The first courses we had very mostly intended to bring everyone in the program to the same level, as we have a very diverse set of backgrounds. There were courses in writing, R programming and statistics. These courses were certainly tough, as we had weekly assignments to hand in, in addition to exams. This required me to always stay on top of the work as you really couldn’t fall behind. But in the end I got through it and I think it was a worthwhile experience for me.

At the beginning of November the second period of the first semester started. This meant that we now had courses in Machine Learning and Computational Statistics. These courses were the first time I really got to learn a lot of new input and especially the Machine Learning course was very interesting. The Computational Statistics course on the other hand was very demanding, with tricky weekly assignments, while the content we learned didn’t feel very relevant to me. 

The second semester

A new experience for me was that there is no winter break at all in Sweden. After we wrote our exams in January, the next semester started immediately. This is something I wish I knew before, as my hopes of doing some winter travelling through snowy Sweden were squashed.

In the first period (January – March) I had two courses again: Introduction to Python and Advanced Data Mining. In the second semester you have to choose one elective course and most of my classmates chose one that took place in this period, but I didn’t. This meant that my schedule was pretty light in these months while everybody else was very busy. A bit of an awkward situation and I hope the university will improve on this by scheduling all electives at the same time.

The courses were fine, I didn’t have too much experience with either topic, so it was interesting to get an introduction. Sadly the python course is only a short one, as I think it could have been even more useful if it contained more content. Keep in mind that in this program you’ll mainly be programming in R, but there are some courses where you use Python instead.

Now onto the second period. Now I had a stressful time ahead of me, as I had four courses at the same time. These were Deep Learning, Bayesian Learning, Big Data Analytics and my chosen elective Sports Analytics. I was also excited about this period as it meant getting into some interesting topics.

And the courses certainly were interesting, but they were also pretty demanding. I feel like Sports Analytics and Deep Learning were a fun and useful experience. For everybody who is interested in sports, I can recommend the Sports Analytics course wholeheartedly! In the other courses we also learned a lot, but we’re also struggling with the Lab setup and the assignments at home. Sometimes it was hard to deal with 4 courses at the same time, with different programming languages and a different focus. 

Now I’m in the final study period before my exams and then I’ll be off for almost 3 months. Some students use this time to complete an internship, but I’m looking forward to some exciting trips and of course some relaxing time at home.

All in all I think I can say studying this program at LiU is very much an “involved process”. You can’t just lean back und wait for the exams, instead there are always assignments and labs to complete. While this can be very stressful, I think I prefer this, as especially concepts related to programming benefit a lot from a “Learning by doing” approach.

I hope this post was a help to everyone thinking about studying this program in the future. If you have any questions, feel free to leave a comment!

Hej då, Niko

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