I believe it depends right. Both sound great options and will get your foundational layer covered for the most part. Even within option 1, I can assume you will get basic programming such as R (for statistical analysis) or Python. A better question for ya might be: what you do aspire to use this masters degree or what are your future professional goals? Looking to apply these skills in the industry as a data scientist? Do you intend to pursue a PhD in the future? Is research & the higher theoretic field something you’re passionate about? Based on those questions option 1 sounds more theoretical and could help you further down the line. Option 2 sounds more applied and could help you understand how these skills can be applied in a company. The type of industry niche could play a factor too! Thinking of going into manufacturing? I can tell you first hand that our data scientists often work on creating models but often doing a bit of other roles such as data engineering roles and even operations. Perhaps you want to go into big FAANG companies, then maybe those companies have more of a budget to spend solely on r&d and thus option 1 could be helpful. At the end of the day, both could work for anything because if you do option 1 you could work on some side project that involves more programming! On the contrary, if you go for option 2, you could also do self study on very specific math intensive subjects at your own pace while you learn industry ready skill. Hope my very small 2 cents help in some way shape or form! Best of luck!

May I ask which bootcam did you complete?

No problem Jake!

I studied maths for my bachelors. A life science background could help, but it really depends on how much stats you know. IMO, the more confident you are with stats, the easier the data science master's will be.

It's a lot indeed!

It's gross income.

Technically 2 minutes if you play at 2x speed.

hey there! did you manage to get a job in uk?

Thanks for the feedback. I'd add that it also depends on the infrastructure you work with, and since (at least from what I've seen) most companies prefer having a more general infrastructure, Python seems to be the more logical choice for a data science team to use.

I don't know much about Leeds Beckett University, so I can't really comment on that. Since I'm biased, I'd obviously suggest going to University of Leeds instead but it's really up to you and what you want out of the course.

If you're 100% interested in data science, then I say go for it!

I don't know much about data science bachelor's degrees but I'd assume they'd be more practical than something like a pure maths degree.

Thanks Akansha! I'll try my best :)

No problem! I'd say as a graduate the biggest thing is to make sure you have enough projects on your CV to talk about in the interviews. Try apply to as many companies as possible and really try your best to research each company and their products before beginning the interview process.

Yes, a data science master's will cover the fundamentals. Since you have a maths background, you can pick more programming or data science theory modules if that's what you want to focus on instead.

Thanks Stella! Glad you found it helpful :D

That's a really difficult question for me to answer. I'd suggest having a look at reviews for each course and see whether the course content matches with what you want to learn.

I've seen people go from an analyst to data scientist in 2 or 3 years. but some people take longer. It really depends on what you're exposed to at work. If there's already a bunch of data scientists that you get to work with, then I'd imagine it'd be much quicker to progress.

@@LeonLokk thanks mate for your opinion, not too keen in going back to uni while working as well as the ridiculous fees, I’ll probably play the long game with this 😂

No worries! Going back to uni definitely isn't for everyone, so I get that 🙂

Of course I'd recommend the University of Leeds because I studied there 😉 but in all seriousness, I can't answer that question and I'd recommend you doing your own research based on your own needs.

I've seen people with all sorts of different backgrounds, so you're probably fine. Engineering is at least a STEM subject, which will definitely help in data science.

Thanks Hanson! It's on my list of videos to create, so hopefully I can make a tutorial once I have the chance to sit down and go through the code properly again.

It's hard for me to suggest any universities besides my own, as that's the one I studied at. I can only suggest doing your own research on what the course offers and whether it matches with your interests and needs.

It took me around half a year due to COVID lockdowns at the time.

Yes I'm British. It really depends on the company but from what I've seen, it's usually the larger international corporations that are more open to sponsorships.

They didn't really teach Python besides in the machine learning module. They focus mainly on R because of it's popularity in academia.

I had no work experience during my time at secondary school. The best thing I can suggest (and wish I did) is to do your own projects in your own time and treat them seriously like you would with work experience. Most employers can understand if you don't have any work experience in the beginning, so having a portfolio will make you stand out a lot.

Thanks! I'd say it would definitely increase your chances. The only thing I'd recommend is making sure that you do enough core maths modules needed for data science, and not just focus on the computer science side of things.

@@LeonLokk Thank you, this info is invaluable. Gonna think deeply about this.

Haha yep, sadly that's the case for many companies!