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Data Science BSc vs Computer Science BSc

What are the similarities and differences between these degrees?

Which one opens more doors into areas such as consultancy, data analytics, engineering and entrepreneurship?

For the above areas, could it be more beneficial to take a BSc in something like Mathematics and Economics or Computer Science, and then progress with an MSc in Data Science?

Some people argue that a Data Science BSc is useless, since the discipline itself isn't defined in definite terms. Furthermore, professionals within it often have degrees in areas with relevant transferable skills such as computer science, machine learning, maths, statistics and economics. Is there any sense in this?

Can anyone studying one of these degrees give insight into their own experiences of them (particularly anyone studying at The University of Nottingham)?
@Princepieman
Original post by Engineering Lad
What are the similarities and differences between these degrees?

Which one opens more doors into areas such as consultancy, data analytics, engineering and entrepreneurship?

For the above areas, could it be more beneficial to take a BSc in something like Mathematics and Economics or Computer Science, and then progress with an MSc in Data Science?

Some people argue that a Data Science BSc is useless, since the discipline itself isn't defined in definite terms. Furthermore, professionals within it often have degrees in areas with relevant transferable skills such as computer science, machine learning, maths, statistics and economics. Is there any sense in this?

Can anyone studying one of these degrees give insight into their own experiences of them (particularly anyone studying at The University of Nottingham)?


Afaik, you should just choose whatever course you like the sound of more. There's really no reason why any of the courses you've highlighted would bar you from any of the careers you've indicated having an interest in.

Data Science as a degree is essentially just maths and cs with a focus on stats, despite the fancy sounding name. If you're into that, sweet!
Original post by Princepieman
Afaik, you should just choose whatever course you like the sound of more. There's really no reason why any of the courses you've highlighted would bar you from any of the careers you've indicated having an interest in.

Data Science as a degree is essentially just maths and cs with a focus on stats, despite the fancy sounding name. If you're into that, sweet!


That's fair.

Can you comment on what makes a Data Science BSc course a well put together one, rather than a mish mash of CS and maths with no particular ministration?

This is the one I'm looking at:
http://www.nottingham.ac.uk/ugstudy/courses/computerscience/bsc-data-science.aspx
@Princiepieman any idea how the data science and discrete maths degrees at Warwick differ or are they pretty similar?

From what I can tell the disrete maths may have a little extra pure maths. Im still looking at IBD I just want a more interesting degree.
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Randnom
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Data science is not useless, but rather an interdisciplinary field aimed at extrapolating meaning from complex, often unstructured data. My suggestion would be to obtain an undergraduate degree in computer science which contains advanced maths and statistics, and of course some software development. This would be set you up for further studies in many fields, such as Data Science, AI and ML, just to cite a few.

If you prefer to get into work straight away following a degree, then a BSc in Data Science may be more appropriate, as it is very applied in nature and will teach you skills which can be used out of the box. Regardless, many students do changes their ideas at the end of an undergraduate degree, therefore I would recommend you keep your options open and study an undergraduate with a solid foundation in the computational/engineering sciences.

For example, a BSc in Computer Science/Engineering with applied Mathematics would give you plenty of options at the end of the three years. Good luck and take the long-term view on education :-)
(edited 3 years ago)

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