Have been searching around but cannot find too much information on the web about this. What are the differences between data science and computer science, if any? Would it be worth it to major in data science (my school doesn't have a computer science program) alongside either a major in finance or Applied Economics major or to just do data science by itself?
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So you want to work on Wall Street, but want to be a programmer? Major in Accounting/Finance so you still have the opportunity to work at the Big 4 (audit/tax) if Investment Banking doesn't work out. Also Finance and Information Systems works too.
Data science is a combination of three fields, and you'll see people define the job as any of the three, interchangeably and/or in combination: data engineering, math + statistics, subject matter expertise. The intersections of those fields mean a lot of things.
• Data engineering: programming using Python / R to automate tasks and do heavy lifting with things like SQL and other database technologies. • Math + stats: speaks for itself. You'll need this for the machine learning portion of the job. Right now, ML technology relies on a few different forms of statistical techniques, namely regression and classification. It goes much deeper than this and you'll need a firm grip on topics like linear algebra, calculus, and advanced statistics. • Subject matter expertise: if you work in advertising, you'll need to understand marketing verticals and modeling techniques for things like consumer preferences. If you work at a bank, you'll need a different skillset. The deeper your knowledge in a given area, the more valuable you are as a data scientist in that field.
So what does a data scientist actually do? You work with a lot of data (SQL + variants, Hadoop + Spark + variants), you probably work with distributed computing systems like Amazon Web Services to quickly expand your computing power on the fly, you fine-tune algorithms to reliably achieve some predictive power on that data, and you drive decision making from a purely theoretical perspective. You're the guy who "knows things."
This differs from traditional CS, where what you learn is truly computer science. CS teaches you things that you still need to know as a data scientist, like how processors work and why memory management in programming is important. But, this is only one piece of the puzzle. Think of DS as a combination of a math and CS major.
I would suggest a major in CS, but if your school doesn't offer one, I still wouldn't major in data science. DS is a rapidly evolving field and the fact is that even if your curriculum were refreshed right before you entered it, it's likely that by the time you finish a lot of what you learned will be behind the curve. Bleeding-edge academic research findings make their way into new algos via R packages (see: R programming) all the time, and are propagated through the business world immediately.
Being in your shoes, I'd pick a major that trains you to think a certain way, then evolve your skillset from there. Economics and Math are pretty much the two fields I would advise you to look at, and being that you're interested in finance, I say go for Economics without a doubt. If you can do an Econ major with a 2x minor in Math and Data Science, I think you'd easily be one of the most hirable candidates in your class.
Edit: forgot to compare against a finance major. I think finance is a fine major, but I wouldn't do it for a few reasons. First, it gives you a very narrow scope of knowledge. You learn math, statistics, accounting, etc. but only as they apply to the finance domain, and only enough for that. You'll be a jack of all trades in the business world, but not much else in any other domain (relative to non-finance majors). If you want IB or related career paths, finance is an option, but seeing as how you're interested in so many other things, I think you should pick a more high-level major with stronger roots in academia. If you can knock out a high GPA in math or economics, you can do pretty much anything, whereas people would still ask you "why data science?" even if you have a 4.0 as a finance major.
@Kassad this is why I love you man, you're one of the most helpful people on this forum as you are consistently giving quality advice.
Yes I was considering a possible applied mathematics/economics (applied or non applied) double major but I was not sure of how much use it would be to me, especially in VC/startups. Also am concerned that the mathematics major could lower my GPA. A minor does seem like an appealing option however.
I mean I don't want to completely rule out IB considering that what I truly want is difficult to break in out of undergrad in the first place (PE/VC), but it's not something that I think truly suits my interests.
In relation to DS and CS, thanks for clarifying the difference between the two. CS to me seems more theoretical in a lot of aspects and teaches you more than just programming and such.
EDIT: while we are talking, what do you think are some good ways to approach mathematics, to study for it in college, and become good at it? I wouldn't say I am brilliant at it, I only took pre-calculus as a senior. My issue with math is that I tend to somewhat forget the concepts after I finish the units. There are some things that I don't truly understand and I kinda push it off to the side and ignore it (ex. geometry, I still never truly got it and forgot much of it).
No problem - I do what I can to help. I would never have gotten any professional gig if not for strangers on WSO.
Math is tough unless you're truly interested, but a minor is more than enough to prove your mettle without over-exposing yourself to the topic. It would also go very well with a major in economics. My advice is to open yourself to as wide an array of opportunities as you can, which - when comparing an econ major vs data science - means doing econ. You can minor in either/or math/data science and you'll have a pretty desirable array of skills.
With regard to approaching math - practice. I sucked at math when I was younger because I didn't have the attention span to teach myself new ideas or to listen to some foreign professor yammer on in incoherent English (I went to a non-target). Long-read the textbook chapters, do problem sets until you get the material, and don't get up from your seat until you do what you set out to do. Forgetting ideas is normal since we don't practice everything we learn, but as time goes on and you do internships / take higher level coursework, the important concepts will remain ingrained in your head. Just focus on pulling a high GPA your frosh/soph years, and you'll thank yourself in junior/senior years.
This is good info
Data scientists program computers in various computer languages (Python, Java, R etc.) and have good knowledge of data structures and algorithms.
Data science involves building models based on machine learning which is a subfield of computer science. Some data scientists also work on Information Retrieval and Natural Language Processing (NLP).
Data scientists use advanced technologies like Hadoop, relational and NoSQL databases, which built by computer scientists. A lot of data scientists have advanced degrees in computer science where they learn some of these skills.
Though a computer science degree is not necessary, a huge amount of data scientists come with an educational background in statistics, physics, economics etc. But programming is a core aspect of data science.
A lot of data scientists study data generated on the web, tech companies consume a big proportion of data scientists.
Data Science is the amalgamation of three different streams, namely: statistics, maths, and data engineering. People who take up Data Science studies manage to easily find a job in one of these fields. Data engineering uses Python/R to make tasks automated and combine SQL and other database technologies to do the heavy lifting. A combination of Maths and Stats lets you do the machine learning section of the job. To effectively utilize Maths and Stats, you must have a firm grip on various topics like calculus, linear algebra, and advanced statistics.
The primary relation between Data Science (DS) and Computer Science (CS) is that you study concepts under CS which you need to utilize in your Data Science study as well. Data Science can be understood as a combination of Computer Science major and Maths. Companies like Acuity Knowledge Partners utilize Data Science in financial services helps businesses gain an edge over their competitors and become the market leader in the industry.
I am a data scientist myself, (well technically I haven't started my job yet) and I would recommend a computer science degree for someone who is interested in going down this route because:
1. Specialized data science majors are too new and sprang up around the recent demand for data scientists, while the CS degree is much more established.
2. Math/stats majors are too... out there. A lot of time slogging through proofs and stuff. Physics is even harder to apply.
3. Flexibility. You can also shoot your shot at being a SWE or MLE at a tech company or a quant researcher if you so desire.
Computer science is the main branch whereas Data Science is a branch of Computer Science. Data science includes statistical analysis as well. The easiest way to understand the difference.
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