[kongzi]

I've browsed this forum where posters talked about careers in law, medicine, IT, etc. Mostly negative posts.

http://www.rooshvforum.network/thread-38408.html
In the above thread for IT for example, it seems that there's low prestige/lack of respect for programmers and IT workers. Also, apparently a lot of older guys enjoy tormenting the younger guys since they see them as competition. However, one of the good things about IT is that there's plenty of jobs worldwide, so if one has experience in the US, he can get jobs in Asia/Europe in an English-speaking work environment, which is good for us since we want to leave the US

Has anyone here worked in or know alot about quantitative jobs in finance, statistician/data analyst, and numerical modeling jobs compared to IT jobs? I'm about to get a Master's degree and am thinking about getting into one of these careers.

[Peregrine]

Quote:

Has anyone here worked in or know alot about quantitative jobs in finance, statistician/data analyst, and numerical modeling jobs compared to IT jobs? I'm about to get a Master's degree and am thinking about getting into one of these careers.

What do you want to know?

[one-two]

I want to be a data scientist too. Seems like a solid career for the next decade.
@Peregrine, are you in this field? What sort of data do you work with and are you hiring?

[Peregrine]

Data is data. The specifics aren't important. Why does it matter if I'm hiring?

[Seadog]

Briefly looked into this a while ago, I forget why, and one of the biggest utilizer of this sort of employee is insurance companies. Actuaries. Their entire business model is based on statistics, cause and effect, risk factors, then quantifying and assigning a dollar figure to them.

The thing about this is it can be fairly high level modelling stuff, and like blackjack card counting, slight edges can manifest themselves in the very long run, which are all but invisible in the short.

[edtf]

I've been working in bioinformatics and big data applications to genomics. Let me know if you have any questions.

[Pinocchio]

i'm an analyst for a broker in london, work mainly in sql and visual studio.

[one-two]

(09-18-2014 10:53 PM)Peregrine Wrote:  

Data is data. The specifics aren't important. Why does it matter if I'm hiring?

Good answer. Cause I want to be a data scientist

[Travesty]

^ Peregrine

Did a woman take over your keyboard?

Data scientists can make alot especially if you start your own consulting biz.

All big tech companies have a large need for them to track user behaviors.

I am sure there are many other Big Data realms esp. financial, medical, energy, telcomm etc... where these skills are extremely valuable.

[Peregrine]

(Point taken. I was drunk last night and pissed that I didn't get laid.)

Data scientists are employed by all manner of industries. Some examples include social media companies like Facebook and all manner of financial institutions.

I work for a multinational financial institution. I can answer questions about quantitative jobs in finance, statistician/data analyst, and numerical modeling jobs etc.

[cmrocks]

I'm currently in a masters program for geostatistics; sometimes known as spatial statistics. My research group is primarily focused in mining and petroleum but the science can be applied to many disciplines. Companies are collecting more and more data and a lot of them don't know what to do with it. Absolutely, going forward, people who are trained in data analysis will have no problem finding work. In addition, we've finally reached a point where computing power can handle the sheer volume of data and size of the models that are necessary for real world applications.

My favorite thing about data science is the ability to quantify the value of decision making. For example, in many cases the "truth" is unknown. We collect data and statistics in an attempt to learn this truth but there is never enough data to truly know the truth. Instead, we get distributions of likelihood; the P10, P50, P90 etc. From this, we can model worst, average and best case scenarios and quantify the monetary value of these decisions.

[kongzi]

Quote:

What do you want to know?

Maybe I should I should create a new topic for this, as I should've mentioned my background and personal goals in the OP: I'm looking for advice about how to get the best lifestyle in my 20s. I'm currently in a master's program in a STEM field in the US. I have spent a year working abroad in a small company in Asia and found it to be a gigantic waste of time with asshole coworkers. I have traveled abroad as well, but didn't manage it as effectively as I should have. I want to ultimately work and live abroad (in a better country than my previous experience) and work as a freelancer/for myself because I find the US to be very socially diseased and the women to be bitchy. Since I like math and am good at it, I'm thinking stats/data science is a great career path for me. This is opposed to IT, where I find working on mobile apps to be very boring. I have some programming skills, but it's not good enough to get hired by the good companies since I'm rather weak on algorithms/data structures

Quote:

I can answer questions about quantitative jobs in finance, statistician/data analyst, and numerical modeling jobs etc.

Would you recommend quant/statistics/data science/numerical modeling as a better career than IT? Is it more lucrative than web development and other IT work?

What are the people like?

Since Americans like bad boys and jocks more than geeks, would you say the prestige is low for these careers? Is it any better in Europe or elsewhere abroad?

Are you required to spend alot of your free time working on personal projects and keeping up with technology news, like for IT careers?

I doubled majored in applied math and another STEM field when I was an undergrad. If I get my Master's in a STEM field (not Statistics or CS) and have some programming skills, how can I get a job in those fields? how can I catch up with those who have degrees in MS Statistics to get a job? Do I need to learn R and SAS on my own and then use them to create personal projects, like programmers have to? Or am I better off studying algorithms and data structures?

Or if I don't like my current master's program, should I just look for data analyst/etc jobs now? Or the better ones require a Master's?

Is it true that there aren't many entry-level jobs in data science, and most job openings require a phD or lots of work experience? If so, am I better off getting an entry-level job as a quantitative programmer or statistician/data analyst?

If I want to live abroad, should I apply for quant/stats/data/etc jobs abroad, or work in the US first to build up experience?

Since I ultimately want to work as a freelancer/work for myself, is it easier to get into that if I first work in stats/data science as opposed to IT?

What are some good grad-level courses I should take?

[polar]

People in this field:

What are the top (3-5) specific skills, qualifications, software certificates, etc. that one can pick up to qualify for an entry-level role? Can someone drop a data sheet, particularly with recommendations on materials for self-study?

[Pinocchio]

im 28 and earning +£100k, self taught on sql and vba. you need to know sql inside out imo, plenty of free material out there, then learn how to publish reports in visual studio/ ssrs.

[Peregrine]

Preface: I'm talking about quantitative analysis/modeling as it pertains to financial institutions.

(09-19-2014 03:22 PM)kongzi Wrote:  

Would you recommend quant/statistics/data science/numerical modeling as a better career than IT? Is it more lucrative than web development and other IT work?

If you're good at math and enjoy it, I would recommend it over IT. Math/stats skills certainly have a longer shelf life than IT skills.

Salaries range from 70 to 300k depending on rank. You could make more, but then you'd be in senior management rather than doing the actual analysis. Pay raises are mostly driven by promotions and job hopping. New jobs are easy to come by.

Hours are generally 40 to 60 a week.

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What are the people like?

Depends on the complexity of the data that you are working with. As the complexity increases, the people working on it tend towards shy/weird/awkward/borderline autistic.

Quote:

Since Americans like bad boys and jocks more than geeks, would you say the prestige is low for these careers? Is it any better in Europe or elsewhere abroad?

I would say it's neither plus or minus prestige.

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Are you required to spend alot of your free time working on personal projects and keeping up with technology news, like for IT careers?

You have to stay apprised of industry developments, but that's true of any career. If you're just looking to punch the clock, no.

Quote:

I doubled majored in applied math and another STEM field when I was an undergrad. If I get my Master's in a STEM field (not Statistics or CS) and have some programming skills, how can I get a job in those fields? how can I catch up with those who have degrees in MS Statistics to get a job? Do I need to learn R and SAS on my own and then use them to create personal projects, like programmers have to? Or am I better off studying algorithms and data structures?

Which STEM field is your Master's in? Math is preferable (for obvious reasons). Physics is second best.

Learn R, SQL, SAS, Matlab, and Excel (and Powerpoint, because you will usually present your data with it). If you're proficient with all/most of those, you should be fine. No personal projects necessary.

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Or if I don't like my current master's program, should I just look for data analyst/etc jobs now? Or the better ones require a Master's?

Master's required, sometimes even PhD. But that depends on the company. My area requires a Master's, PhD preferred.

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Is it true that there aren't many entry-level jobs in data science, and most job openings require a phD or lots of work experience? If so, am I better off getting an entry-level job as a quantitative programmer or statistician/data analyst?

There's grunt work that has to be done, which can be done by just about anyone who is good at Excel/R/SQL/Matlab/Access.

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If I want to live abroad, should I apply for quant/stats/data/etc jobs abroad, or work in the US first to build up experience?

Doesn't really matter from what I've seen. Data is data. I've seen people go both ways. If only US companies will take you, work there first and apply abroad later.

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Since I ultimately want to work as a freelancer/work for myself, is it easier to get into that if I first work in stats/data science as opposed to IT?

Remote work opportunities are more plentiful in IT. Your main shot at freelancing in quant work is becoming a consultant down the road, either for a firm or on your own.

[Peregrine]

(09-19-2014 04:00 PM)polar Wrote:  

People in this field:

What are the top (3-5) specific skills, qualifications, software certificates, etc. that one can pick up to qualify for an entry-level role? Can someone drop a data sheet, particularly with recommendations on materials for self-study?

In order:

Math and/or statistics degree (BA, Masters, and/or PhD) from a reputable university
Excel/SQL/R/Matlab/etc and/or any programming language
Relevant experience in the industry that you want to analyze data in

[kongzi]

Thanks alot for your reply. I have a few more questions

(09-19-2014 07:36 PM)Peregrine Wrote:  

Which STEM field is your Master's in? Math is preferable (for obvious reasons). Physics is second best.

Learn R, SQL, SAS, Matlab, and Excel (and Powerpoint, because you will usually present your data with it). If you're proficient with all/most of those, you should be fine. No personal projects necessary.

So just knowing the basics of those is sufficient? I already know the basics of allt of those except SQL and SAS

I thought for quantitative roles in finance, programming skills is really important, especially C++?

For my Master's project, I'm currently using C++ and although I like math/stats more than programming, it would be nice if my skillset in C++ paid off

Quote:

Remote work opportunities are more plentiful in IT. Your main shot at freelancing in quant work is becoming a consultant down the road, either for a firm or on your own.

And how many years of experience would I need before I can become a consultant?

[Peregrine]

Knowing the basics is not enough. You have to be proficient. Programming skills help in the sense that it shows you can think in code (so your C++ skills are an asset).

~10 years of experience before people will take you seriously as a consultant. Alternatively, you could bounce from contract to contract if you like.

[kongzi]

One more question: Should I study alot on data structures/algorithms? I know the basics but I wouldn't say I know them well enough to pass an interview for programmer/software engineering positions

[Peregrine]

(09-20-2014 04:55 PM)kongzi Wrote:  

One more question: Should I study alot on data structures/algorithms? I know the basics but I wouldn't say I know them well enough to pass an interview for programmer/software engineering positions

The technical interview is not as strenuous as it would probably be for software engineers. The basics should be fine.

[GauchoNomade]

I am considering re-inventing myself in a Data Science career.
It seems growing, well paid and interesting.
Reccommended MSc. programs?

[aguasin]

Something that hasn't been mentioned is that there's also the tech angle of this, being Business Intelligence, Data Warehousing that seems to be booming at the moment. (In the EU, amongst other places)

You'd just need an IT degree and experience with:

BI / DW:
OBIEE, SAP BI
Oracle Database / SAP Hana / Teradata and SQL Server to a lesser degree.
SQL, PL/SQL

Big Data:
Hadoop
Pig / Hive / Python, etc.

Salary range: 40k - 140k Euro, depending on experience.

[cibo]

So I'm a data scientist and have worked for F500s and start ups.

Expanding on Agustin point. The data science field breaks down into:

Data analyst
Data Scientist
Data engineer

This creates a range between technical and business. Analysts have more business knowledge and have to do more client facing work while data engineers have to program more with less business facing. There's also a bit of difference in the tools between tech companies and normal corporations.

That said the common skills you need are:
SQL to get data from databases and is a fairly easy common language used for many of the Big data tools, Hiveql for hadoop, CQL for cassandra, etc. Most of the relational databases (mysql, teradata, orcale, sql server,etc.) are pretty much the same with some differences in functions and syntax here and there. The NoSQL databases have a lot of different types and can be a bit confusing if you don't understand relational databases enough. I'd work on those later after you have a solid grasp of Relational databases since the NoSQL's db's were created in response to the limitations of the traditional database.

Statistical programming language: SAS for corporate, R/python for technology/startups. This need somewhat depends the amount of raw statistical/machine learning work you're doing. Also when you start getting data from websites and apis, these start becoming more important.

Data visualization tool: Excel/VBA for coporate, d3.js for tech, and/or Tableau for any type of firm. You also use the stats tools for chart making.

Understanding how to work with both structured and unstructured data(think text in twitter). F500 Corporations work more with structured while tech work with both. There are endless algorithms for analyzing data. Start simple since you can nearly the same performance as the top of the line algorithms with simple ones. Linear regression, native bayes, k-means cluster, decision trees are more than enough. Maybe already up read up about bag of words with tfdf normalization if you work with text a lot. After that, most of the other algorithms will be based on those ones since it usual breaks down to supervised vs unsupervised learning with mixes in between.

Communicating data in a way that non-technical people can understand. This is not easy sometimes since you just spent 90%-95% of your time coding and analyzing. You might spend like 4%-9% on charts/presentation and like 1% of the time explaining what you did. But those last 5-10% are very important since that's how the client gets all their benefit out of your work. This includes even more technical data sciencists since they have to show the model works right.

A master's/phd in a STEM area also helps to establish credibility.

[GauchoNomade]

A couple of people have told me that although is a booming sector, it does not suit a location independent lifesyle.
Very interested in knowing which avenues allow to consult/freelance Big Data in a location independent way.

[Brodiaga]

(01-28-2015 10:38 AM)GauchoNomade Wrote:  

A couple of people have told me that although is a booming sector, it does not suit a location independent lifesyle.
Very interested in knowing which avenues allow to consult/freelance Big Data in a location independent way.

I have the same question. My understanding is that a typical data scientist is lucky if he can work from home a couple of days a week. Most don't even get that. Whereas for a programmer or a remote system admin, 100% telecommuting jobs are possible even though not easy to find either.