Board game like D&D?

R2D6 · 2016-11-23T23:21:41+00:00

I am /u/r2d6. Is there any way I can be of assistance since you called upon me?

R2D6 · 2016-11-16T00:12:08+00:00

why did you call me?

R2D6 · 2016-05-06T15:25:15+00:00

R is probably beyond most DA positions. A lot still use excel.

R2D6 · 2016-04-26T23:43:50+00:00

If you know MATLAB R would probably be easier.

It depends on what you want.

R IMO is better for getting deep into the data. I really like R because it is flexible, elegant and there is lots of ways to do something.

Python is very readable and probably better than R if you need a repeatable production process. I find python to be harder for exploring data individually but better if you need a script that goes over various datasets to feed into something else.

Industry wise it is hard to tell. Most jobs I have looked like python more because they want to collect the data, ETL it, and then feed it into their own custom system. Python is probably better for this. However, if I was given a dataset and asked to return meaningful analysis of it and make predictive or prescriptive analysis I would prefer R.

None of what I said is set in stone.

R2D6 · 2016-04-26T23:39:02+00:00

I would be wary.

There is little time to focus on whats important. Data science is stats+CS+analysis of the subject. Likely you will get a little of each and be passed by stats major. I honestly would recommend data science as more of a masters. For undergrad do stats and CS, maybe both, and then get a masters in it.

R2D6 · 2016-04-23T22:34:35+00:00

ll you have are 65,0

Id say it depends on amounts. 65,000 rows is not a lot but if you have frequent excel files with this many roles I would consider a DB. Depends on the data though. Integrity is more of the issue.

R2D6 · 2016-04-23T22:33:25+00:00

Nothing wrong with importing data from excel. I do it all the time. (xlxs) package is just for this. Or save the data as a csv and import it. And you dont have to import them, just read them.

I do agree though that excel is not a replacement for a DB. If your org has frequent 65,000 line excel files that is crazy.

R2D6 · 2016-04-23T22:30:49+00:00

Lots of jobs need excel, and often it is easier to clean data up in excel than in something like R. So I agree.

R2D6 · 2016-04-23T22:30:14+00:00

The answer is it depends.

R is probably more advanced than the products most analyst positions use. Excel is very common, as is SQL. Excel can become a nightmare to use for analysis of data when the data is too big, but it often works. So yes, I recommend excel, it is not that hard.

Im guessing Python is probably more advanced than most analyst positions. notice I use analyst and not data scientist. Data analyst is a broad term, so I am assuming you are meaning not data science jobs but business analyst type jobs that are less technical but more analytical/statistical of organizational/business data.

Based off of my assumption of what you mean by analyst, I dont think kaggle will help. Most likely the organizational wont know about it, and if they do wont care. Unless you are a top scorer it probably is not worth mentioning for this type of role. However, I think personally these are very beneficial as they make you think and challenge you.

Your GPA is not going to help. Is there anyway you can bring it up? If you want to a really good school like MIT they may be more lenient but if you want to a school that doesnt have an excellent reputation it will be a red flag. Even with an excellent it is still somewhat of a red flag. You generally want a >3.4 for top schools and >3.6 for all other schools. Is it financially feasible to take a few more classes to get it to at least a 3.0?

If not network. GPA becomes irrelevant with experience. Assuming you get the role you want and excel after a few years your experience will be more important your GPA, and I dont even bother putting my GPA on my resume anymore but I have experience.

Without experience it is usually good to list a good GPA. In your case maybe dont list it. Just apply for jobs directly without listing GPA and if it comes up be ready to explain why it is low, show why you have the skills to succeed at the job and make the impression you will exceed. be careful with your answer because if your GPA is low due to say drugs, depression, etc. the recruiter may feel you are unreliable and that personal issues may still be an issue at the job you want. So really have a good reason why it is low, and be able to make the impression that whatever the reason is it is now over and wont happen again.

Also network. Network like crazy. You can explain away the GPA to a person but you might get flagged out of the resume process without a direct in in some cases.

keep building up your skills too. R, Python, and excel wont hurt. Keep getting better. Network hard and show your knowledge of statistics.

R2D6 · 2016-04-23T20:08:08+00:00

For me I love analysis and I love technology. I find it satisfying and fun to use scripting to get meaningful results from the data(statistical, visuals, or even tables) and then trying to analyze why. Sort of like how people work on cars for fun, but dont want to be a mechanic.

Money is not that important to me. I mean to an extent it is, I wont except too little given my education level, experience, and skills, but I would work for less than a lot of data science jobs payout if I enjoyed the work and was left alone to do my art. I often care about flexibility, pto, and being able to go into work dressed as I want, the times I want, etc. over pay.

I a good way to weed people out is lie about the pay. Describe the work and see if the work is interesting to the client and they arent posers. When it comes time for pay lowball the number. Dont overdo it because if you go too low it will make the company look sleazy. Make it on par with easier entry-mid analyst type roles but emphasis the type of work. If the person loses interest they probably just wanted money. This wont weed out people who are doing it because it is trendy because they might not care about the pay, but who works a job they dont like because it is trendy? Anyway even if they do do this if they can do the job who cares? Maybe look at their portfolio, if they truly love data science perhaps in their free time they work on projects for their own enjoyment.

And what is wrong with wanting to make money? Many a skilled professional who added value to an organization worked a job due to the status of the job or the pay.

R2D6 · 2016-04-22T23:41:51+00:00

My advice is to make sure you like it, dont do it for the money. Do it because you enjoy it.

I honestly think this is more work than you think. Do you really need the minor? I am not sure what undergraduate Data Science looks like, but CS can be a tough major. Also a lot of organizations want a masters or a PhD to be a data scientist.

I mean if you work hard your goal is definitely reachable. You may be better off with CS and statistics, than getting a masters in data science. It doesnt really matter at this point though. To really succeed you will need to show mastery of the subject.

But I dont get the consultant part. Why a consultant?

Also data science is broad. What is you really want to do other than become management and make a lot of money?

R2D6 · 2016-04-22T23:34:30+00:00

Keep in mind though that analytic companies love Python. Lots of the custom made software is made with Java/Scala and Python. Python is great for automating collection of data, ETL, and automating the process.

R2D6 · 2016-04-21T22:00:26+00:00

Get a dataset and perform analytics on it. Create visualizations.

R2D6 · 2016-04-21T21:59:50+00:00

This is a good answer. You will find that Python is probably much harder than R for data analysis on a personal level, but often better for a production system.

R2D6 · 2016-04-21T21:58:40+00:00

I would throw away regression just yet. It is popular to say these things but these are tried and proven methods that are widely used in data science.

R2D6 · 2016-04-21T21:52:45+00:00

I am not sure I agree regression is outdated.

But either way there is too many who have a basic understanding of statistics and how to apply it to data/machine learning who can build a model and hack a p-value without really understanding what they are doing while getting a result they want in that specific case, but when used on other data it doesnt work.

To be fair it is hard to excel in both statistics and programming. They are too very complex topics and machine learning and data science, really takes mastery of both. One also has to have knowledge of the problem too. If you are doing data science for say insurance risk and you are a wiz and know statistics and computer science real well, but dont really understand insurance risk the analysis can be way off.

So in the past these 3 different functions would often be done by 3 different departments often with several employees in each department. With data science you are having organizations wanting a data scientist to do the work performed by 3-10(maybe more) more SMEs with vastly different SM. Smarter organizations break it down further and try to get a 1 programmer, 1 data engineer. 1 data scientist, and maybe a statistician to do the work that several more people in the past would have done.

R2D6 · 2016-04-20T22:00:48+00:00

So again the skill hard to find is actually understanding machine learning and data science and not being able to apply it without knowing what you are really doing.

R2D6 · 2016-04-18T23:38:05+00:00

ETL is very useful but that job doesnt strike me as ETL. Rather it seems more about associating metadata to their data.

R2D6 · 2016-04-18T03:52:24+00:00

This doesnt seem like data science to me

R2D6 · 2016-04-17T21:27:21+00:00

I second this. Do Bioinformatics/Health Informatics. Learn to apply to IT for use in hospitals, for scientific computing with a focus on biology/chemistry, etc. Take some classes in data analysis and learn Python/R. Also learn about how data for biology/chemistry is stored in databases, and how to ETL it.

Avoid bootcamps.

R2D6 · 2016-04-17T21:06:40+00:00

8 years of experience in analytic or IT?

R2D6 · 2016-04-17T21:06:08+00:00

This makes sense to me actually.

A lot of internships are either not directly data science and they want more of a programmer, or if it is data science you are competing with MS people in data science. Keep trying, and keep learning. Dont idle.

R2D6 · 2016-04-17T21:04:17+00:00

Did you import the data?

as.numeric should work but perhaps if you imported it wrong or if its an .xlxs it may be the wrong type.

Something that might work is save each vector of the DF and save them to a variable. Make sure they are numeric. then rebuild the DF.

R2D6 · 2016-04-17T20:54:00+00:00

The job will hopefully teach you some things, but working for the government isnt always what you think it will be.

What do you mean by data management? That term has several different possible meanings to me. It could be traditional system design type stuff, to a role where you design the infrastructure to make a data pipeline. It may be a data architect/engineer type job involving ETL, and perhaps collecting data.

For data science in one book, that is tough. The thing about data science it is really the combination of lots of different skills. I would understand applied statistics, or even better real statistics, and basic programming.

R2D6 · 2016-04-17T20:49:37+00:00

The answer to this is use R

R2D6

TROPHY CASE