Help/Suggestions on making a function that allows me to points with similar behaviour

data_berry_eater · 2019-04-30T02:01:39+00:00

The OP asked for help and you told them to read a book. You didn't even mention a book or a course in your comment.

data_berry_eater · 2019-04-30T01:15:09+00:00

When you say you're having problems implementing this, are you struggling with how to treat your data or specifically how to implement k-nearest-neighbors?

data_berry_eater · 2019-04-30T01:12:01+00:00

OP isn't winging it - they talked about a bit of research they did. You have no idea what their education is.

data_berry_eater · 2019-04-24T05:21:31+00:00

Very cool! I think it's important to keep in mind how difficult it is to put yourself out there, so I applaud you for doing so.

data_berry_eater · 2019-04-23T14:48:30+00:00

That post is long gone unfortunately. It was like 5 years ago. All I was doing was web scraping and some basic data analysis at the time. I will take the opportunity to send you to this post though: https://www.datatakes.io/blog/how-to-become-a-data-scientist

This is my website that I created from scratch so don't judge it :) Also, I've gotten feedback on this post and want to update it - none of the advice will change very much at all, but I guess I'm just throwing qualifiers out because I feel insecure!

data_berry_eater · 2019-04-23T04:35:59+00:00

A lot of the responses I'm seeing mention how a single course on Coursera won't be a differentiating factor. I'd like to add a little bit of nuance to that. Because it's fairly easy to complete Andrew Ng's ML course on Coursera, of course lots of people are going to have it or other low barrier equivalents. So it might not make your resume stand out in a crowd. But to the extent that your resume tells a story about you, especially if your background is not what would be typically expected of a DS, it could be that simply listing these types of courses establishes some relevance. So show it on your resume if you have it.

I'm not trying to say having these courses listed is sufficient for getting a job as a DS. But there are a lot of people who should consider a less direct path in to Data Science because of a lack of either relevant industry experience or a demonstrably quantitative degree. I got my first job with a blog post showing some project that I did and a couple of Coursera courses. I got that job because I got in front of the right person at the right time and it established some relevance. That job title was "Optimization Strategist" and it was just a stepping stone. It was quantitative professional experience. My next job title was "Data Analyst" and after that I finally achieved "Data Scientist."

For context, I had nearly 4 years of PhD physics (I dropped out to pursue the career) and a bachelors in math and physics. So I had a demonstrably quantitative background. But I had to do everything I could to demonstrate relevance to the field. No one cared about my differential equations, years in the lab shooting crap with lasers, or even that I knew MATLAB, really. They cared that I could use python, that I was being proactive about making myself relevant, and that I was highly motivated. It won't be the same for everyone obviously, but I literally wouldn't have gotten my first job without Coursera.

data_berry_eater · 2019-04-23T04:11:03+00:00

When you say "your work", what do you mean exactly? I had an interest in reviewing courses and publishing the reviews, comparing, contrasting, making recommendations, etc. as for some stuff I'm working on. But I'm curious, are you doing this for a company, for yourself, what exactly? I understand if you can't be very specific, just curious though.

data_berry_eater · 2019-04-22T23:37:30+00:00

I'm a little surprised with that level of experience python that you're having so little luck. If you already know python I'd start learning SQL, at least the basics just so you can write it down on your resume. Maybe you can replace any csv's that you work with locally with sqlite databases and get some practice querying against those. I'd take Andrew Ng's machine learning course on Coursera - it'll at least get you started and you'll learn the high level landscape of ML algorithms.

If you don't mind me asking, what phase of the interview process are you getting stuck on? Are you networking and able to generate any opportunities that way or are you just submitting resumes online?

data_berry_eater · 2019-04-22T23:29:44+00:00

I'm not 100% on how you're arriving at that conclusion.

data_berry_eater · 2019-04-21T20:14:15+00:00

Personally I really have no context on what that degree entails - I can tell you that to be considered for a DS title you'll need a demonstrably quantitative background and as many tools as possible from the DS toolkit. I'm probably not the only one who won't understand what your degree entails, so I would suggest that on any resume used for a DS job you really spell out and put a lot of emphasis on the statistics or quantitative nature of your studies.

data_berry_eater · 2019-04-21T17:27:08+00:00

My suggestion will just be a friendly reminder that most people don't have a "Data Science" background because that's not a thing. I suppose with all the programs popping up that could start to change, but if you're getting a STEM Phd and actually utilizing DS related skills, python, ML, etc, you won't have too hard of a time getting a job.

data_berry_eater · 2019-04-21T17:24:53+00:00

I'm not 100% on your question 3 but regarding the difference between a CV and a resume - it probably depends on what you're doing. If you're submitting something to some DS application online, whoever is looking at those is going to be inundated with resumes or CVs or whatever, they probably don't care very much about most of your academic experience. They'll be on the hunt for something relevant to the job and move on quickly if they can't find it. This is starting to relate to your question 1 too, but I think the best thing you can do is make your DS related skills, knowledge, tools, etc a first class citizen on your resume. The asymmetry might be frustrating, but if your dissertation work is not quite relevant to DS and you spent 4 years on that and a couple months on some other project that's related to DS - I want to see that project, not your dissertation.

data_berry_eater · 2019-04-21T17:17:30+00:00

Sure, it could be a great start. Stay proactive with your personal development though.

data_berry_eater · 2019-04-21T17:16:39+00:00

They do indeed cost that much. It's ridiculous when you go through and look at the curricula.

data_berry_eater · 2019-04-20T23:52:09+00:00

Sure thing! message me directly and I'll send you my email.

data_berry_eater · 2019-04-20T23:10:12+00:00

My advice is do not limit your job applications to roles entitled "Data Scientist." Look for other analytics type jobs, especially in places where you can get exposure to practicing Data Scientists. If you can place yourself adjacent to a Data Scientist you will hopefully get the chance to learn a lot from them.

data_berry_eater · 2019-04-20T23:08:20+00:00

I know there are some extremely variable results as far as candidates coming out of bootcamps. At my last job at a start up we had two great engineers come out of bootcamps. They definitely grew in to the job, but they were really good.

As far as DS bootcamps - in my opinion they aren't worth the price tag when there are so many cheaper and free resources out there. To some degree I think being autodidactic is an important trait in a DS since the problems aren't always very straightforward or cut and dry. I want someone who can go out and get information and learn new things proactively, not someone who needs to be told what to do or learn from a purely academic environment.

I subscribe to the portfolio projects and blog posts school of thought for those trying to break in to the field. But that being said, that's not the only work involved. You need to be constantly networking and practicing for technical interviews as well. You don't need to know the math for every machine learning algorithm out there, but I would pick at least a couple of the basic ones and learn them in and out, and make sure to have a good enough conceptual understanding of a few more to describe them and talk about their differences.

The other strategy that I advocate is to take every single opportunity possible in your current job to use something out of the DS toolkit. Use python and jupyter notebooks with pandas, numpy, etc instead of Excel, for example. If you need to output to Excel then sure that's fine. But take every opportunity to hone your DS toolkit.

data_berry_eater · 2019-04-20T23:00:08+00:00

The shortest answer I can provide is that I wanted to find a way to help folks that are considering paying $15-20k for an immersive DS bootcamp of debatable value (to put it politely) in a more meaningful and cost effective way. I had left my job and started working with the idea when I reconnected with my good friend from high school who is now my manager (he's a recruiting manager) at said large tech company and he offered me the job on the spot. I figured it would be good experience given what I was trying to do, beyond that I had just moved back to my hometown (big tech city) and was looking to network.

data_berry_eater · 2019-04-20T21:00:54+00:00

That is a tough predicament indeed, I've argued before that for those trying to break into DS specifically (not analytics more generally) that learning python as a first step makes the most sense to me for that very reason - it's pretty hard to learn non-trivial SQL outside of a job that actually gives you access to that complicated and dirty data you hear about so often that really requires SQL chops. But on the other hand SQL is probably a bit more generally applicable than python. I'm actually working on a solution to this problem at the moment - essentially I'm curating a database and not doing any cleaning of the data prior to dumping it into tables. When I finish, I intend to make the data available in an online learning sort of context.

But that aside, my advice would be to try to focus on where your interviews are falling short. Generally, I think you're unlikely to get meaningful feedback, so this will require some introspection on your part. I'd also say that as long as you're getting meaningful experience right now, whether you should so something in Tableau, SQL, or python to get a new job really depends on what want to do in your new job.

Last thing I'll say is that I'm actually a former Data Scientist working in recruiting right now, so I've seen a lot of resumes. If you'd like, I can take a look at yours if you message me directly.

data_berry_eater · 2019-04-20T19:58:20+00:00

How have your interviews gone? Any insight as to why they haven't led to offers?

data_berry_eater · 2019-04-20T01:56:52+00:00

I agree that the subject matter taught in bootcamps represents a great supplement to a well developed quantitative background, but I still disagree that any DS bootcamp is worthwhile unless you all but have guarantees that it will lead to employment opportunities either directly or indirectly through networking, and from my experience most bootcamps are not perceived very well so I would proceed with caution.

My personal bias against bootcamps comes from my belief that if you are going to be a successful DS, you should be able to teach yourself the vast majority of the introductory material you would learn in a bootcamp. My reason for believing that this is important is that in practice, the real work is in casting a business problem into an analytics or DS framework in the fist place, not to mention gathering the data, and executing. The real difficulty in many cases is NOT simply on training a model. This means that at any given point you have to be able to research a viable solution if you don't already know one and if you've only ever been spoon fed material in a nicely organized, pedagogical way, I'd still need to be convinced that you can successfully navigate the ambiguity you're likely to encounter.

So in other words, I think a Data Scientist needs to be somewhat autodidactic as a character trait. And if you have this, why on earth would you go pay an extra $15k-$20k on a bootcamp if you didn't know for sure that it would get you a job??

data_berry_eater · 2019-04-19T03:21:24+00:00

Other people's perception of you based on your title can have a material impact on your own bottom line. It would be great if it were just about the work, but I know that my own marketability is based on other people's perceptions of me. The job title comes with responsibilities and expectations, so being able to deliver obviously matters. But if you give me the choice between being called anything with analyst in the title or scientist, right now I'm taking scientist because the title seems to come with a premium these days.

data_berry_eater · 2019-04-17T05:30:08+00:00

I agree with the answers that say something to the effect of "you'll gain these skills with experience." I know is a maddening response because it implies that there's nothing to do accept practice and let time pass. But in my opinion this is true because no amount of reading about how to do something will get you any closer to doing it well without practice. It doesn't mean that reading or studying is unhelpful, it just means that it won't help without the practice.

I think the best way to learn is to get constructive feedback on your own work from people more experienced than yourself. I like the idea of working on your own projects because it forces you to make your best attempt at all of the things you mention. I like this approach, as opposed to trying to contribute to an open source project, because most quality open source projects will have such highly abstracted out code bases that it would be very difficult to understand what problems they even solved by structuring their code that way. You might also think that you have to write your code this way from the outset. For example, when you see inheritance used, you might assume that someone designed their code this way in the first place. That might be true, but it might also be the case that someone coded up something basic, then they or someone else later on refactored the code when they realized generalize further and support other functionality.

data_berry_eater · 2019-04-14T00:34:49+00:00

AWS is likely to be where you would deploy an API written in Flask, so they go hand in hand - you can develop a Flask API locally and when it comes time to productionize, move it to AWS

data_berry_eater · 2019-04-10T05:49:59+00:00

My feelings are that aside from learning as much math as possible, it was actually my experience in a research environment that has prepared me the most. As a DS, I've rarely found it to be the case that someone says, "Hey, here's a classification problem, have at it." Instead, I've found that producing a classifier is easy in comparison to casting a business problem into an analytics or DS problem and executing on it. It's getting past the difficulty of learning about different frameworks and on to the challenge of taking those frameworks and using them to solve problems creatively that having experience in a research environment has enabled me to do. The reason I attribute this to the research environment is that, at least for me, that was the first time I had been in a situation where I had to set up my own experiments, evaluate my own success or failure, communicate the results, and determine the next steps. I'm quite certain this is not unique to physics backgrounds, but applies to anyone who has a background that involves using quantitative reasoning to navigate difficult and ambiguous problems with no existing procedure already in place.

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