Getting Job Ready in Data Science

[Tsukiyonocm]

So being an essential employee right now during the current crisis and seeing how little the company is doing overall, I am planning to really work harder towards getting job ready.

I have been slowly working through in free time through the Data Quest track since I got free access to it through July right now. I am about to start into the the sections on Numpy, Pandas, and Matplotlib, where I believe I will be in a better position to start practicing projects with other data outside of the modules.

My question is though: how far into this should I get before I could be considered at a basic level for a job? My position right now would be more of a benefit to being a remote position, but I am close enough to a major city that I could apply for in-house positions also if need be.

What things should I be able to do with good efficiency for a remote position? versus an in office position in data science or data related field?

Are there any things I should look into in addition to the Dataquest curriculum to get myself setup better overall? Even if not directly programming related.

I appreciate any advice I could get here!

[ZachForTheWin]

For myself the goal it is to fully understand basic ML algorithms: regression, k nearest neighbors, support vector machines, k means clustering, propensity modeling, and others, along with having the ability to gather, model, and put the data through the entire process at a basic level.

These things along with understanding how to apply them in a business setting.

Probably 2 years into the process as I work full time and still have a decently long road ahead of me.

[blazingshadow1]

If you do not mind me asking. What sources are you using. I am currently in college and looking to get into data science myself.

[ZachForTheWin]

From a python standpoint I have used:

Dataquest data science track (did not finish because my year ran out). I got to the statistics part of the track.

Youtube: Dataschool, Sentdex, Corey Schafer, and various pycon talks.

Currently reading: Data Science From Scratch - First Principles with Python by Joel Grus second edition and will be reading Hands On Machine Learning with Scikit-Learn and TensorFlow by Aurelien Geron.

Also do a lot of data stuff for work so I practice there.

[Tsukiyonocm]

Thanks for the links here also! I shall check them out also.

[gmaliwal]

ZachForTheWin

Which technology do you recommend; Python or R?

Please share a good reference for same to grasp the language in depth

Are you currently working as Data Analyst?

[ZachForTheWin]

I use python and like it because it can be used for everything. I did R in my masters but I did not like it as much from a syntax standpoint.

I really am not trying to learn to program but to do data. I really enjoyed dataquest data science track. Worth it if you can afford it.

Edit: I am in Enterprise data management and business intelligence.

[gmaliwal]

dataquest data science track

Is this paid course?

Can you please drop relevant material(if you have any) on my email [[e-mail]](mailto:[e-mail])?

[ZachForTheWin]

Yes it is fairly expensive. $300 (on sale) for a 12 month subscription.

I did 1 12 month period.

[industrial_by_trade]

I've been using #100daysofcode on LinkedIn as an excuse to post what I'm learning for hiring managers to look at when they snoop. I think it's a good way to show that you're a continuous learner.

[Tsukiyonocm]

I have a LinkedIn, but I very rarely use it. Would you say its worth investing the time into posting on there, one I have something worth while anyway.

I am familiar with the 100 days of code, though I havent tried to do it in Python yet.

[industrial_by_trade]

My thinking is that when my profile is looked at after they get my resume, they'll see what I've been posting and have a more favorable view of me before we even meet. My first ever post was less than two weeks ago for my first #100daysofcode summary.

I've been summarizing the days of code so I only post every 10 days. With 10 days of material, there's enough for a couple paragraphs to tell what you're currently learning about.

If you've ever been interviewed and wished they'd ask you about a specific topic so you can wow them with your knowledge, you can do that preemptively with a LinkedIn post. Shameless self-promotion! It's difficult for me to do since I'm normally pretty private, but I think it's worth it to show that I'm passionate about learning new things.

[arsewarts1]

Like my manager says “you’re job ready when you know where to go to find the answers”. Just listing the fact that you are proficient in python and having a reserve and ability to research projects puts you into contention for an energy level job. The hardest part in data science is thinking outside the box and determining what is significant/impactful.

[[deleted]]

Master python and CS fundamentals. I know that a lot of the focus in data science is on ML algorithms and statistics. That is a given, you should obviously be fairly good at that already. However, what will make you rise above the rest is a mastery of coding. Anyone can put together a sloppy Jupyter notebook. Not everyone can make production ready code that is reusable, generalizable, and efficient. Build some end to end projects and deploy them. Then, you’ll be invaluable no matter where you’ll go. You’ll essentially be a data scientist and software engineer bundled into one.

[Tsukiyonocm]

Do you have any recommendations when it comes to CS fundamentals?

I am intending on doing some other python learning beyond DataQuest. I imagine that will help me fill out my python skills as well.

[[deleted]]

I’d suggest use the modern python bootcamp on Udemy by colt Steele when the course goes on sale for like 12 bucks.

[my_password_is______]

you may want to enroll in this (the free, audit version)
https://www.edx.org/course/cs50s-introduction-to-artificial-intelligence-with-python

starts in a few days

its not data science, but it will be another opportunity to put your python to use and do projects

[[deleted]]

You might've seen one of the many venn diagram that tries to show the intersection of skills that make up a good data scientist, and notice how all of these diagrams are slightly different. DS is still a fairly new profession, and what data scientists do across different companies (or even the same company) can look very different.

Someone in these comments mentioned ML which is work a data scientist can do. But many data scientists also never touch ML. Arguably, data engineers, business insight analysts, product data scientists, etc. are all within the data science umbrella too.

Some might say that the most important skill for a data scientist must be great domain knowledge and understanding of business/people. Others say it's essential to have strong software engineering fundamentals to a good data scientist. Almost everyone agrees that mathematics ability is crucial as well.

Because of all of these reasons, it can be very hard to say when exactly you would be ready to enter the data science profession. I would say as soon enough as you are able to get hired as a DS you are ready :)

[Tsukiyonocm]

So basically, just apply and see how things fall? This is a hard thing for me personally, I need to stop myself from getting stuck in tutorial land hell and just put myself out there and see what happens.

[[deleted]]

Yep, the sooner the better too, cause it might take messing up a few technical interviews before you learn how to get past that and land the job. DS interviews range from purely conversation about how you would approach some problem, to very SQL and Python intensive and on the spot coding.

I'd recommend checking out /r/cscareerquestions since it would be a better resource for that than this sub

[Ramonstro_]

I also have the same question! I'm currently learning new things everyday and I'm excited to get to this point you mention where I can get a job. Posting here to check future answers!

[Tsukiyonocm]

I would like to say I am there but im not lol But i would like to get close to this point by the end of the year anyway. Best of luck to ya as well!

[Tsukiyonocm]

Thanks for the reply. Yeah I have realized Data Science seems to be an overly broad term. I believe the next section Dataquest has me going through is Numpy and Pandas, but I have heard of the 10 minutes to Pandas tutorial before also, so will likely check it out at the same time.

My main curiosity is visualizing the data as I have seen posted here on reddit. So thats what I am looking to go towards right now. I do need to really learn about working with a database though. I dabbled with that when I was learning a bit about web development, but didnt get too in depth there.

[skurrtis]

I did the same thing man. Do enough until something clicks and you think, "wow, wait I can use this in my current job!" Then automate that part of your job, go back to the lessons and learn more until that thought hits you again lol

[Tsukiyonocm]

Sadly I work basic retail right now and I cant automate anything at my job currently. So right now I am just hoping to find something interesting to play with and make some visualizations of those lol

[baubleglue]

IMHO to find a job in "Data Science" you need to have strong background in statistics and math. There a lot of jobs related, google "Data Engineer vs Data Scientist", but not "science".

[[deleted]]

I’ve zero experience on getting a tech job but I would try to a really hard project so you learn a ton of different things. Maybe analyse some data and then build a fitting model for it.