Got My Dream Job in Machine Learning!

Got My Dream Job in Machine Learning!
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#1

So, yesterday I finally got my 1st job in coding, and particularly in machine learning! Really excited to share with you guys man! And whole lot of thanks to everyone on FCC! You guys are really awesome!

I think ended up getting a job in the field of machine learning is a quite a different experience from most FCCers, so I hope that sharing these experience with you guys will help you on your decisions making when searching for your dream job in the future, and also open up more options to choose which field to specialise after finishing FCC extensive curriculum. Here is my story and experience to share with you guys.

So I started out on FCC in July last year after dropping out from my university since I couldn’t transfer my degree to CS. It was long long hours of coding back then until December when I started FCC backend projects pair-programming with another FCCer. At that time, she and I were also searching for jobs, and since we lived in the same city, and so we can help out each other in finding jobs. She wanted to get a job on UI, especially CSS and RWD, and me particularly on AngularJS or Node.js, no matter as frontend, backend or fullstack developer.

We got interviewed for a few times in our city. But since the pay for a junior developer in my country is extremely low, she then decided to apply for a working holiday visa to UK, and try to get some progressive experiences on UI designing there. And now she is getting a very good pay and learning some really good designing skills there.

About me, after completing some FCC backend projects, but not all, I started to work on some projects of my own based on my ideas, while improving my AngularJS and Node.js skills progressively. After a few months, I came across an article about machine learning, and was really interested in it and got hooked. After the legendary battle between Lee Sedol and AlphaGo, and realising that the pay for a data scientist is extremely high in my country, and with some prior statistics and algebra knowledges, I started to find the resources and tutorials to teach myself the coding skills needed to be a data scientist. I’m very appreciated to have incorporated the self-teaching skills I’d gained from learning experiences in FCC.

Since I’ve got my coding foundations here in FCC, learning Python to do data science was extremely smooth. While learning data science, I’d joined some hackathons in my city and got to know some really impressive people. Lots of them are from startup accelerator programs. We hanged out sometimes for some coffees and then they brought me to their offices, where lots of startups worked there in a public area. Then I got to know that lots of the startup teams there were hiring. So I bravely talked to lots of the team, and we shared ideas. Very fortunately, one of the team there was looking for someone who can code some machine learning algorithms then deploy it to the backend for production. Knowing that they were hiring, I maintained some meetings weekly with them to tell them what can I do and provide for them. At last, they hired me yesterday!!! It was such a relief!!! Finally!!! This is my dream job!!!

It has been nearly half a year since I left FCC to pursue data science, but this all could happen just because of FCC. Big thanks to all the campers and anywho who’d contributed! Before started out on FCC, I was trampling around in the internet, especially Google to search for tutorials that best fit a beginner. But when I found FCC, I was so excited and instantly knew that I would spent some months on here learning some coding skills.

This is my story, it has been a year since I joined FCC. It was a very long long journey, it’s hard but certainly proved to be worth persisting. I hope this story is motivating and encouraging enough and everyone here can get their dream job in the future.

My advices:

  1. For a non-CS-degree guy like me, getting a job in a typical company is hard. Maybe try interviewing jobs from some smaller startups. If you don’t mind a low pay, try to interview for an intern instead of a full-time position.
  2. Connections are extremely important. Join some hackathons and try to make as much friends as possible in there. You might not know any of their company is hiring! I’ve a friend of friend of mine who now work in Google just 'cause of a good friend he’d known in a hackathon.
  3. Everyone feels not employment ready at some point. After all, just don’t care about it and show the recruiters and employers all the best projects you’ve ever done.
  4. You’ve learnt a lot from FCC curriculum to become a fullstack dev. But if you’re not good at CSS but really good at backend skills like me, you’ll have to be more professional on the backend than as fullstack does. If you’re good at frontend but not backend, you’ll have to be more professional on the frontend than as fullstack does too.
  5. Possibilities are all out there. You might be feeling not as confident when talking with other devs on some dev topics, just be curious and ask. You’ll be improving after all.
  6. Opportunities are not only to be found but can be created too. Don’t be shy to talk about your capable skills. You wouldn’t know that the one who you’re talking to might need you. You’re worth it!

Work hard!!!


Machine Learning Help
#2

Amazing. Congratulations on landing that job ! This is literally my dream as well.

Could you be so kind to talk about your path to become a data scientist, i.e. your curriculum. Also, I am interested in your mathematical background, did you have any priori experience with statistics ? For myself, I am enrolled in a MSc Computational Science programme. We don’t go much into any practical things, like exploring datasets using Python/R, instead we focus on the theory. I’d like to spend my summer vacation on learning more practical things and hopefully landing that data science job after my graduation next year.


#3

Could you be so kind to talk about your path to become a data scientist, i.e. your curriculum.

My path is simple:

  1. Learn Python basics.
  2. Familiarise data manipulation and cleaning using numpy & pandas.
  3. Learn data visualisation using matplotlib.pyplot & seaborn.
  4. Learn a little about data scraping with APIs and also using requests.
  5. Learn SQL language. This is extremely easy compared to mongo shell.
  6. Learn common probability distributions using scipy.stats, and linear algebra using numpy matrix.
  7. Learn ML theories through practising with real-world data using scikit-learn.
  8. Learn a little about neural network and deep learning using whatever libraries.
  9. Try Hadoop MapReduce and Apache Spark.
  10. Participate in Kaggle competitions!

Also, I am interested in your mathematical background, did you have any priori experience with statistics ?

I’ve my grounds on statistics and probability back in university. Just the simple one is enough. I refined these with some MOOCs and cs109 @ Harvard, as stated below.

For myself, I am enrolled in a MSc Computational Science programme. We don’t go much into any practical things, like exploring datasets using Python/R, instead we focus on the theory. I’d like to spend my summer vacation on learning more practical things and hopefully landing that data science job after my graduation next year.

I can recommend some resources for you here:

  1. Dataquest: A place where I started this all. They teach theories through Python codings, i.e. practical.
  2. cs109 @ Harvard: Where I get my solid foundations on. The lecturers and TAs are awesome!
  3. Routinely check out some popular and leading-edge data science blogs, especially FiveThirtyEight.
  4. If you really want to specialise yourself in a particular domain, such as image recognition or stock market prediction, you should search for some papers and seriously read and understand it practically.

Some things about data science that I like:

  1. A data scientist is one who codes better than a statistician, and applying statistics better than a programmer.
  2. ML theories are only justified when you run real-world test data against your ML algorithms after trainings.

FYI: You can PM me anytime if you have any question. I’d be very pleased to help.


#4

Thank you @shian48263 for your thorough answer. It was inspiring. I bookmarked this and will definitely get back to it when I start my data science journey !


#5

Congratulations! I appreciate you taking the time to write such a lengthy post. It’s filled with lots of good information. Good luck with your job.


#6

Thank you for the encouragement. That counts for a lot. :slight_smile:


#7

Thank you so much for writing your experience!
I am planning to take a similar path as what you did, but it seems like it’s going to be a long way since I have to learn both Machine Learning and also back-end web development. Nonetheless, the path seems really rewarding for me.

Mind if you answer some of my questions? If so,

  1. Do you have to write code in javascript to deploy your machine learning algorithm? I am not quite familiar with deploying in back-end since I haven’t touched any back-end yet.
  2. For using database in machine learning, do you learn only mySQL and MongoDB or learn other databases too like MariaDB?
  3. Since your path includes trying Hadoop MapReduce, do you have to learn Java theories like OOP and beyond too? I wrote code in Java before but not much.
  4. Do you participate Kaggle competitions by your own or created a group? I found Kaggle competitions are interesting, but I am not sure if I can do them alone.
  5. Are you planning to learn Natural Language Processing in the future since NLP is heavily associated with Data Science?
  6. How much skill do you think people should have before joining hackatons?
  7. How do you keep in touch with people who you met in hackatons? I like talking with people, but I have no clue on keeping up with my connections.

My apology for my really long list and jumpy questions. I found your path is quite fascinating.