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My Journey to get a Data Science Job as a Fresher. No Scenes Cut!

Recently, I got offered a job as a Data Scientist at a real estate startup. Getting a data science job is never a straight forward process. Let me uncover for you my story on how I achieved this milestone, and what exactly happens behind the scenes (what a domain name promotion) in one's journey towards being a data scientist. I would not tell you to exactly follow the path I took. There were a lot of good and not-so-good materials I encountered along the way. In the end, I'll brief out an all-good path for you to achieve a data science job much before I did. Till then just hear my story out, it'll be messy, ain't real life a mess?


Let me tell you a bit more about myself. I was enrolled in a bachelor's course majoring in Computer Science from a not-so-reputed university. Yes, I wasn't exposed to any kind of overwhelming tech culture, research facilities even if they exist were in their infancies.

I want to start this by making a concrete point. You might be in a better or worse position than this, but you cannot absolutely blame your study-place or surroundings for the lack of opportunities they provide or give up on your fate just because you are not enrolled in a top-notch university. 

We live in the age of the internet, where one can study, skill-up and learn almost anything by themselves. You have MOOCs for everything, you can get online mentorship at cheaper rates, you have great encouraging online communities. One can just not complain. 

Or maybe let's just get into what we are here for.  

May 2017, the end of my sophomore year when I started looking for a Software Development Internship. I applied at various job boards and ended up getting an internship at a travel startup. I was assigned the task to build a content analysis tool. There I was introduced to concepts like Natural Language Processing and other data analysis tools. The problems we were solving there were very unconventional and I started getting inclined more and more towards data science. By the end of the internship, I made up my mind to further study how problems can be solved using data. 

In my university, I started looking out for data-related electives. I started looking for MOOCs. I wanted to start with a more "applied" course first so that I could get a gist of what I could gain from doing data science. I enrolled in the Coursera's University of Michigan Applied Data Science Course. It was a specialization which included 5 courses namely:
  1. Introduction to Data Science - where I was introduced to various Python data processing frameworks like Pandas and Numpy.
  2. Data Visualisation - where I learned to use Matplotlib and understood some good practices and things to keep in my mind while doing visualization stuff.
  3. Applied Machine Learning Course - here I was introduced to various regression and classification algorithms. It didn't go deep into maths but excellently gave intuitions behind the algorithms we'll be using.
  4. Applied Text Mining - this course had a good bunch of exercises; I learned about LDA for topic modeling and vectorization techniques.
  5. Social Network Analysis - I didn't take up this course as I was already overwhelmed about the above ones and wanted to dig much deeper into them. But I would be doing such a course definitely in the future. 
This was how I gained an overview of what types of problem can be solved using data. Ah, but I kinda do not recommend this course. 

Fast forwarding to Summers, Twenty 18.

A year passed doing MOOCs, college courses and the next summers came and I made up my mind to take a core data science internship this time. I applied for internships, I somehow got shortlisted for some, I started the process by doing some take-home assignments but failed to make any sense out of them. I dropped the plan to do an internship and thought to start afresh, which was to devote my summers only learning. I was fascinated by one fundamental subject in computer science which was "Data Structures & Algorithms". I decided with one of my friends that we'll cover up all the standard concepts of DSA and participate in competitive programming. I loved solving problems on HackerRank and CodeForces. We found an excellent resource for competitive programming which was this CommonLounge Playlist

I might be sounding offtrack as I speak about competitive programming, but I just wanted to point out that DSA has played a very important role along my journey of writing good code, thinking about solving problems "peripherally", in very creative ways. I wouldn't say anyone to get into competitive programming for Data Science, but the knowledge of Data Structures and Algorithms will for sure help you a lot. To the utter co-incidence as I write this, here's a popup in one of my Data Science Whatsapp groups. (I didn't make this up, trust me!)

It is not a mandatory thing, but having this will sharpen your vision about analyzing the complexities and involving good ways to solve problems.

We completed the playlist understanding and solving binary search, sorting, dynamic programming, recursion and graph problems. From that time until today I still participate in online competitions whenever I could. 

Someone said Deep Learning.

It was time when deep learning had gained a lot of hype. On my LinkedIn, I could only see people completing Andrew Ng's Deep Learning.ai course. In no time I got enrolled myself into it, and that's when I went deep into mathematical details behind logistic regression and neural nets' algorithms. That was when things started becoming overwhelming and intriguing at the same time. I completed the first two courses and slowly started to peek what Tushar was studying, Andrew Ng's Stanford Machine Learning course. 

Yes, I messed up. I did deep learning first, went back to study the traditional ML algorithms in depth and understood the math behind it. Somehow, in the end, I could make sense out of everything. 

The Placement Season. 

As I said, I was in a kind of tier-3 college and we do not have big shots, well-paying startups coming here for placement drives. I made up my mind to only sit for a highly technical job where I could somehow make my way to get experience in solving data related problems. One benefit in being from a tier-3 college was to outshine amongst others easily. The reality really was that the competition was not fierce to get a job. If you are actively learning out of the degree curriculum you are far much ahead than your peers. So I was selected in the very first company I sat for. Fidelity International. Had a lovely interview experience. Awesome people. The interview was somewhat inclined towards AI and data science, as that was mentioned the most in my resume. 

I was the one with the lowest GPA (7.2) being selected among the other 9 point rockstars. We had around 6 months time before we could begin with the internship. I was involved in my Final Year project which was creating a device for detecting the freshness of the fruit. We'll have another post to discuss this.

Last Semester. Fidelity Internship. 

The day came, when I was all set to officially get into the corporate culture, that too in an MNC. I don't know why, but I do not have a good feeling for working in an MNC, maybe kicking the core part of the business is not easily doable there. 

So our training started, we were being trained to learn Java, Oracle, and other Java Web Development Frameworks

A nightmare for an aspiring Data Scientist.

I did not like Java much, still, things went on. I did well, solving all the algorithmic problems our instructor gave us. It was project time, wherein interns were allocated some in-house projects. I suggested one of my own, which was to predict stock market trends using tweets from twitter. Research work on this is being done at some places to some extent. We were approved to do this here as it also closely related to the business Fidelity was into. We had a full three week's time. 

I again took another MOOC, which was Andrew Ng's RNN course so that I can understand and apply some state-of-the-art learning algorithms for the NLP task I was going to do. Then came, another nightmare. 

"Do it in Java."

As our training was done in Java, we were to use only Java in whatever project we want to pursue. I seriously don't want to get in how we completed this, but we did. Really, we did meet our targets. Our project display was a success. 

Done with the Training. Jumping into teams.

So we were 2 months into the internship period when we were assigned our respective teams and the actual work began. I knew I will screw up very badly if I was given web development work. I had previously done a lot of web development work, and even being good at it, it made me frustrated. It was not my cup of tea now. I had already told my mentors that even in the worst case put me into Database team but not in Core Development. 

I learned no one cared about what a person was really passionate about. Each one of us was very randomly allocated teams. With all the luck from the entire universe with me, I got an option to choose between the Spring framework or AngularJS. I have no hatred towards any of these frameworks, but it was not something meant for me to do. 

And you don't do what you don't like. 

But as now I was employed and for which I was being paid for, I responsibly did all the stuff I was asked to do, in office time. As soon as I came home, I had my DataCamp, KhanAcademy's Statistics course, and not to forget the Job Portals all up and running. For the next two months, I was leading two lives. A Backend Developer, and a Data Science Jobseeker. Yes, I worked like hell, I got sick, still, I managed and this time I was becoming foundationally strong in Data Science. 

Starting from ZERO. 

Haha!

Somehow I got an interview opportunity with a company named Squad for an analyst internship role. I did well in their phone interview and take-home assignment round. But was rejected in the last round, as the role was a bit non-technical, required more of excel expertise than python knowledge. Still, I felt I still lack somewhere foundationally. 

Oh, I want to make one point here, I seriously don't like the concept of take-home assignments. It works for the employers but it takes a hell lot of valuable time of the candidate.

I took a free membership of DataCamp. I'll let you know in the end how to get one for yourself. I made up my mind to brush up my basic Statistics skills. From probability distributions to densities to hypothesis testing, I was getting good at everything. I took KhanAcademy's course to completely understand the ins-and-outs of Hypothesis Testing. By now, I had gained a lot of confidence and it was the time I actively started looking out for companies seeking freshers for data science job roles. 

Did I say companies seeking FRESHERS? Who hires a fresher for a Data Science Role :/

If it was a year back, I couldn't just make it through, but this time as I said I had become foundationally strong, I know what to solve and how to solve. I started getting REAL interviews. I was now very particular about the products of the company I was applying for. I wanted to get hired by a company with my full satisfaction. Money was not in my mind, but satisfaction was. Sometimes money brings satisfaction too. 

I applied at some Holiday-homes Indian startup. I got a chance to get interviewed there. I met the HR, I was given a bunch of Python questions on HackerRank platform, with Fibonacci Sequence being the toughest amongst them. -_- 

I successfully completed all of those and then met another guy who took my ML interview. It went well in starting, I was talking about my learning in the field of statistics. A bunch of ML questions then. I could answer some and some I couldn't, but there was some negativity I felt from the interviewer's side, he didn't sound professional which made be somewhat less interested in this job. 

I had the other interview with the founder. He was a polite person, he asked me some behavioral question to judge if I was a good fit in their culture. I answered all his questions pretty honestly. He was amused by the fact that I was about to leave my HIGHLY-SECURED-FULL-TIME JOB at Fidelity for a startup. I didn't know it mattered too much for him, but I was just being honest all the times. 

The HR then came, told me that I was SELECTED, and discussed the compensation details with me. I went home telling him that I'll let him know my final decision the next day. The thing was, some people there didn't fit well with my culture. I found negativity in interviewing with some. I let go that offer. 

I gave more and more interviews. At one time I got an offer to Head the Technology of a very early age startup. The guy was amazing, had an amazing idea, but I was not in a position to Head the tech, which had the initial focus in managing all the development related work. I wanted to gain experience in the data domain. We became good connections and I moved on. 

The Day.

Finally, I got matched with a US-Canada based Real-Estate Startup, "Nobbas" at Angel.co. I talked to Aakash, who was the CTO of Nobbas, for a data science opportunity there. The very first thing we discussed was my interest and things I was passionate about. He was interested in hearing my journey and was delighted to know how uncommonly I was not settling for a safer job at Fidelity, but looking for a role I was headstrong about. 

We met for an onsite, at WeWork. WeWork is one lovely coworking place. Just be into one and you'll know what it brings for the professionals. I met Aakash, we talked about ourselves, what we do and how we became who we are. Then, I was given a coding problem to solve. I did it quite fast, wasn't tough, it was just a tick in the box to know if I can code neatly. We then, discussed my projects, a small data knowledge round was conducted. We discussed how we can solve problems at Nobbas using data. I came up with a few solutions, and I was getting more and more comfortable with such a non-traditional interview experience. It was very different than what I had before. 

After that, I met Alex. Alex was another amazing human being. Alex took a stress-test round. He asked some very straightforward questions to which I was only able to answer with my full truthfulness. Some of the questions were as follows: 

What do you do when you can't solve a problem?
How do you relieve stress?
Was there a thing which you found impossible to solve, what did you do then?
What drives you, why are you here, why not at fidelity?
Some algorithmic puzzle question. Just to check how well I think under pressure.

I had experience-rich answers to all of these. I did let him know my true purpose. He was impressed with how I was driven by purpose. I did ask for feedback from him and he happily did so. He gave a good word for me to Aakash. 

And finally, I was hired. 


I really feel that answers to such assessments must be at the utmost importance for any employer to look for in a candidate. We finally discussed the compensation package and I loved the way of how considerate they were for their employee. They offered me a package, taking care of my travel, my parents' worries, and my satisfaction. 

With Nobbas my checklist was like:

A data science role. Check.
Awesome team. Check. 
Working in a startup. Check.
Working out of WeWork Gurugram. WeWork is a happy place for sure. And, check.
Worries. Unchecked. 

All in all, a satisfying offer I gladly, accepted. 

A few are fortunate to get lovely, full-of-learning interview experiences. I cherish all my interviews where there was a lot of positive vibes be it the place where I got rejected. You have to give 2/3rd of your day at the workplace, make sure you will like it, you will like the people, the culture. A cultural fit is a two-way thing. Make sure your team fits with your cultural zone as well. 

I Promised Something. 

At the beginning of this post, I promised that I'll show you a good straight-forward path you can follow to land your first data science job easily. You have seen I have messed up the order, which is the number one reason I took so much of time. I want to make it easier for you and save you from getting into unnecessary and not-so-useful stuff.
I can never spell 'unnecessary' correctly. Can you? 
  1. A statistics course is a must. I will first encourage you to join DataCamp. You can join it for free until two months through Microsoft Visual Studio Benefits. I don't use DataCamp much for other purposes, but I would recommend you to take the "Statistical Inference 1 and 2" from the data analyst track. (Give it a week's time)
  2. Take Khan Academy's Probability and Statistics course. I feel, doing only the Hypothesis Testing part from the playlist will suffice and will make you foundationally strong with Hypothesis Testing. (A week's time is enough for this too)
  3. Pick up the 100-Page-ML-Book. I usually read fewer books. But this is a keeper. The book is available for free here. It is based on read-first-buy-later policy. I bought this, it was worth it. You will get an amazing overview of much of the Machine Learning field with actually not ignoring the mathematical stuff. This book is endorsed by Peter Norvig as well. (Read it along while learning)
  4. Try implementing some ML algorithms on your own. Or take the Andrew Ng's Stanford ML course. You need not do it end to end. Do it topic wise. (Give it 3-4 weeks)
  5. Let's say you want to get in conversation with today's AI Scientists. Take up a Deep Learning course. I found the Udacity's free "Introduction to Deep Learning course with PyTorch" much more intuitive than the other too overwhelming ones. Andrew Ng's course is still one of the best, but I would suggest you take it when you want to go into much more detail, or when you actually are trying to solve a real-world problem and can refer these materials to know the algorithms in and out. (Give it a month's time)
  6. By now you'll be able to read research papers. As a fresher, implement some research papers you like and add it to your portfolio citing them. It will make your employers able to consider you for some position.
  7. Pick up personal projects, jump into Kaggle. This is the time you'll learn along the way. DO NOT STOP LEARNING. 
You'll lead your way from here. For sure. 

Whenever you feel confident enough, start applying for jobs.

Some Pointers for Interviews.

When giving an interview remember the person interviewing you doesn't know you at all. They don't know how hard you have worked for it. You need to tell your story. You need to make them realize that this isn't just another job for you. You are here because you are passionate. You are DRIVEN BY YOUR PASSION. Tell them that you don't have the experience, you have the will. 

Be honest, do not lie. Your employer would love to have an energetic, passionate human being who is driven by what he or she does. 

Ahh. Enough Said. 

I wrote this post in one go. I am really tired. And it's freaking 3:43 AM here. I would be extremely happy if you made your way till here. Thanks for listening out my story. Hope my journey can help you, push you forward in your path, and there'd be no one happier than me if it could bring even a slightest positive change in my reader's mind. 

I'll be kicking off with my job in the second week of next month. I have a lot of things planned ahead of what I would be posting next. It'll be something about "The First Week in a Data Scientist's Job."

See ya.

Comments

  1. Really Informative.Thanks for posting your experience, sharing your story and moreover guiding newbies in the right direction.Though I am not passionate about data science or anything to do with computers,no offence, hearing your story has certainly ignited a spark in me to follow my passion unapologetically.Thanks.

    ReplyDelete
    Replies
    1. Hey Tushar, thank you so much. I’m so happy it has benefited you in some ways. It encourages me to write more for the community. I wish you the best in everything you want to achieve, the future is yours.

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  2. Thank you for sharing this information. It's very helpful and congratulations for the job, I hope that it's everything that you wished for. :)

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    Replies
    1. I’m glad you find it helpful. Thank you so much for the wishes. It means a lot.

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    2. Thank you so much for sharing this information it's very helpful for people like me who wants to pursue career in data science

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  3. Congrats Sahil
    You finally got the work that you wanted.
    All the best

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    Replies
    1. Yes, it feels great to do what you love. Thank you CJ, you have awesome initials. :D

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  4. Wwwwwwow, this is amazing. Thank you for sharing such an amazing and informative journey.

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    Replies
    1. I feel really great when I see such a support. I’m so happy you liked it. Thanks man.

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  5. Thanks for this post, I would like to know what other courses in CSC, in addition to DSA, you think will help ? I understand that a data scientist ideally is not required to know this but often times the projects we do involve building software products.

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    Replies
    1. Hi Raja, apart from DSA I think, one must also understand the methodologies to conduct a project. For example, the Agile methodology. But that, I think can we well taken up when you join the organisation. Also, some focus on DBMS concepts and Discrete Mathematics will help for sure.

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  6. great mate, truly inspirational. cheers for following your passion..

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  7. You should post this on medium too 😊

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    Replies
    1. Hey Daniella, I already did that. Here it is: https://medium.com/behindthescen-es/my-journey-to-get-a-data-science-job-as-a-fresher-no-scenes-cut-f3934ae8321a

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  8. Hey Sahil... Some of the decisions you've made here are incredibly bold and inspirational. Love your passion! Wishing for a bright Data Future for you :)

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    1. - Sushma, your batchmate

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    2. Glad to hear from you, Sushma. These words empower me. Thank you so much for coming by and reading it :D

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  9. I have no idea how I ended up reading this post, the last thing I remember is that I was searching for data science jobs for fresher's and somehow I got here. I feel like this resonates with me a lot. I am in my second last semester of college and recently got rejected by Honeywell, after clearing the first two technical rounds just because my area of interest was data science and they didn't hire freshers for that role and I was adamant about the kind of job profile I wanted.I was criticized a lot for sticking to one domain by my fellow students and teachers alike. I was feeling extremely dejected but reading your story has given me hope and has once again ignited the spark in me to do what I really want and not follow the path that everyone else is taking. Thank you for inspiring me and I wish you a successful tenure at Nobbas, hopefully next year by this time I'll too get a job in the data science domain☺️

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    Replies
    1. This is my favorite comment on the blog so far, love this confidence. Keep going you'll make it for sure. Never forget to believe in yourself; if you believe you can make it, you definitely will make it.

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  10. Hey! Thanks for the great insight, Sahil.
    I'm in your shoes right now and actively looking for data science/analyst roles.

    I'm from the Mechanical Engineering background and would like to know if I should learn conventional coding for coding rounds. I'm thorough with R and machine learning concepts with hand on knowledge. I'm just worried that not having conventional coding knowledge would be a set back for me. Also, do all companies have a coding round before other rounds of the interview process.

    Thanks!

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