student story

Shubhangi Kharat

Data Scientist (Digital International Automotive Trading Platform)
Data Science Bootcamp, 2022
Diploma Program

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How did you hear about WeCloudData?

I was searching online for the best universities/colleges for Data Science (DS) and came across WeCloudData. There were many good reviews about their program and when I went through the curriculum, it covered all the required skills for DS. A few things that stood out to me while I was registering was the fact that it would be online (so I could complete the program from home) and that Amir, their program advisor, was very helpful during the process. Since I was still thinking about taking a bootcamp, he would reach out to me to let me know whenever a new cohort started.

What was your career background before enrolling in WeCloudData’s Data Science program?

I have a Bachelor’s in Information Technology and worked as a Senior Developer for 7-8 years in India. My transition from working in India to Canada was smooth – I was on a work permit and worked as a Meta Marketing Expert to save money for the courses. In 2021, I got the PR to start the program.

Can you tell us a bit more about your journey on how you got to where you are today?

I worked very hard to pursue my career in DS! My previous role was related to content management systems (CMS), and during that time, I worked with various data teams which piqued my interest in DS. I also knew I wanted to continue my studies after doing a bachelor’s.

During bootcamp:

  • After enrolling in the DS bootcamp, I attended the lectures and liked that I was able to ask questions during class to clarify points if needed. The instructor, Vinny, was very friendly and took on a collaborative approach when teaching us. He often gave examples of how the concepts we were learning related back to the industry and why they were useful.
  • If I needed additional support with assignments/labs/debugging, I reached out to the TAs who were knowledgeable in this domain.

After bootcamp:

  • I wanted to get a job as soon as possible so I started my job search once I graduated from the program. To prepare for it, I revised everything that was taught and did additional research online about industry requirements. For example, whenever I looked at a job description, I would check what skills were needed and learn those to prepare for interviews. I learned many new things like model deployment, Kubernetes, Kubeflow, etc.
  • My career mentor also helped me create my resume (and I got a job within a month!)

Based on your previous education experience, how did online classes compare to learning in-person?

I enjoyed the online classes – the lectures were recorded, which were very useful since I could always go back to review a topic if needed. I also felt that online classes were more productive since I didn’t need to spend as much time commuting, meaning I was less tired and had more energy to study!

A tip to be successful in online classes is to always clarify any doubts you might have. For example, start with rewatching the recorded lectures. If it’s still not clear, I researched the topic on my own to clarify and make sure I understand the material.

How did the real client projects help you when it came to finding a job?

As a data science consultant working on a recommender system project, I helped the client improve their current system, did exploratory data analysis (EDA), updated scripts according to database changes, and worked on model improvements. I also helped users find sponsors through a website (mediating user matches, generating the top 10 recommended sponsors, etc).

Since I worked on this project which mirrored working at a job, I had more confidence going into interviews. I was also asked to talk about the project during the interview and my contributions showed that I had relevant hands-on DS experience.

What was your job search process like?

After graduating from the diploma program, I started my job search (around the middle of Jan 2022). For around one week, I focused on resume building and then started applying to jobs. After 1-2 weeks, around 10-15 recruiters had reached out to me for both data science and data engineering positions. I believe it was a combination of my past experience and real client project experience that made my profile stand out.

I applied to 15-20 jobs during that time and received 5-6 interviews. I ended up going with my first interview/offer.

For my current job, I applied in February and the process took around a month. It was 2-3 rounds of technical interviews (the first one was with the machine learning engineer, and the second one was with the project manager) in which I shared my personal projects and did a codility test.

In terms of career mentorship support, since I got a job relatively quickly, I did not spend much time on the mentorship. However, I received help with building my resume and applied to jobs on Indeed and LinkedIn.

Can you give us some insight into what the Data Science interview process is like?

During my interview, I was asked about my experience and received questions regarding my process for exploratory data analysis (EDA) and machine learning (ML) models (e.g. Methods for improving accuracy, process of cleaning data, modeling methods). I was also asked about production deployment, ETL, and serving models.

Do you have any words of advice for current or future students planning on pursuing a career in Data Science? 

  • Try to learn as much as possible (be curious for more knowledge)
  • Revise materials taught in class
  • Do research on what job descriptions ask for and learn those skills to pass job interviews
    • Although my background definitely helped with this, in general, this is a good tip for furthering your career
    • Mainly prepare with what you’ve learned from the Data Science program
  • Don’t just study the material – further your understanding of why/how it can be applied (e.g. Why choose this model over another? Understand how things work in relation to others – make the connections)
    • Don’t focus on one thing only (try to branch out more as you might get asked to do different tasks) – focus on the job description and learn many algorithms
  • If you’re only interested in ML, try searching for jobs related to that (e.g. Machine learning engineer)
  • Collaborate with other people in the data teams, business communication is a very important skill!
    • Think about how you present your work to clients and try to focus less on the technical terms as they may not come from the same background of knowledge – you want to make sure clients understand why it’s good for their business -> Use easy words/layman’s terms!

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