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Data Science Bootcamp

Become a data engineer by learning how to build end-to-end data pipelines

 

Become a data analyst through building hands-on data/business use cases

Become an AI/ML engineer by getting specialized in deep learning, computer vision, NLP, and MLOps

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Improve your data & AI skills through self-paced and instructor-led courses

  • Industry-leading curriculum
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Learn to build impressive data/AI portfolio projects that get you hired

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Build data strategies and solve ML challenges for real clients

Help real clients build BI dashboard and tell data stories

Build end to end data pipelines in the cloud for real clients

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AI/Data Transformations with our customized and proven curriculum

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student story

Ziyang Tian

Data Engineer & Teaching Assistant @ WeCloudData
Data Science Project-Only Program, 2020
Portfolio Course

Data Science Real Project Course

Lead Data Engineer, Beamdata

Free Consultation

Change your career with us

My Background and Motivation for Joining the Client Project Program

For my undergraduate degree, I studied Computer Science (CS). I continued on this path and finished my master’s degree in CS as well, in Dec 2019. The focus of my thesis was on document vectorization, which is related to Natural Language Processing (NLP), and required me to work with Python and Machine Learning very closely. I also did some Python practice on Leetcode and several certification courses on Coursera. Those courses were all about machine learning and deep learning.

After I graduated, I started my job search and realized that work experience is the most important thing, especially during the pandemic. Employers will always want people who can start work right away rather than having someone they need to train from the beginning. Having work experience can prove you don’t need this extra training. WeCloudData had real-client projects and so I decided to join this program to gain this experience.

My Client Project Experience

My overall experience was really nice. I worked on several client projects and practiced my technical skills & communication skills a lot since we needed to communicate with the clients regularly. The technical side of projects focused more on the practical aspects so I always had opportunities to apply the knowledge I learned.

I worked on a Knowledge Graph project for a healthcare startup and a big data project for a media company. I learned and practiced many big data skills – previously my focus was on the machine learning side, but to be a good data scientist, you need to know how to work with big data tools like AWS and Spark. Knowing the technologies/tools in theory is not enough, it’s very different from when you actually have to do it. Regular course work doesn’t work with a large dataset but these client projects do. Those two projects both used Spark so it was really helpful.

You might make lots of errors but there’s lots to learn! Google for solutions. Next time you get that error, you’ll be able to immediately solve it.

I think if I worked on a personal project and got stuck/couldn’t find the solution, I would have just taken a break! But when you’re working on client projects, there are deadlines so you need to get things done on time and force yourself to work on it. Working on client projects can also help you quickly get familiar with the tools and the work experience you gain from this will be very helpful when you start job searching. This experience can really help you learn things quickly.

My Job Search Experience and Challenges

Before attending bootcamp, I had a very tough time. The most frustrating thing was the number of interviews I got and the range of jobs I could apply for – I could only apply for machine learning or NLP-related jobs. Since I also didn’t have work experience, it was very hard. I even planned to apply for some other titles like software developer or front/back-end developers. However, because I spent the past few years studying machine learning, I didn’t want to find an unrelated job.

The second thing was if I wanted to apply to those jobs, there would be new things I would need to learn – it would be difficult to catch up in a short time. But for data science, it’s closely related to machine learning. The only difference is the need for big data skills and SQL skills (but SQL I learned in my undergrad). Here (at WeCloudData) they also provided the real-client project experience, so I was very excited about that.

Before joining the program, I applied for around 20-30 jobs and I got 1-2 interviews but they weren’t machine learning related – it was for software developer positions.

In the program, I worked on the projects for about two months and then started my job search again. Every week I applied for 10 jobs. At that time I did still need to work on client projects so if I had time, I worked on my job search (I would open LinkedIn and Indeed as often as I could and type in the keywords to find job postings). I got more than 10 interviews! There was even a period of time where I had one interview every week.

Suggestions for Those Interested in Client Project Program

In my opinion, this program is very worth attending! After the program, you must work on the client projects. You will encounter many technical challenges but you will learn how to solve problems (gain problem-solving skills). You will practice this a lot. You will also need to learn how to use Google strategically.

Related Courses

Portfolio Course

Data Science Real Project Course

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