The blog is posted by WeCloudData’s full-time data science diploma program student Yining Zhuang.
In this blog, I would like to share my experience with people who are thinking of changing their career path from business to data science. I hope my journey can encourage people who are struggling in their current position, and help steer them in the right direction towards achieving your goals.
Before I Start
In 2017 I decided to pursue an MBA degree at Brock University with a specialization in Business Analytics, but meanwhile, I worked as an accountant in an accounting firm. During that time, I kept asking myself the same question: is this what I want to do for the rest of my life? I knew accounting was not my niche and that I would never be an expert in the field, and the thought of this was a source of constant frustration for me.
Due to the nature of my work, I had to deal with vast amount of data, but I came to realize that we were handling the data in a very inefficient way. At about the same time, my interaction with some of my software engineer friends raised my interest in learning how to program, and I got a chance to explore the concept of data science.
While searching for new opportunities, I discovered the main responsibility (oversimplified) of data scientists to be working with a large amount of data and extracting analytical insights. In addition, data scientists are required to communicate their findings with the stakeholders, in order for companies to be able to benefit from making the decisions to drive their business growth and profitability. I concluded that this would be a perfect role for me as it would allow me to combine my business background with technical skills, which seemed very exciting. After several weeks of consideration, I quit my job and started my data science journey at WeCloudData, and I would say this was truly the best choice I ever made.
About the Program
Before I joined this program, I was told by the program advisor and instructors that this is a very intensive program and that I would need to immerse myself completely into learning. And they were completely right! The program covers a lot material since studying Data Science means that there is a wide range of skills that one must master and that are required for different industries. It was impossible for me to acquire all the skills and algorithms by merely googling them. But at WCD, I found the courses to be well structured, and the instructors and teaching assistants to be always available to assist us with any questions; but most importantly, during this program, they made sure to teach us the skills that are in demand in the job market. The courses start from the basics and walk you through key machine learning models that are used in the real world. I learned a lot of tools in coding, pipeline, and analytics, as well as algorithms and other skills by practicing and carrying out various projects.
Once the knowledge foundation is built, the best way to accelerate learning is by completing some serious projects. The advantage of projects is that they are designed to allow you to learn new skills while continuing to practice the previous ones. For example, one project might require tools such as SQL for joining tables, Python for data cleaning, and Machine Learning algorithms for models in order to solve the questions. Similarly, some of the projects require data scientists to solve business problems, which means you need to comprehend what kind of business the company runs and how the process works; which is why having an understanding of business is the advantage that would certainly distinguish one from others.
Challenge
Although my experience at WeCloudData was very positive, I did have some challenges with sections in AWS learning that even made me want to give up at the time. If you’ve already encountered similar technologies, such as Linux or cloud services, AWS may be easier for you to learn. However, for those people who have had no prior experience such as me, it can be very challenging. Originally, I thought that machine learning and analytical skills would be enough to launch a decent DS job, and that AWS is not a must for most data scientist roles. Nevertheless, while going through job descriptions on employment websites, I found AWS experience would be a plus to help my resume stand out.
Moreover, I had a chance to discuss this with my very good learning partner from the same cohort, where we share thoughts and our findings in the job market, and we both agreed it is best to go over the AWS courses again, as we believe it would benefit us plenty in job hunting. The second time of AWS learning was much easier and effective than I expected, and I noticed a lot of important content that I missed before. Now, I can build data pipelines using Airflow and Cron easily on EC2. I have observed that managers tend to ask a lot about my AWS experience during interviews. My takeaway from this experience and my suggestion to everyone is that if you find a course is too hard, don’t give it up easily and give it a second chance and your best shot! Your hard work will pay off.
WeCloudData is the right place to help anyone to start a career in this industry and provides the exact analytical and coding tools for its students to succeed, along with real-world experience and career support. Nonetheless, it is important to note that one should not solely rely on the courses delivered by this program or any other ones in the field, as it is impossible to cover every aspect of data science completely. As I mentioned earlier, data science is vaguely defined and the requirements can differ. It is crucial to keep learning even after the courses finish, as the more knowledge you absorb, the clearer your direction will be.
In conclusion
Changing careers is not easy for anyone and you may spend time wondering if all your hard work will get you to your dream job. But what will help you ultimately get that great data science position is your initiative to start something new and your level of commitment when joining this program. May the Force be with you.
