Ken Jee is a data scientist and YouTube content creator who has quickly become known for creating engaging and easy-to-follow videos. Jee has helped countless people learn about data science, machine learning, and AI and is the initiator of the popular #66daysofdata movement.
Currently, Jee works as the Head of Data Science at Scouts Consulting Group. In this post, he discusses his work as a data scientist and offers advice for anyone looking to enter the field. We explore the importance of university education, the relevancy of math for data scientists, creating visibility within the industry, and the value of an open mind when it comes to new technologies.
This post is a transcription of bits and pieces of Jee’s wisdom with which I had the pleasure to speak to on my podcast. At the conclusion of this article, you’ll find a link to the entire discussion. While there have been numerous editing in the answers provided by Jee to ensure brevity and conciseness, the intentions of his answers are maintained.
Why did you start making data science videos on YouTube?
I started making data science videos on YouTube because I didn’t see the resources that I was looking for when I was trying to learn data science.
I also saw making videos as the best way to improve my communication skills. Creating content has given me a competitive advantage because it has attracted employers to me rather than going out to get them. I usually refer to this as the concept of content gravity. The more content that I create, the more pull I have on employers and opportunities coming to me.
I love working on interesting data projects and creating easy-to-digest content that can help others learn and grow. I believe that data science skills are valuable and shareable and that data-driven content has great potential to go viral. Companies should encourage their employees to have side hustles and be public about them, as it looks good for the company.
I see a future where everyone uses social media to share their work and ideas and where this is accepted and expected in most roles. In some of my previous job roles, I’ve been referred to as “the guy who makes YouTube videos.” My external efforts outside of work have aided my internal visibility within companies.
How did you become interested in data science?
I became interested in data science because I wanted to improve my golfing skills. I started to explore how data could help me analyze my performance and find ways to improve. I soon discovered that I had a unique advantage: the ability to analyze data and create data-driven actions to improve my golfing abilities. This led me to explore further other performance improvement methods supported by data and intelligence.
How essential is mathematics in data science?
I believe that mathematics is less important when breaking into the data science field. What’s important is getting your hands dirty and coding. I recommend that people get their hands dirty by building projects and coding, as this will help them intuitively find where math is valuable and important.
I also recommend reviewing calculus, linear algebra, and discrete math, but only once you have a reason to do so and understand how they are relevant to data science. As you continue to progress within the field, you will gradually learn where math skills are important and relevant. And once you see the value that they bring, you will be more motivated to learn them.
Is self-directed learning more important than a formal degree when entering the data science field?
One of the primary reasons I encourage people to investigate unusual learning methods, as opposed to attending a university, is that many students underutilize the resources available at institutions. I used all of my office hours with professors and asked questions from PhDs who knew a lot about subjects, but I discovered very few students did the same.
In my opinion, having a degree is only useful if you put in the effort and make the most of the available opportunities. I recommend taking advantage of other options available at university, such as side projects. Doing so can help students get the most out of their education and give them an edge in the job market. However, I warn that simply getting a degree does not guarantee a successful career.
Editor’s Note: Jee contributes to the data science learning platform 365DataScience, educating learners on starting a successful data science career. He also has a master’s degree in computer science and another in business, marketing, and management. Jee holds a bachelor’s degree in economics.
Obtaining a master’s degree in an advanced subject such as data science is not always the best method to stand out. Having an impressive portfolio, unique work, or volunteer experience can be more valuable.
It is worth considering if you can invest the time and money into obtaining a master’s degree as it is undoubtedly a viable resource. But it’s also important to consider the opportunity cost of returning to school to land a job. So, it’s financially practical to view attending graduate school to obtain a particular role within AI as an opportunity cost. You essentially must determine if attending grad school will provide a good return on investment.
How do you learn?
I learn best by struggling through something on my own at my own pace, rereading the same thing over and over again until I understand it. In grad school, I fell in love with reading, and the majority of my knowledge came from textbooks.
I recommend looking at things from different angles to get a diverse understanding of a topic. One of the most important keys to accelerating learning is finding a suitable medium that explains the topic in a way that makes sense to you, this could be reading a blog post, watching a video, or listening to a podcast.
Although my primary method of obtaining knowledge in grad school was through books, I admit that my learning of data science concepts and topics today involves videos and YouTube tutorials. Specifically, I want to mention the popular data science YouTube channel StatQuest with Josh Starmer.
What are the best skills to differentiate yourself as a data scientist?
Data scientists have to learn coding, math, and business in order to be successful. I differentiated myself from the competition with my unique combination of skills. My business knowledge and ability to meet the strategic requirement for coding and data science made me a highly desirable candidate. My resume and portfolio stood out from the competition. Additionally, my communication skills and business knowledge gave me a distinct edge in job interviews.
How did you become the head of data science at your current company?
I discovered I didn’t fit well into corporate bureaucracy very early on. My focus was on creating value, getting noticed for adding value, and getting work satisfaction. My title has progressed from data scientist to head of data science. I am now responsible for all data-related work and have taken on the role of Director of Data Science.
This change reflects the increased responsibilities that I have taken very early on within my current company, from solely being responsible for all data science activities to managing teams of data scientists. If you are looking for a job, I recommend that you create your opportunities by reaching out to potential employers.
You may be surprised at how open they are to hire you if they see that you are willing and able to do the work. I advise data science practitioners to find a position that doesn’t yet exist or make one for themselves. This way, you can skip the line and get to where you want to be without waiting for opportunities.
What is your advice to entry-level data scientists?
Entry-level data scientists should share their work and journey with others. People are hesitant to produce content because they are afraid of being judged, but this is not usually the case. People are more likely to be positive and supportive. I also recommend learning to code first, as this skill is valuable for data scientists. However, I recognize that everyone learns differently, so this is not a one-size-fits-all approach.
Summary from the author
Jee’s journey within data science is unique, but the steps that led to his success are replicable and adaptable to your data science career. My discussion with him revealed the importance of using digital content to communicate your expertise and presence within the data science field, which can sometimes be filled with noise. His advice to data science practitioners is to focus on creating value and making sure that you’re learning continuously to keep up with the rapidly changing field. So whatever your goals are for your data science career, don’t forget to enjoy the journey and document it along the way!