Claire Chung is a PhD student at the Chinese University of Hong Kong researching bioinformatics. She is also one of the authors of Matplotlib 2.0 by Example. We spoke to Claire about her experience in tech, and her book. She offered a unique perspective on gender issues in the industry as well as wider questions of accessibility and the future of research and how technology will continue to change the way we make scientific discoveries.
Packt: What’s the most interesting thing/key take away from your book?
Claire Chung: Good storytelling is crucial even for the best data crunching results.
Data visualization determines whether you can actually get the message across in presentation. This is true not just for the Tech field, but in any workplace setting – to persuade your clients, your boss, or simply to communicate objective quantitative concepts to fellow scientists or even the general public. Many people around feel scared when it comes to the word ‘code’, or think that numbers alone can provide a complete picture, so sometimes they turn to unsuitable plots that are easily produced with common software, leaving them not customized. We want to tell everyone the right plot and refinement of your data graphics do matter, and it doesn’t take superb programming skills or expensive software to create brilliant data visualization.
Packt: Tell us about your experience in the Tech sector, how did you get where you are today and how did you come to write your book with Packt? Did you experience any setbacks along the way?
CC: How I went from a bioinformatics lab to writing a Packt book with a lab alumnae today is a bit like joining dots together. Computers have interested me since I was small, because the internet opened up a brave new world of unseen knowledge that was free for me to explore. Moreover, I find huge fun and satisfaction in using logic and creativity in problem solving. I remember persuading my mum at a younger age to let me replace the graphic card based on my own diagnosis, instead of paying double for just a check excluding repair fee, and solved the issue swiftly. Whenever there are technical problems, family, teachers and friends would usually come to me. I enjoy helping them fix the issues, with explanations so they understand how to deal with similar situations. At that time, I did not particularly aspire to be in the Tech field. Sometimes I think that if I wasn’t a scientist, I might have been a dancer or tour guide or anything else. However, with my curiosity focused on exploring the fascinating deep mysteries of life, I chose to read Cell and Molecular Biology in college.
During my undergraduate study, I saw that the Campus Network Support Team in the IT department of my college was recruiting student helpers. I took the opportunity to test self-learnt skills, and gratefully was accepted after the apt-test and interview. While most members naturally come from the Computer Science and Engineering faculty, and were mostly male, I observed a very good work culture there. Everyone was friendly with and willing to learn from each other, regardless of fields of study, education background, gender or anything else. We became good partners at work and good friends in life. Later, I had the privilege to be appointed as leader of the team, responsible for coordinating team effort, recruitment and training of new members, as well as communicating with senior staff and operators in providing support service. This friendly atmosphere and valuable experience play a part in giving me confidence to work in the Tech field and faith that a bias-free friendly environment is totally achievable.
In pursuing my studies in life science, I found out that biological data plays a huge role in driving data science. Traditional approaches of genetic research, for instance, study gene actions one by one. Not only is this largely time and labour intensive, we can easily miss the big picture of the network of interactions between genes and different components in the multiple layers of regulations. Like other Tech fields, technological advancement makes it much easier to generate data, computational power has also increased exponentially in recent decade to power, but data analysis is still at bottleneck. The amount of skilful analysts cannot keep up with the immeasurable data generated each day. While we do not replace traditional studies being of different scope and the basis of research in any scale, there is much more effort needed to innovate for better delineating biology, the workings of life.
Packt: How has the industry changed since you started working? Do you think it is getting easier for people from different backgrounds to get into the Tech world?
CC: I am working in bioinformatics and have met people in all walks of life getting into Tech. In my point of view, it is certainly evolving into a more open field.
Decades ago, programming and data analysis skills used to be vocational expertise. Computer engineering students graduate into programmers in companies. They mostly work in technology-dedicated companies, or are appointed or sent to attach to existing departments on demand. This model still persists, but I have also observed a large paradigm shift nowadays. Companies are hiring more people from diverse background. Startups and new teams in large corporations with high degree of freedom keep emerging. I have friends with art training developing UX award entry apps. I met people with physics, chemistry and civil engineering education and various level of coding background in the same team when speaking in a Tech conference. Today, like how typists have almost disappeared entirely, office productivity software usage is expected as much as basic English writing and arithmetic, basic programming skills are gradually become some sort of literacy.
In my case, I am working in a lab among the first established bioinformatics lab in Hong Kong. Not long before I enter the lab, there are so few bioinformaticians, that coming to our lab is one of the very few local options to go when you want to analyse any sequencing data at all.
Today we still present an obvious edge in being the most familiar with these techniques, to critically assess and provide solutions to different studies, and hence the most ready to further explore beyond basic analysis, so my supervisor continues to help us select collaboration to get the most interesting questions answered. On the other hand, with increasing availability of courses on bioinformatics and data science, user-friendly packages for data analysis, laboratories are able to hire students who are willing to learn from trial-and-error, or purchase commercial services for basic or routine analyses. While knowledge and techniques set the concrete foundation for anything to build on, I feel that there are no more technical skills that are irreplaceable life-long. Innovation, human-related and overall management skills make the difference.
I was really a sidekick at the time I started; there were only two girls including myself. But guess what? We have more or less even males and females joining as regular students now.
Packt: What are the biggest misconceptions about working in the Tech sector today?
CC: I think the biggest misconceptions are “I am not …, I will never do/understand it”. But more specifically, there seems to be a belief that you must hold a degree in Computer Science or Engineering in order to start your career in the Tech sector today. However, this concept probably isn’t as strong in the field as it is outside.
Many people outside the field got extremely curious, how I am working in front of computers as a student under a biology programme. While formal major study is undeniably the most direct way to obtain solid foundation of knowledge and to enter the field, it is not a definite ticket to success in the industry.
The reverse is true as well. The “Tech field” today is not a shrouded dome. It is a hub where people pass through in getting to their destination. You can start off with a strong computation background, and that can help you land on many different areas. Besides the low-cost learning materials around mentioned, more and more attachment programmes are welcoming aspiring apprentices to bring in new sparks.
Think in the other way, starting with less releases you from high opportunity cost. You can also invest & publish your work on GitHub or app stores. Results speak it all. I believe fresher minds on holiday embarking today can probably come up with more brilliant ideas, than I can do with limited time outside my PhD study.
We should not be intimidated by things like “I am not white”, “I am not a guy”, “I have not tried this before”, etc. Instead, think of “how you will work it out”. We also should not be bounded by stereotypes against ourselves or anyone else. Personal non-work related qualities and hobbies are irrelevant to ability. (I always wear dress or skirts around in campus or in tech conferences simply because I like it.) Of course, we never need the whole world to work in the same sector, which is not healthy for individuals and society at all. Just don’t set a limit for ourselves.
If you want to dive in, be prepared. Then “knock, and the door will be opened.”
Packt: What do you think the future looks like for people working in the Tech industry? Will larger companies strive to make it more accessible to everyone?
CC: I think the future of the Tech industry will get ever more open and competitive.
Will larger companies strive to make it more accessible to everyone? Sure. I think they are doing so even right now. Look at the moves taken at Google cloud platform, Mircosoft STEM resources and NVIDIA’s deep learning institute, just to name a few, including online courses they sponsored. The industry is always in thirst of talent. More open resources bring in more brilliant minds to keep advancement. As discussed, there will no longer be a clear gap set by hours spent or grades obtained in classroom, but willingness to learn and apply actively. Even for people with the strongest formal engineering training, this is the attitude that will push them forward towards far greater success. I am looking forward to seeing more vibrant energy in the field in coming future.
Thanks for chatting with us Claire! Check out Matplotlib 2.0 by Example here. Or explore more data analysis resources here.