We have already talked about a simple learning roadmap for you to develop your data science skills in the first resolution. We also talked about the importance of staying relevant in an increasingly automated job market, in our second resolution. Now it’s time to think about the kind of person you want to be and the legacy you will leave behind.
3rd Resolution: Choose projects wisely and be mindful of their impact.
Your work has real consequences. And your projects will often be larger than what you know or can do. As such, the first step toward creating impact with intention is to define the project scope, purpose, outcomes and assets clearly. The next most important factor is choosing the project team.
1. Seek out, learn from and work with a diverse group of people
To become a successful data scientist you must learn how to collaborate. Not only does it make projects fun and efficient, but it also brings in diverse points of view and expertise from other disciplines. This is a great advantage for machine learning projects that attempt to solve complex real-world problems.
You could benefit from working with other technical professionals like web developers, software programmers, data analysts, data administrators, game developers etc. Collaborating with such people will enhance your own domain knowledge and skills and also let you see your work from a broader technical perspective.
Apart from the people involved in the core data and software domain, there are others who also have a primary stake in your project’s success. These include UX designers, people with humanities background if you are building a product intended to participate in society (which most products often are), business development folks, who actually sell your product and bring revenue, marketing people, who are responsible for bringing your product to a much wider audience to name a few. Working with people of diverse skill sets will help market your product right and make it useful and interpretable to the target audience.
In addition to working with a melange of people with diverse skill sets and educational background it is also important to work with people who think differently from you, and who have experiences that are different from yours to get a more holistic idea of the problems your project is trying to tackle and to arrive at a richer and unique set of solutions to solve those problems.
2. Educate yourself on ethics for data science
As an aspiring data scientist, you should always keep in mind the ethical aspects surrounding privacy, data sharing, and algorithmic decision-making. Here are some ways to develop a mind inclined to designing ethically-sound data science projects and models.
- Listen to seminars and talks by experts and researchers in fairness, accountability, and transparency in machine learning systems. Our favorites include Kate Crawford’s talk on The trouble with bias, Tricia Wang on The human insights missing from big data and Ethics & Data Science by Jeff Hammerbacher.
- Follow top influencers on social media and catch up with their blogs and about their work regularly. Some of these researchers include Kate Crawford, Margaret Mitchell, Rich Caruana, Jake Metcalf, Michael Veale, and Kristian Lum among others.
- Take up courses which will guide you on how to eliminate unintended bias while designing data-driven algorithms. We recommend Data Science Ethics by the University of Michigan, available on edX. You can also take up a course on basic Philosophy from your choice of University.
- Start at the beginning. Read books on ethics and philosophy when you get long weekends this year. You can begin with Aristotle’s Nicomachean Ethics to understand the real meaning of ethics, a term Aristotle helped develop. We recommend browsing through The Stanford Encyclopedia of Philosophy, which is an online archive of peer-reviewed publication of original papers in philosophy, freely accessible to Internet users. You can also try Practical Ethics, a book by Peter Singer and The Elements of Moral Philosophy by James Rachels.
- Attend or follow upcoming conferences in the field of bringing transparency in socio-technical systems. For starters, FAT* (Conference on Fairness, Accountability, and Transparency) is scheduled on February 23 and 24th, 2018 at New York University, NYC. We also have the 5th annual conference of FAT/ML, later in the year.
3. Question/Reassess your hypotheses before, during and after actual implementation
Finally, for any data science project, always reassess your hypotheses before, during, and after the actual implementation. Always ask yourself these questions after each of the above steps and compare them with the previous answers.
- What question are you asking? What is your project about? Whose needs is it addressing? Who could it adversely impact?
- What data are you using? Is the data-type suitable for your type of model? Is the data relevant and fresh? What are its inherent biases and limitations? How robust are your workarounds for them?
- What techniques are you going to try? What algorithms are you going to implement? What would be its complexity? Is it interpretable and transparent?
- How will you evaluate your methods and results?
- What do you expect the results to be? Are the results biased? Are they reproducible?
These pointers will help you evaluate your project goals from a customer and business point of view. Additionally, it will also help you in building efficient models which can benefit the society and your organization at large.
With this, we come to the end of our new year resolutions for an aspiring data scientist. However, the beauty of the ideas behind these resolutions is that they are easily transferable to anyone in any job. All you gotta do is get your foundations right, stay relevant, and be mindful of your impact. We hope this gives a great kick start to your career in 2018.
“Motivation is what gets you started. Habit is what keeps you going.” ― Jim Ryun
Happy New Year! May the odds and the God(s) be in your favor this year to help you build your resolutions into your daily routines and habits!
