Learn the ethical challenges associated with being a data scientist by analyzing threats around you, which may make it difficult to always follow your moral compass. Jungwoo explains that security and privacy considerations need to be built into your data science projects from their very beginning.
- [Voiceover] The threats are everywhere, in fact, we hear about new data breeches all the time. Sometimes, these security incidents are insider jobs, disgruntled employees, or industrial spies maybe lurking around you. You yourself may be tempted to eavesdrop on your coworkers or supervisor's data, just out of curiosity. As a result, the ethical integrity of a data scientist can make up a huge difference in guarding the security and privacy of user data.
In addition to watching out for an insider threat and keeping yourself out of the danger zone, it is also an ethical thing to intentionally and proactively build in security into a data science product you are developing. If you don't do your job as a data scientist to ensure the security and privacy of your customer data, somebody is bound to fall victim to a crime down the road. It is often the case that security is a second thought when working on a data science project.
Time to market pressure seems to be always winning. However, many organizations are realizing that, considering security and privacy risks, and their countermeasures from the very beginning of a data science project can ultimately save all the troubles of legal, monetary, and reputational liabilities. The data science code of ethics is still being formed, as the profession of data scientist keeps evolving.
As the profession matures, you need to be constantly reminding yourself of all the basic elements of ethical conducts as a data scientist.
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and analytics, explores which markets are using big data the most, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the four leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why analytics is important
- How data science is used in social media, climate research, and more
- Data science skills
- Data science certifications
- The future of big data