Data Science as a field has been predominantly used to help humans make better decisions and build systems that could automate tasks to get work done faster. Previously this field of knowledge was used to work on stand-alone data to demonstrate visualizations and aid humans make better decisions in a specific field of work. But the field has evolved since to focus more on seamlessly integrating data science into organizational systems so as to produce affordable, fast, and reliable products to people that are utilized on a daily basis.
If you are a data scientist, you are in for one more great year with lots of prospects. Here is how you can plan the year ahead
DevOps is the Key word
If you are a data scientist who focuses on building models you must also develop DevOps skills as it is no longer an independent specialization but rather an expectation of every data scientist. Learn to integrate your data science model into complex legacy systems for seamless work.
Communication and collaboration are the essence
As a data scientist you would need to know how to collaborate within and across teams through better communication, be empathetic, and understanding. Data science is no longer just about the technical aspects of the field and require personnel who can multitask and take on different roles to work more effectively.
Minimize ethical and social repercussions of your AI applications
While data science has been great for business decisions and helped organizations advance at a much faster rate than anticipated, machine learning deployment has several ethical and social issues that need to be accounted for when implementing any new technology.
There needs to be a recognition of human emotions such as fear, ego, and worry regarding loss of jobs which result from automation of tasks through data science.
There is a greater need for organizations to take responsibility and work with HR personnel to come with solutions that minimize the risk of human suffering in the process to make the organization more efficient and reliable. An inability to recognize the impact on human lives will invariable greatly impact the usage and implementation of AI.
Furthermore, organizations and businesses would need to recognize the threat posed by AI in breaching human privacy and the ethical concerns associated with such acts. It is important for them to build business models and utilize AI in a way that is more socially conscious.
Practice responsible use of AI to conserve the environment
Though AI can help predict climate change and combat environmental problems, data scientists also need to recognize the environmental threat posed by AI in this process.
Modern day applications require copious amounts of training of machines which in turn lead to large carbon emissions that impact the planet and add to the existing issues of global warming plaguing the earth. Running complex algorithms and programs also require copious amounts of energy which is often not accounted for when thinking of AI and building systems.
Data scientists need to account for these implications on the environment and take responsibility in coming up with minimally invasive solutions that not only aid human development but don’t exacerbate the current rates of environmental degradation.
While AI is used to come up with solutions to combat climate change, it can also pose a greater threat to the planet if data scientists fail to acknowledge the impact of using AI on the environment and ecosystems.
Reduce the biases in your data
Data scientists need to practice more inclusive research, become more socially conscious, and account for human bias involved in the process of training machines.
Subjectivity in decision making and training exist across different fields. It is important to acknowledge the role and impact of this subjectivity in training and automation of machines. Biases pertaining to race, class, caste, sex, gender, religion, ability, age, language, and/or sexual orientation often get transferred to this process of training which could have long term repercussions for the progress of humanity.
Data scientists need to be more accountable of their own positionality in working on specific projects and account for the biases that they could unintentionally bring into their work to ensure that machine training and programming occurs in a more trustworthy and inclusive manner.
If the pandemic has taught us all something, it is that we need to be more responsive and compassionate humans who make more responsible decisions to preserve the planet for our future generations.