Netflix reached its current level of success, as a popular streaming service and content creator, with its original show, House of Cards. The secret of the successful making of this popular TV show was the extensive data-research that went in. The platform used data science to leverage the huge quantities of data gathered by it to identify key elements – visual elements, actors, subject matter. Incorporating these elements made the show a success among the audience as well as critics.
A Data science professional help business make such decisions. They add value by being at the intersection of business and technology.
As we enter 2020, the world of data science has grown without a doubt. We explore how the fledgling field of data science is evolving in the wake of Fourth Industrial Revolution. Professionals should keep these trends in mind when carving a career in the field.
5 Data Science Career Trends
1.Most organizations are becoming insights-driven
Deloitte survey shows the data maturity of businesses has increased. As many as 70% of the organizations now see analytics as crucial to meeting their business needs. The reason is the emerging trends around cloud computing, big data, machine learning, and AI, which are considered central to making informed strategic decisions that can in-turn optimize revenues.
2.Data Scientists are earning handsomely, but…
Obvious result of the warming up of organizations toward Data Science? Increase in the demand for its experts. The much-cited Glassdoor survey ranks data science professionals among the top 10 most earning pros. However, new signs show that the salaries of this coveted role are plateauing, as noted by Stack Overflow as well as Glassdoor in its new survey. The reason for this flattening lies in more number of data scientists available than before. Another reason of this leveling out is that a little bit of standardization and professionalization of the field of data science is going on, as per Julia Silge, Data Scientist at Stack Overflow.
For data science professionals and aspirants, this means taking a deeper dive, gaining a higher order of business understanding and translating analytics into value-propositions for the products and services. Having said that Glassdoor Senior Economist, Daniel Zhao, notes, “Data scientists still have one of the highest-paying and highest-satisfying job in the US.” They are taking home $95,459 in median annual pay. The trend is expected to continue as more organizations pick up on the need for anchoring user data. Nonetheless, as the field gets more competitive, professionals need to augment their skills and understanding to climb up the ladder and justify their pay grades.
3.Need for “decision-makers” and specialists
New Vantage Partners survey of 2018, as reported in MIT Technology Review, shows even though as many as 97% of firms are investing in Big Data and AI, about half of analytics never make it into production. Now’s the time to move past the pilot phase into deployment, implementation, monitoring and predicting.
To stay relevant in a data science career, it is important for professionals to be able to deploy advanced analytics capabilities for value enhancing business decision making. The industry is looking for professionals who can take organizations behind those numbers, to offer not only predictions but also propose solutions, especially true for consumer-oriented businesses. Gaining industry-specific expertise can also help professionals create value and offer deeper insights.
4.Skills that power the profession – Python is the king!
Glassdoor published 10 crucial software skills for data scientists – Python, R, Hadoop, SQL, Spark, Java, SAS, Tableau, Hive, and Matlab (based on most commonly used terms on job search aggregators). Of these all, if analytics and AI is the key to the future, Python as a skillset is going to play the most significant role. Furthermore, quantitative skills and the art of experimental analysis will help professionals. In addition to all of that, data science professionals will now be expected to scale up data strategy for the entire organization and implement machine learning. Opting for a globally valid, vendor-neutral certification, as offered by Data Science Council of America, among others, can help professionals establish their expertise for a gamut of data science skills.
5. Regulatory Influence – Use data wisely!
As governments lay regulatory frameworks around data protection, such as the General Data Protection Regulation (GDPR) of European Union, data science professionals will be required to not only be aware of the recent developments in data governance but also be able to interpret laws for businesses and advance solutions around it.
What Google’s Chief Decision Scientist has to say? - Data Science Career Takeaway
Cassie Kozyrkov was made the Chief Decision Scientist for Google in 2018. Her career pathway has much to offer for data science professionals. Initially, she was Google Cloud’s Chief Data Scientist, graduated to the role of Chief Decision Scientist. Kozyrkov has a degree in Economics; and later went for an unorthodox combination of Statistics, Neuroscience, and Psychology. She brings together a union of data and behavioral science with human decision-making.
Kozyrkov is more than a data scientist for Google. She views AI and Big Data not as lofty ideals from science fiction, but as new tools to be used by humans. She talks of data science as a decision intelligence discipline –coming together of applied data science, AI and Analytics.
Data Scientists won’t only predict the future, but help businesses make it. Are you ready for that level of professionalism?
