We are living in the information age, and in this age, data is the currency of the digital economy. This article is aimed at how businesses and job-seeking individuals can leverage and benefit from Data Science.
Amit Dugh & Ravi Dugh, May 13, 2020
The science of using data to drive efficiencies, create new products, and deliver business value is called Data Science. It is a multi-disciplinary field with elements of statistics, programming, and business. Data can be turned into insights to increase efficiencies, reduce costs, identify new opportunities to gain a competitive advantage. Data scientists deploy techniques for data analysis, data visualization, data mining, and also machine learning. Data Science is about uncovering findings that are central to the strategic business value (Lo).
Data Science for Businesses
The importance of Data Science can be easily shown by the below figure depicting the market capitalization of the Top companies in the world. Companies in Energy, Financials, and automobile sectors haven fallen in rankings. The rankings of data-driven companies are on the rise.
Further, when we closely review the industry sectors, the data-driven transformation has impacted the sectors unevenly.
According to the BCG analysis, we can see that China has the most advanced technology companies by number. However, the US is the leader when viewed from data maturity. The below chart depicts the world leader in data-driven innovations.
In order for the businesses to maintain and leverage data science competitive edge, they must incease their data-driven maturity by accelerating data transformation.
Data Science Careers for Individuals
Harvard Business Review in 2012 referred to Data Scientist as “The Sexist Job of 21th Century”. This title still holds, and the field is still undergoing a massive change. In the age of the COVID19 pandemic, the Digital economy is accelerating even faster than before. Based on the latest statistics, we can see that global social media users are have not even reached 50%. Unique mobile phone users are about 66% of the population.
These statistics are expected to increase based on the Hootsuite report. With more digital growth comes the data collection and the insights that can be drawn out of this data. Such insights have been described above in the business section as to how it can be leveraged for increasing revenue and reducing costs in short an effective tool to have a competitive edge over others who do not use it.
Now let’s look at the report by the World Economic Forum report “Jobs of Tomorrow: Mapping Opportunity in the New Economy” where it is reported that the demand for both “Digital” and “Human” factors are the critical drivers for growth in jobs of future. Careers in Data and AI professional clusters are expected to increase by about 16% from 2020 to 2022.
The below figure points out the top 10 emerging jobs and respective skills required in the Data and AI professional job cluster as described by WEF.
Now that we have seen prospective career opportunities, we need to identify the job roles, the skills required to perform the jobs, and the respective salaries that are associated with the job roles.
The job roles in data science can be roughly divided into, data analyst, data architect, statistician, database analyst, machine learning engineer, data engineer, data scientist, and data & analytics manager. The roles of a data analyst, machine learning engineer, data engineer, and data scientist are core data science roles. The following is a summary of skills (Nelson):
Often Data Science can be interchangeably used with Analytics, but there are differences, and a lot depends on the context. Analytics is about analyzing the data, e.g., using existing dashboards. Data Science is diving into data at a granular level to find patterns. Data Science requires a complex skillset in mathematics, technology, and business.
The jobs roles can also be described based on the work being performed, or the hierarchy of the Data Science needs as per below:
Machine learning (ML) engineers are the intersection of computer science and data science. They are expected to program algorithms that can be used at scale for making decisions (e.g., pattern matching). Machine learning engineers often focus on writing production-level codes to develop computer systems or software robots. ML engineers typically take inputs from data scientists to build APIs to ensure real-time smooth functioning of a technical model that requires some inputs and generates some outputs to make business decisions.
Data Science industry is fast changing. In 2020, new titles have emerged in the sector (ODSC); these include:
- AI Product Manager
- AI Architect
- Information Security Analyst
It is important to note that Data Science salaries are higher than other computer occupation salaries.
According to BLS (Bureau of Labor Statistics), Computer and Information Research Scientists have median wages of $122k with a projected growth rate of 16% for 2018-2028.
Now, let talk about individuals with skills from business and are looking for a mid-career more. How exactly can such job-seeking individuals leverage their business skills to get data science jobs.
Individuals with domain expertise and analytical skills can go onto learning data science courses available online to build and redirect their careers into Data Science applications in their respective domains. A list of online websites offering Data Science Professional Certifications is available below.
We believe that data science has a huge potential, especially considering as many industries are going through digital transformation. The technology-driven change will continue, and as such, the creative and critical thinking skills required to solve business problems will be in high demand.
- Photo by Markus Spiske on Unsplash