It is projected that there will be no jobs for data scientists in the next decade—the bubble of data science job titles is bursting as companies have learned harsh lessons. Rather than going for generalization in talent acquisition, they are prioritizing specialized skill sets. Data science will continue to have a growing impact on organizations, just not as a generalized role.
New hiring practices set by organizations dictate that employees should be assessed based on specific skills, not by their job titles. Some of the trending technologies that are attracting the biggest investments include artificial intelligence (AI), machine learning (ML), big data, and cloud computing. So while data science is more important than ever, specialization is the way of the future.
The Evolving Role of Data Science
Becoming a jack-of-all-trades in data science is not the wisest strategy. When you are looking for jobs in data science, it is recommended that you hone your skills for a certain craft. You can become a data engineer, machine learning engineer, algorithms developer, or a data analyst, for example.
Globally, data science education and curriculum has become more accessible than ever. These courses such as our Caltech Data Science Program provide fundamental concepts for aspiring data science students. It is paramount that this generalized learning is incorporated as a stepping stone for students, after which they can go deeper into a domain and choose a specialization.
Industry trends reveal that the most in-demand data scientists are those who have specialized in a certain domain such as database management, AI, or machine learning. Specialization does not only come with a salary boost, but it can also allow you to handle job responsibilities that are more meaningful to you.
Unlike other careers with stagnant growth potential, data science offers a wide range of opportunities where you have to keep track of the latest industry developments to remain relevant.
Key Causes of Disruption to the Data Science Role
The following factors are causing a major shift in the contemporary data scientist role.
Growth of Data
The rise of AI and ML-based applications and algorithms has transformed numerous businesses and industries. Additionally, IoT devices are integrated into smart factories and smart homes, while the domestic user relies a lot on social media platforms. The proliferation of all these technologies is expected to generate voluminous amounts of data. Unfortunately, there are not enough resources to manage data demands. Sixty-five percent of companies revealed that they were unable to classify or analyze their stored data, according to a study from Gemalto.
New Data Security Standards and Regulations
After the European Union enforced the General Data Protection Regulation (GDPR), companies are required to follow certain regulations when it comes to data storage and processing. To meet this requirement, data scientists are setting up powerful data security standards. Organizations require data scientists to assist them in linking their data with business processes so that they can easily remain up-to-date with these ever-changing privacy regulations. On January 1, 2020, California followed the EU with a data privacy act that’s very similar to the GDPR. This event is foreshadowed to increase the demand for data science skills.
Personalized Products and Services
Specialization in data science is picking up because these skills allow companies to extract relevant data insights. For instance, a company can use machine learning algorithms to provide recommendations for customers based on demographics like age, gender, habits, and other metrics. Similarly, they can employ business intelligence (BI) to identify which services are incurring losses to the company.
Real-time analytics is also reshaping the skills of data science. When data comes from various sources, especially when it is dynamic, it can be handled only by experienced data scientists with specialized skill sets. Moreover, the demand for analytics governance and higher-quality data is making an impact on how organizations can deal with data analytics job positions.
Advancement and Adoption of Edge Computing, Cloud Computing, AI, and Machine Learning
The growth of technologies like AI, deep learning, machine learning, and their applications (like chatbots, and robotic process automation) has greatly increased the significance of data science. A growing number of organizations are embracing machine learning and AI. It means that the tools and skills related to AI will be widespread across all sectors.
According to IDC and Forrester, 75 percent of enterprises will integrate intelligence automation into their infrastructure by 2022. AI-powered applications will facilitate the identification of experiential and operational insights, maximizing innovation. Senior data executives and officers with interest in AI have to dedicate their efforts towards obtaining the data for their data science teams in 2020.
Enhancement of Data Science Processes
The essence of a data science process is that it entails repetition, along with the input of multiple skill sets and professionals. Today’s companies want to pick up the pace and refine the process so that it is less random and more predictable. They intend to continuously optimize the processes involving data science, a strategy that requires appropriate skills.
Evolution of Data Science Teams
The dynamics of data science teams have experienced a turnaround. Unlike before, companies are clear on what benefits data science has to offer them, so they can go for specialized skills to generate value.
Skills Specialization Translates Across Both Career Development and Business Needs
Specialized skillsets in data science offer the following benefits:
- Address business predicaments and internal stumbling blocks
- Leverage AI to attain greater flexibility in innovation
- Become a catalyst for an upward career trajectory
Specialized skill sets are used to demonstrate:
- Career Development – Facilitates collaboration and improves efficiency with emerging technologies. Over time, you can learn domain expertise via real-world experience, especially when your daily data science job requires a lot of attention to a wide range of specialized tasks.
- Business Needs – Assist organizations in establishing robust corporate business capabilities and optimize data science methodologies.
Types of Data Science Specializations
If you work in the United States, Canada, or Europe, then you can go for Data Science Certifications such as the following:
- Certified Associate: Data Analyst
- Big Data Certification
- Certified Specialist in Predictive Analytics
- Certified Business Intelligence Professional
- Caltech Data Science Bootcamp
Rather than targeting lengthy, generalized programs, data scientists are looking to target short programs with a focus on a particular skill set. Some of these areas include enterprise cloud platform, data analysis, database management, machine learning, deep learning, big data, neural networks, Python, statistical analysis, and solution architecture. For instance, in the case of machine learning, a study reveals that around 45 percent of developers want to learn or enhance their current machine learning skills.
Mastery of these specializations will create job roles like data ethicists, AI specialists, cloud engineers, and master data management program managers. This will affect how businesses target specialized skill sets. Eventually, the role of data scientists is going to be changed forever.
Technologies, businesses, industries, and customer expectations are blossoming at a rapid rate. Organizations have to achieve the right balance and address demands related to both the talent structure and emerging technologies.
As 2020 begins, companies must improve their innovative and problem-solving capabilities, both in terms of speed and efficiency. Additionally, the face of future workspaces hinges on specialized value generation and contribution sources. Data scientists are forecasted to be the most influential contributors in these cases. Companies will forgo their existing hiring procedures and emphasize the introduction of professionals with specialized domain expertise. The talent structure will change from a conventional hierarchical pyramid shape and take the form of a rhombus shape—where talent and leadership are going to be supported with an AI-centric foundation.
Simplilearn is a top professional certification company that can offer the education and certification data scientists need to narrow their domain expertise and specialized knowledge, including the Data Science Course in collaboration with IBM. A great option for ongoing education and for data scientists wishing to refine their specialized skills moving into the future.