Learn what a data scientist does in terms of the roles they play in an organization and what it takes to become one by analyzing the differences between data scientist jobs and other more specialized jobs in data science.
- Data science is an all encompassing term. Similarly, data scientist is also an all encompassing job title. Data is everywhere and its volume is also ever increasing. Every organization can benefit from hiring a person who can provide data analysis and analytics to reflect on its past performances and to attempt to predict its future. However, not every company can afford to hire a person who's job is dedicated to working on corporate data.
Not to mention, hiring multiple experts specializing in different aspects of data science. The role of a data scientist is that of a generalist instead of a specialist. In an environment where a data scientist works with other data science specialists, such as a machine learning scientist, the data scientist can act as a liaison between the leadership of the company and the data science specialists. Therefore, one of the hallmarks of a competent data scientist is an ability to communicate effectively.
Compared to other highly specialized jobs in data science, the entrance barrier to a data science job is relatively lower. A solid training in computer science or statistics, may be enough for you to get started at an entry-level position. But a Master's degree in data science is a big plus. In this case, what's more important is your passion for data. Also, a potential for job growth is very high as you become a seasoned data scientist and take on various leadership positions in a company.
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