Learn newly emerging technologies in data science by analyzing its prominent new trends such as convergence among cloud computing, big data analytics, and machine learning.
- [Voiceover] Just like many other technology fields, the discipline of data science is dynamic and constantly changing. Therefore, it is a must for a data scientist to keep refreshing their knowledge to stay relevant. One of the prominent new trends is the convergence among cloud computing, big data analytics, and machine learning. In fact, it's no longer necessary to provision private resources housed in your own organization to deploy a distributed computing solution like Hadoop.
Various online retail data services, including warehouses, mining, and analytics, are already available in the cloud through vendors like Amazon, IBM, Google, and so on. This makes it cheaper for companies to use data science techniques to solve their business problems, which in turn increases the demand for data scientists. Some other salient features, making these cloud-based data science services more attractive, are their scalability and ease of use.
The majority of data scientists no longer have to worry about data infrastructure and management problems due to these emerging online data science services. In conjunction with the consolidation between cloud computing and big data analytics, the importance of machine learning as the critical part of the data science equation is also rapidly growing. Especially deep learning hinging on a set of machine learning algorithms taking advantage of neural networking is getting more traction.
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