In this chapter, we will understand the data science process and terminologies required to understand the process.
“Data science is the blend of data interface, algorithm development and technology in order to solve analytical complex problems”.
Data science is an interdisciplinary field encompassing scientific methods, processes and systems with categories included in it as Machine learning, math and statistics knowledge with traditional research. It also includes a combination of hacking skills with substantive expertise. Data science draws principles from mathematics, statistics, information science, and computer science, data mining and predictive analysis.
The different roles that form part of the data science team are mentioned below −
Customers are the people who use the product. Their interest determines the success of project and their feedback is very valuable in data science.
This team of data science signs in early customers, either firsthand or through creation of landing pages and promotions. Business development team delivers the value of product.
Product managers take in the importance to create best product, which is valuable in market.
They focus on design interactions around data models so that users find appropriate value.
Data scientists explore and transform the data in new ways to create and publish new features. These scientists also combine data from diverse sources to create a new value. They play an important role in creating visualizations with researchers, engineers and web developers.
As the name specifies researchers are involved in research activities. They solve complicated problems, which data scientists cannot do. These problems involve intense focus and time of machine learning and statistics module.
Adapting to Change
All the team members of data science are required to adapt to new changes and work on the basis of requirements. Several changes should be made for adopting agile methodology with data science, which are mentioned as follows −
Choosing generalists over specialists.
Preference of small teams over large teams.
Using high-level tools and platforms.
Continuous and iterative sharing of intermediate work.
In the Agile data science team, a small team of generalists uses high-level tools that are scalable and refine data through iterations into increasingly higher states of value.
Consider the following examples related to the work of data science team members −
Designers deliver CSS.
Web developers build entire applications, understand the user experience, and interface design.
Data scientists should work on both research and building web services including web applications.
Researchers work in code base, which shows results explaining intermediate results.
Product managers try identifying and understanding the flaws in all the related areas.