In this section, 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 tackle analytical complex issues”.
Data science is an interdisciplinary field incorporating scientific methods, processes and systems with categories included in it as Machine learning, math and statistics knowledge with traditional research. It additionally 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 various roles that form part of the data science team are mentioned below −
Clients are the people who utilize the product. Their interest decides the success of project and their feedback is truly significant in data science.
This team of data science signs in early clients, either firsthand or through creation of landing pages and promotions. Business development team delivers the estimation of product.
Product managers take in the significance 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 investigate and transform the data in new ways to create and publish new highlights. These scientists also combine data from diverse sources to create a new value. They play an significant role in creating visualizations with researchers, engineers and web developers.
As the name specifies researchers are engaged in research activities. They solve complicated issues, which data scientists can't do. These issues involve intense focus and time of machine learning and statistics module.
All the colleagues of data science are required to adapt to new changes and work on the basis of requirements. A few changes should be made for adopting agile methodology with data science, which are referenced 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 utilizes high-level tools that are scalable and refine data through iterations into increasingly higher states of value.
Consider the following examples identified 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 zones.