Data science is a discipline conflating elements from various fields such as mathematics, machine learning, statistics, computer programming, data warehousing, pattern recognition, uncertainty modeling, computer science, high performance computing, visualization and others.
According to Cathy O’Neil and Rachel Schutt, two luminaries in the field of Data Science, there are about seven disciplines that even data scientists in training can easily identify as part of their tools set:
- Machine Learning
- Computer Science
- Data Visualization
- Domain Expertise
- Communication and Presentation Skills
The Future of Big Data
With some guidance, you can craft a data platform that is right for your organization’s needs and gets the most return from your data capital.
Most data scientists, however, are experts in only a couple of these disciplines and proficient in another two or three – that’s why Data Science is a team sport.
I’ve definitely learned the importance of teamwork in this field over the last few months, while working with Perficient Data Science team on a Big Data Lab.
Ultimately, the goal of Data Science is to extract meaning from data and create products from the data itself. Data is the raw material used for the study of “the generalizable extraction of knowledge”.
With data scaling up by the day, it should not come as a surprise that Big Data would play an important role in a data scientist’s work – herein lies the importance of our Big Data Lab and our teamwork.
Our Big Data Lab is the place where Data Science’s many underlying disciplines come together to create something greater than the summation of our individual knowledge and expertise – synergistic teamwork.