DS VS ML

 Data science, analytics, and machine learning are growing at an astronomical rate and companies are now looking for professionals who can sift through the goldmine of data and help them drive swift business decisions efficiently. IBM predicts that by 2020, the number of jobs for all U.S. data professionals will increase by 364,000 openings to 2,720,000. We caught up with Eric Taylor, Senior Data Scientist at CircleUp, in a Simplilearn Fireside Chat to find out what makes data science and data analytics such an exciting field and what skills will help professionals gain a strong foothold in this fast-growing domain.

What is Data Science?

People have tried to define data science for over a decade now, and the best way to answer the question is via a Venn diagram. Created by Hugh Conway in 2010, this Venn diagram consists of three circles: math and statistics, subject expertise (knowledge about the domain to abstract and calculate), and hacking skills. Essentially if you can do all three, you are already highly knowledgeable in the field of data science.

Data science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions. Read more