Data Science Seminar
On Thursday, April 18th, The WISH staff hosted data science informational seminar about the importance it has on metrics and statistical measurements. This gave students a chance to learn more about the field from an employer and student worker perspective.
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Data science is a concept to unify statistics, data analysis, machine learning, and their related methods in order to understand and analyze actual phenomena with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.
Data science expert Jane Wall was the primary speaker for the event giving background and knowledge about the field. Along, she brought two of her graduate students, Terry and Amanda, to give insight from a student perspective.
“The purpose of my project was that a lot of the times in politics, we get stimulus packages for economic,” Terry said. “So, I wanted to take a look at some visuals that could help us decide in a hypothetical stimulus situation as to where we should spend the money so we’re actually doing some good here.”
“My main points of interest are what areas are a need for spending where we may have aging infostructures where bridges aren’t paying inspections and where we can get the most value for our dollar,” Terry said.
Data science has recently become a popular term among business executives. However, many critical academics and journalists see no distinction between data science and statistics, whereas others consider it largely a popular term for data mining and big data.
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
“The first part and probably the most time intensive was data collection,” Amanda said. “What I wanted to do was take the database article and put it into a useful text format where I could put it in a data frame, look at the words that they are using and take out individual words.”
Effective data scientists can identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.