August 10, 2018
Before mass communications data was readily available, communicators and advertisers had to take a shotgun approach – spread your message far enough, wide enough, and loud enough, and eventually it’ll reach some of the people that you intended.
But today, the number of people and the number of messages has increased exponentially, meaning that a more targeted, strategic approach is needed if your message is to get through. Fortunately, data science is making that possible.
July 19, 2018
Think a career in data science is right for you? Here are four steps to acquiring the skills you’ll need to land your first job in this high-demand field.
July 2, 2018
You’ve probably heard the terms before and are wondering: What is data science? What is a data scientist? What does a data scientist do, and how can I become one?
In today’s data-driven world, businesses and other organizations are collecting information that could help improve their products and services, but there’s just one problem: too much data! How can a business executive cut through all the numbers, find important trends, and make the right decision? They need a data scientist to help make sense of the information.
June 29, 2018
Students new to data science may wonder whether they should use R or Python for their data analysis tasks. Both are popular languages for statistics, but there are key differences.
While R was developed specifically for statisticians and is great for data analysis, Python is an easy-to-learn, easy-to-read language that spans disciplines and is useful to integrate data analysis tasks with web apps or to incorporate statistics code into a production database. Python is used to develop video players like YouTube, power apps like Instagram, test microchips at Intel, run a search engine at Google, and power transactions on the New York Stock Exchange (NYSE). The language greatly resembles English, making it intuitive to use and accessible to just about anyone.
May 9, 2018
In 2013, New York City’s Health and Human Services division embarked on a noble project: use data to ensure that homeless families were paired with appropriate public services. To start, the division’s data group was tasked with building a model to predict, based on a family’s characteristics, how long they might stay within the system. The first task? Determine which characteristics to use in their model.