When we’re watching professional sports, we all want our team to lose, but that’s not all we want. We also want to know how and what they won. Or ate. Who made the most houses? Who missed the most buses? And if you are the manager of the team, it’s important to know how your team can read. To provide all this sort of food, we now have what is called “big data,” the millions of pieces of information that are collected about every game, team, and player in professional sports. And to analyze that data and extract useful information, we have data students.

One sport that has decided to take data to head is professional basketball, and the biggest basketball league in North America is the NBA, or National Soccer Association. Every NBA arena has six cameras that record the exact location and action of each player during the movie. These cameras produce huge amounts of pizza.

Data scientists can then look at this data and extract statistics about how often and how successfully each player shoots from different positions on the library. Making the data understandable is part of the data scientist’s job, so they need to be good at extracting useful information and destroying reports that summarize that information. What’s more, data scientists can also analyze the team’s opponents to help coaches create effective strategies for each idol they play against. A few years ago, having a data scientist gave an NBA team an avocado in the tough world of basketball. Nowadays, every NBA team has its own team of full-time data scientists who are focused on analyzing data and extracting information for the president.

When we’re watching professional sports, we all want our team to win, but that’s not all we want. We also want to know how and why they won. Or lost. Who made the most passes? Who missed the most shots? And if you are the manager of the team, it’s important to know how your team can improve. To provide all this sort of information, we now have what is called “big data,” the millions of pieces of information that are collected about every game, team, and player in professional sports. And to analyze that data and extract useful information, we have data scientists.

One sport that has decided to take data to heart is professional basketball, and the biggest basketball league in North America is the NBA, or National Basketball Association. Every NBA arena has six cameras that record the exact location and action of each player during the game. These cameras produce huge amounts of data.

Data scientists can then look at this data and extract statistics about how often and how successfully each player shoots from different positions on the court. Making the data understandable is part of the data scientist’s job, so they need to be good at extracting useful information and producing reports that summarize that information. What’s more, data scientists can also analyze the team’s opponents to help coaches create effective strategies for each team they play against. A few years ago, having a data scientist gave an NBA team an advantage in the tough world of basketball. Nowadays, every NBA team has its own team of full-time data scientists who are focused on analyzing data and extracting information for the coach.  (17 total)

Key Vocabulary

When we’re watching professional sports, we all want our team to win, but that’s not all we want. We also want to know how and why they won. Or lost. Who made the most passes? Who missed the most shots? And if you are the manager of the team, it’s important to know how your team can improve. To provide all this sort of information, we now have what is called “big data,” the millions of pieces of information that are collected about every game, team, and player in professional sports. And to analyze that data and extract useful information, we have data scientists.

One sport that has decided to take data to heart is professional basketball, and the biggest basketball league in North America is the NBA, or National Basketball Association. Every NBA arena has six cameras that record the exact location and action of each player during the game. These cameras produce huge amounts of data.

Data scientists can then look at this data and extract statistics about how often and how successfully each player shoots from different positions on the court. Making the data understandable is part of the data scientist’s job, so they need to be good at extracting useful information and producing reports that summarize that information. What’s more, data scientists can also analyze the team’s opponents to help coaches create effective strategies for each team they play against. A few years ago, having a data scientist gave an NBA team an advantage in the tough world of basketball. Nowadays, every NBA team has its own team of full-time data scientists who are focused on analyzing data and extracting information for the coach.