Given that data science is a relatively newer discipline (the term first referenced in 1960), there doesn’t seem to be one agreed upon answer when someone asks, “What is data science?”. The following paragraphs aren’t an attempt to come up with the perfect answer, but they will serve in a gathering of all the pieces that make up data science and why they are important. So enjoy gaining a better grasp of this universe.
League of Legends is a Multiplayer Online Battle Arena (MOBA) video game that has become one of the most popular games in the world. Over 27 million people play the game daily across the globe for fun and rank, as well as 13 professional leagues with teams owned by other professional sports organizations. With the rise in popularity of E-Sports in the mainstream media and across global cultures, League of Legends has become the community’s poster child over the last 5 years.
My first attempt at creating a convolutional neural network equally tested me on my patience and understanding of how they work. On a machine with limited capabilities, I pursued creating a Convolutional Neural Network (CNN) that can identify an image of numerous traffic signs with high accuracy. This problem is a great way to learn and understand convolutional neural networks, as this problem and system is used within self driving cars. Self driving cars need to be able to detect traffic signs, correctly identify what the sign means, and act upon it to safely transport passengers to their destination. I retrieved a dataset of over 30,000 images of 43 different traffic signs, and created a CNN that could identify them with as high of accuracy as I could.
There are numerous game statistics recorded for each NBA game. These statistics cover almost every aspect of each game and can essential tell most of the story on how a game was won. Statistics that are captured range from things like total baskets made, total shots attempt, assists, steals, turnovers, rebounds, and many more. Which of these statistics has the most important in determining who comes out the winner of a game? Do some statistics stand out as more important than others? Has the importance of these individual statistics changed over the seasons as the professional game has evolved? The below analysis is my attempt to answer these questions.
Probably Approximately Correct Learning Theory, or PAC learning Theory, provides a mathematical definition of what machine learning is. It is the theory behind how we can create and use models of a data set for classification and regression problems in a supervised machine learning setting. It is what describes how the machine learns from examples in mathematical form. The theory answers whether there’s an algorithm which can produce a good model when given random data.