Theory: Applications of Data Science

Want to learn more? Take the full course at https://learn.datacamp.com/courses/data-science-for-business at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Previously, you learned the definition of data science and the steps in a data science workflow. In this lesson, you'll learn how to apply data science to real business problems. Let's take a deep dive into three exciting areas of Data Science: traditional machine learning, the Internet of Things, and Deep Learning. Suppose you work in fraud detection at a large bank. You'd like to use data to determine the probability that the transaction is fake. To answer this question, you might start by gathering information about each purchase, such as the amount, date, location, purchase type, and card-holder's address. You'll need many examples of transactions, including this information, as well as a label that tells you whether each transaction is valid or fraudulent. Luckily, you probably have this information in a database. These records are called called "training data", and are used to build an algorithm. Each time a new transaction occurs, you'll give your algorithm information, like amount and date, and it will answer the original question: What is the probability that this transaction is fraudulent? Before we can answer that question, let's walk through our example and highlight what we need for machine learning to work its magic. First, a data science problem begins with a well-defined question. Our question was "What is the probability that this transaction is fraudulent?" Next, we need some data to analyze. We had months of old credit card transactions and associated metadata that had already been identified as either fraudulent or valid. Finally, we need additional data every time we want to make a new prediction. We needed to have the same type of information on every new purchase so that we could label it as "fraudulent" or "valid". N