Theory: Building a data science team

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. --- In this lesson, you'll learn how to build and structure your data team to meet your organization's needs. You might be surprised to learn that "Data Science" isn't a single field; it's actually three different jobs: Data Engineer, Data Analyst, and Machine Learning Scientist. Let's explore each one. Data engineers control the flow of information: they build specialized data storage systems and the infrastructure to ensure that the data is easy to obtain and process. Most data engineers are very familiar with SQL, which they use to store and manage big data. They also use one of the following programming languages like Java, Scala, or Python to process data and automate data-related tasks. Data analysts describe the present via data. They do this with dashboards, hypothesis tests, and visualization. They often have some background in statistics or computer science, but tend to have less engineering experience than data engineers and less math experience than data scientists. Data analysts use spreadsheets to perform simple analyses on small quantities of data. They use SQL, the same language used by data engineers, for larger analyses. While data engineers build and configure SQL storage solutions, data analysts use existing databases to consume and summarize data. Analysts also use Business Intelligence, or BI, Tools, such as Tableau, Power BI, or Looker, to create dashboards and share their analyses. Machine learning is perhaps the buzziest part of Data Science; it's used to extrapolate what's likely to be true from what we already know. These scientists use training data to classify larger, unrulier data. Machine learning can tell us how much money a stock might be worth next week, which images contain a car, or what sentiments are expr