The ability to understand and work with data has become increasingly important in today's world, where data is ubiquitous and valuable. The first module in this course covers a range of topics, including what data is and its different types, what "big" data looks like, and how companies are using it. It also explores the fields of data analysis and data science, and how the two come together to help form the field of data analytics.

There are multiple ways of storing and accessing data, with a variety of deployment strategies and storage options, including databases, data warehouses, and data lakes. In week 2, we’ll look at accessing data in a database. We’ll provide some context around relational databases, and then do a deep dive into SQL, which is a query language used for accessing and updating databases.

Real-world problems are often hidden behind large amounts of data. Through exploratory data analysis (EDA), we can distill valuable information from data and make reliable predictions about it. In other words, Python and data analytics provide computer scientists with effective tools for understanding, processing, and utilizing real-world data. This module will take an in-depth look at how to use the powerful Python libraries pandas and NumPy to perform key tasks such as loading, querying, cleaning, summarizing, and visualizing data.