 What is data science? Maybe you've heard of business intelligence, artificial intelligence, machine learning, computational science, data analytics, data mining, or some other similar phrase. All of these terms can get used in different contexts to talk about working with data and using programming and statistics to solve real-world problems. Data science cannot exist without some sort of real-world problem to solve and working with somebody who has expertise in that particular content area. For example, you wouldn't really do an analysis of MBA statistics without understanding something about the rules of basketball. This course will pose problems in which you already have some content expertise and provide opportunities to gain experience in new ones. This course is designed to be very collaborative and you'll be encouraged to work with your fellow classmates when solving data science problems. So what will you be learning? Data science as a practice has three core activities. The first of which is exploration. Data exploration is figuring out what patterns exist in your data, often through the use of data visualizations. It also helps us discover patterns in the data that just looking at a table of numbers may not uncover. Perhaps we can study the usage data for our web application or maybe something a little bit more light-hearted like the trends in TV shows. The next part of data science that we'll study is statistical inference. Data scientists often calculate a result from a small sample of individuals taken from a larger population. Then they need to determine if that observation was significant or just due to the random chance of drawing a lucky or unlucky sample. Through the use of computer simulations, we can see what kind of patterns would appear in our samples just by chance alone and compare those results with our observed data to determine what, if anything, we can conclude about the larger population. Lastly, we'll be learning how to make predictions from data. This is where we'll have some partial information about something we wanna know more about and we wanna guess about the things that we don't know yet. We'll make informed predictions, not just random guesses, using classical techniques like regression and more modern machine learning processes like classification. In this course, you will learn new technological skills to help you solve problems, but you won't only be programming. This course will consist of a mix of programming and non-programming tasks, some of which you'll complete on your own while others will be group activities. By taking this course, you will be able to understand the statistics and mathematics of data science as well as learning how to program a computer using the Python language to run all of the analysis and simulations. Once you can do all of that, you'll be well on your way to becoming a data scientist.