Let’s be clear. Doing data science isn’t exactly *simple*. However, it’s entirely possible to go from only knowing basic math to understanding the ideas behind the most popular techniques in data science. If you can add, subtract, multiply, and divide, and feel pretty comfortable with decimals and percentages and fractions, you can understand fancy-sounding methods like *support vector machines*, and *linear regression*, and *hierarchical clustering*, all of which are techniques used by professional data scientists.

To give a quick example: The price of an item and how many people decide to buy are related. The higher the price, the fewer get purchased. The idea behind *linear regression* is just to use math to draw a line on a graph that best summarizes the relationship. (For those in algebra, it’s an application of the slope-intercept formula.)

We will be talking about what the techniques are, how they work, and when they might be more or less useful. This class is a calculation-light sampler of what you could do if you decided to learn more math and become a data scientist. Or if you don’t want to *be* a data scientist, you will at least be better able to understand what they say and do.

If you took *Thinking with Math* or *Living with Math*, this class will be aimed at being only slightly more “mathy.” It’ll be less mathy than Algebra I.

About the instructor: Michael describes chocolate as "delicious."