Data Science Fundamentals

We will first discuss exploratory data analysis and feature selection. Next, we'll explore feature engineering and other common methods for understanding and preparing the data for analysis. We will then explore some of the different algorithms that can be used to create both unsupervised or supervised machine-learning models. We will also discuss how to use validation/resampling techniques to train and test the data. We will also discuss evaluation techniques that can be used to evaluate the model's performance, and how to improve it based on those evaluations. The data science cycle will be demonstrated using a real-life example.

Data science allows us make data-driven insights. This Data Science Fundamentals course will provide an overview of data science and its use in finance and business. We will walk through the data science cycle to learn how to create machine learning models that can make predictions.