This course provides a thorough understanding of each of the key Python libraries used for data science -- NumPy, Pandas, Matplotlib and Scikit-learn, known as the Python data stack. We will perform data exploration, analysis, visualization and modeling.
In six, half-day sessions of hands-on training, you can quickly become a knowledgeable, productive, and efficient Data Science professional and earn a Stanford Technology Training Certificate of Proficiency in Data Science.
We will begin by discussing the data science process and how to effectively work through a data science problem. We'll talk about how to clean, transform, and prepare data for analysis. We will also cover descriptive and inferential statistics which will enable you to perform hypothesis testing so that you can better interpret the significance of your analysis. We will also focus on machine learning and predictive analytics. We'll discuss the various ways to measure model performance, how to select the best model for your project, and ways to refine that model.
