This interactive course will lead students through the fundamentals of data analysis and modeling using Python data science libraries. We will focus on the Pandas data frame library as a means of reading, cleaning, and organizing data and explore the Python programming language that underlays its framework. We will explore visualization capabilities, both within Pandas and using the Seaborn statistical visualization library. On the final day, you will learn how to model data using basic linear regression techniques within Pandas and scikit-learn, with an introduction to the sophisticated scikit-learn machine learning framework.
LEARNING OBJECTIVES:
*Understand data acquisition and cleaning, using Python tools (Pandas and others).
*Be able to choose useful visualizations of data sets or aspects, and use relevant tools for their creation (Matplotlib and Seaborn).
*Learn to create and evaluate regression and classification models using supervised learning techniques (scikit-learn)
RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE:
*Basic statistics knowledge
*Basic familiarity with Python programming
