Course Description

Using Python for Research Python is a powerful and versatile programming language that is widely used in research across various fields such as physics, biology, economics, and social sciences. This course is designed to teach researchers how to use Python to perform data analysis, visualization, and modeling tasks. The course begins with an introduction to Python, including installation and configuration of the programming environment, basic data types, control structures, and functions. Next, students learn about the NumPy and Pandas libraries, which are essential for data manipulation and analysis. They will explore how to read and write data files, filter and select data, and perform descriptive statistics. The course also covers data visualization using Matplotlib and Seaborn libraries. Students will learn how to create different types of charts, graphs, and plots, including scatter plots, line charts, histograms, and box plots. They will also learn how to customize the appearance of plots, add annotations, and combine multiple plots. In addition to data analysis and visualization, Python is also widely used for modeling and simulation. The course covers the basics of scientific computing with Python, including solving differential equations, numerical integration, and Monte Carlo simulations. Students will learn how to implement mathematical models in Python and visualize the results. Finally, the course introduces machine learning using the Scikit-learn library. Students will learn how to train and evaluate different types of machine learning models, including linear regression, decision trees, and support vector machines. They will also learn how to perform feature selection, hyperparameter tuning, and cross-validation. Throughout the course, students will work on real-world research projects and use Python to solve research problems. By the end of the course, they will have a solid understanding of Python and its applications in research, as well as practical skills to apply Python to their own research projects. Author: (edX Harvard)