The workshop addresses the fundamentals of statistics management with Python. It was designed to be lectured at the Artificial Intelligence and Computational Methods in the Social Sciences lab - QLAB PUCP. The objective is to give the student the fundamental knowledge to perform main statistical tasks in Python. The Python codes will be written and executed in Jupyter Notebook. Students will be provided with the necessary databases to be able to run the codes.
In the first session, we will cover descriptive statistics, providing tools to calculate measures of central tendency, spread, and summary statistics. Following that, we'll delve into probability calculations and sampling techniques, including both with and without replacement. Additionally, we'll introduce discrete and continuous probability distributions.
In the second session, we will cover more statistical concepts, starting with the Central Limit Theorem (CLT) and its applications in various summary statistics. We will explore additional probability distributions using scipy.stats, including Poisson, Exponential, Student's t, and Log-normal. The session concludes with practical insights into correlation in Python, emphasizing visualization techniques and variable transformations for effective data analysis.