This introductory tutorial covers the very first steps how to start working with Python (installation, different IDEs, where to get help, and resources to learn Python) and brings you closer to the first coding.

How to fit reinforcement learning models to behavioral data using Maximum Likelihood Estimation (MLE). The main goal is to show how to answer research questions using modeling. Post goes over important steps of modeling, such as model selection, model validation, and data generation.

This post serves as an introduction to the EEG data processing and particularly the usage of MNE-Python package. The post goes over such preprocessing steps as labeling bad channels and trials, artifacts removal, and data epoching. Additionally, the event-related potential is calculated and compared between groups and conditions. Sample of EEG data is taken from Cavanagh et al. (2019) experiment.

Overview of the power and the effect size of the test and why low p-value is not always enough to make a decision.

Introduction to the inference for a population proportion using binomial test and $\chi^2$ test for independence.

Basic concept of the NHST and the idea of p-value. Overview of the inference for a population mean using one/two-sample $t$-tests and ANOVA.

The main types of charts. How to build graphs in Python using `matplotlib`, `seaborn` and `plotly` packages.

Overview of how classification and regression trees work behind the scenes using the binary classification example.

Overview of Python `numpy`, `pandas` and `sympy` packages for Data Science.

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