Statistical Programming and Data Analysis

Using programming languages like R, Mplus, Python, and others can be incredibly beneficial for you when analyzing data. These languages provide easy-to-use functions and libraries that help you make sense of large amounts of information. With them, you can quickly find patterns, visualize data, and make informed decisions. Plus, they make it easy for you to share and reproduce your results, which is essential for your work or research. In this subfolder, we want to make it easy for you to start. You will find commented scripts that use actual data from our research groups for different languages. These scripts make it easy for you to clean, analyze and visualize your data, even if you have very little knowledge.

Data Cleaning

When it comes to data cleaning, you're finding and fixing mistakes in your data. You'll remove duplicates, fix errors, deal with missing information, and spot outliers. Your goal is to make sure your data is accurate and ready for analysis, so you can trust the results you get from it.

Data Analysis

When you dive into data analysis, you're exploring data to find patterns and insights that help you make decisions. You'll summarize data, spot relationships, make predictions, and forecast future trends. Your goal is to understand your data better, so you can solve problems and make smart choices.

Data Visualization

With data visualization, you're showing data in graphs, charts, and maps to make it easier for you to understand. It helps you see patterns and trends in your data quickly. Using visualizations, you can explore your data, tell stories, and make better decisions based on what you see.