The workshop will provide an introduction to graphical data analysis techniques used to explore, summarize, and communicate data. Students will learn how to create and interpret various types of graphs and plots to gain insights into data patterns and relationships. Emphasis will be placed on selecting appropriate visualizations based on data types and research questions, as well as communicating findings effectively. By the end of this course, students should be able to: Understand the principles of graphical data analysis and their applications. Create and interpret different types of graphs and plots (e.g., histograms, scatterplots, box plots). Choose appropriate visualizations based on data types and research questions. Identify patterns and relationships in data using visualizations. Communicate findings effectively through graphs and plots. Also, introduce statistical methods used to analyze and interpret data. Students will learn how to use descriptive statistics to explore data and drive business insights. Emphasis will be placed on understanding and applying statistical concepts, as well as interpreting and communicating findings effectively. It also will introduce machine learning regressors, mainly linear regression. Students will learn how to train, evaluate, and apply regressors to predict continuous target variables from input features. Emphasis will be placed on understanding and applying the principles of machine learning, as well as interpreting and communicating model predictions effectively. Additionally, practice machine learning classifiers, including logistic regression, decision trees, naive bayes, and neural networks. Students will learn how to train, evaluate, and apply classifiers to predict categorical target variables from input features.