• Understand the principles and best practices of data visualization and EDA
• Use R and ggplot2 to create custom plots for different types of data
• Use descriptive statistics, probability distributions, hypothesis testing, and regression analysis to summarize and explore data
• Identify and handle mistakes, biases, errors, and outliers in data
• Communicate and present your data-driven findings using data storytelling

This course is designed for professionals who want to learn how to use data visualization and EDA to discover and communicate insights from data. It is suitable for professionals from any industry, function, or region who are interested in or responsible for data analysis in their organization or personally. It is also suitable for those who are currently taking a basic machine learning course or have already finished a machine learning course and are searching for a practical data visualization and analysis project course.

Day One:

Introduction to Data Visualization and EDA

• What is data visualization and EDA and why are they important for data analysis?
• Data visualization process and pipeline
• Data types, sources, and quality
• Data visualization tools and platforms
• Data visualization applications and use cases in various domains

Day Two:

Data Visualization with R and ggplot2

• What is R and ggplot2 and how to use them for data visualization?
• Grammar of graphics and aesthetics
• Geometric objects, scales, facets, coordinates, themes, etc.
• Plotting univariate, bivariate, multivariate, categorical, numerical, temporal, spatial, etc. data
• Customizing and saving plots

Day Three:

Descriptive Statistics and Probability Distributions

• What are descriptive statistics and probability distributions and how to use them for EDA?
• Measures of central tendency, variability, shape, correlation, association, etc.
• Probability theory and rules
• Probability distributions such as binomial, normal, Poisson, exponential, uniform, etc.
• Expected value, variance, standard deviation, coefficient of variation, etc.

Day Four:

Hypothesis Testing and Regression Analysis

• What are hypothesis testing and regression analysis and how to use them for EDA?
• Sampling methods such as random, stratified, cluster, systematic, etc.
• Hypothesis testing methods such as t-test, ANOVA, chi-square test, etc.
• Regression analysis methods such as linear, logistic, multiple, etc.
• Model evaluation and validation

Day Five:

Data Storytelling

• What is data storytelling and why is it important for communicating data-driven findings?
• Data storytelling principles and practices
• Data storytelling tools and platforms
• Data storytelling techniques such as narrative, context, visuals, interactivity, etc.
• Data storytelling examples and best practices

To enhance learning and practical application of concepts, the training course will use a combination of interactive lectures, case studies, group discussions, practical exercises, and real-world examples. Participants will also get the chance to collaborate on group projects and create action plans adapted to the needs of their respective organizations.