Data Exploration Using RStudio

Exploratory Data Analysis (EDA) is an important stage in the data analysis process aimed at understanding the characteristics of the data before modeling or further analysis is conducted. Through EDA, analysts can identify patterns, trends, relationships between variables, unusual values (outliers), and potential issues in the data that may affect the analysis results. In addition, the data exploration process also aids in decision-making regarding appropriate analysis and visualization strategies. In this course, participants will learn the basic concepts of data exploration, data preprocessing techniques, data exploration strategies, and the application of data visualization using RStudio. There are ten module topics in this course, namely:

1. Introduction to Exploratory Data Analysis (Theory)
2. Data Preprocessing Part I (Theory)
3. Data Preprocessing Part II (Theory)
4. Strategy for Data Exploration (Theory)
5. Exploring Dataset in RStudio (Theory + Code)
6. Centrality, Variability, and Unusual Value (Theory)
7. Set Working Directory in RStudio (Code)
8. Data Storytelling (Theory)
9. Interactive Visualization (Theory)
10. Interactive Visualization in RStudio (Code)

The instructional videos in this course last about 10–15 minutes for each module. Additionally, participants need an extra 20–30 minutes to practice coding and complete case studies using RStudio. Thus, the total time required to complete all the material ranges from 250 to 400 minutes.
After completing this course, participants are expected to understand the basic concepts of Exploratory Data Analysis (EDA), perform data cleaning and preprocessing, and apply effective data exploration strategies. Participants are also expected to use RStudio to explore datasets, calculate measures of central tendency and dispersion, identify outliers, and present analysis results through informative and interactive data visualizations. Additionally, participants are expected to communicate their analysis findings through engaging and easily understandable data storytelling approaches. In each module, participants will be provided with case studies and implementation demonstrations using RStudio to reinforce their understanding of concepts and practical skills. Therefore, it is highly recommended for participants to prepare an RStudio working environment and personal notes during the learning process to maximize their understanding of the material presented.