In this module, you will apply data wrangling techniques learned in previous courses to clean and prepare the collected datasets for analysis. Working with the data gathered in Module 1, you will transform raw data into a structured and analysis-ready format. You will clean text data, standardize variables, handle missing values, and perform data transformations such as encoding and normalization. By the end of this module, you will have prepared a reliable dataset that supports meaningful exploration and modeling in later stages of the project.

At this stage of the capstone project, you will apply the data collection and data wrangling skills developed in the previous modules, along with your prior experience in SQL querying and data visualization. This module focuses on performing Exploratory Data Analysis (EDA) to better understand the patterns, relationships, and trends within the prepared datasets. You will work with the datasets generated in earlier modules to explore key variables, identify meaningful insights, and prepare the data for predictive modeling. If you encountered challenges in earlier steps, prepared datasets are available to help you continue progressing through the project. In this module, you will complete a series of hands-on labs that guide you through the essential stages of exploratory analysis.

In this module, you will apply regression modeling techniques to build predictive models for bike-sharing demand using the prepared dataset. Drawing on modeling concepts learned earlier, you will construct and refine multiple regression models to improve prediction accuracy. You will evaluate model performance using appropriate statistical metrics and interpret the contribution of different predictor variables. This stage represents the transition from data exploration to predictive analysis within your capstone workflow.

In this module, you will apply your data visualization and application development skills to create an interactive dashboard that presents the results of your predictive analysis. Using R Shiny and visualization tools, you will design a dashboard that enables users to explore predicted bike-sharing demand across locations. This module focuses on transforming analytical results into interactive visual tools that support data-driven decision-making.

In this final module, you will consolidate the results of your capstone project into a professional presentation that communicates your workflow, analysis, insights, and predictive results. You will prepare a structured presentation that highlights the project problem, methodology, key findings, and conclusions. This module represents the culmination of your learning journey, where you demonstrate your ability to apply data science skills to solve a real-world problem and communicate your results effectively.