In this module, you will learn how to clean and prepare datasets for analysis through various data wrangling techniques. You will work with web-scraped data and apply methods such as renaming columns, cleaning text using regular expressions, and removing unnecessary links or characters. You will also learn how to handle missing data, convert categorical values into numeric formats, and perform data normalization to prepare the dataset for further analysis. Through hands-on labs, you will practice using functions and data manipulation techniques to transform raw data into a clean and structured format.

At this stage of the Capstone Project, you have gained some valuable working knowledge of data collection and data wrangling. You have also learned a lot about SQL querying and visualization. Congratulations! Now it's time to apply some of your new knowledge and learn about Exploratory Data Analysis (EDA) techniques, again through practice. You can use the datasets you wrangled in the previous Module. However, if you had any issues completing the wrangling, no worries - we have prepared some clean datasets for you to use. You will be asked to complete three labs:

In this module, you will learn how to build and evaluate regression models to predict hourly bike-sharing demand using weather and datetime data. You will begin by constructing a baseline linear regression model and then improve the model by incorporating polynomial, interaction, and regularization terms. Through hands-on labs, you will compare different models and evaluate their performance using metrics such as R-squared and RMSE. You will also analyze the influence of predictor variables by examining and visualizing their coefficients.

In this module, you will learn how to build an interactive dashboard application using R Shiny to visualize bike-sharing demand predictions. You will create a dashboard that integrates a Leaflet map to display predicted demand across different cities and allows users to explore the results through interactive controls such as dropdown menus. You will also enhance the dashboard by incorporating data visualizations with ggplot2 to display detailed bike-sharing demand trends for selected cities. Through hands-on labs, you will gain practical experience in designing and improving interactive data applications.

In this final module, you will focus on presenting the results of your capstone project. You will create a comprehensive PowerPoint presentation that highlights the key steps of your analysis, the insights you discovered, and the outcomes of your predictive modeling work. You will learn best practices for structuring and communicating data-driven findings effectively. After preparing your presentation, you will submit your final project either through an AI-graded submission or a peer-reviewed submission.