Hello. Welcome to the Data Analytics in Accounting Capstone course. My name's Linda Lou and I will be your professor for this course. This capstone is the last course in accounting Data Analytics Specialization, which contains the following courses: Introduction to Accounting Data Analytics and Visualization, Accounting Data Analytics with Python, Machine Learning for Accounting with Python, and lastly, this course. Before taking this capstone course you should have completed all three previous courses. In this capstone course you're going to take the knowledge and skills you have acquired from the previous courses and apply them to a real-world problem. For this capstone project you will be provided with a loan dataset from Lending Club, which is the largest peer-to-peer lending platform. You will explore the characteristics of the features in the dataset through statistical analysis, exploratory data analysis, and visualization. You will also create a machine learning model to predict whether a loan will be repaid or not. Finally, you will construct a portfolio with the help of your analysis. The goal is to create a portfolio that achieves better return than the overall return of all loans on the Lending Club platform. You will be working with three files in this course. The first file is the Lending Club long dataset, which is the CSV file that contains the information of approximately 20,000 loans. The second file is the data dictionary file that explains the meanings of column names in the loan dataset. The third file, which is also the centerpiece of this capstone is the product template notebook. It is a Jupyter Notebook file. You will complete all your analysis within the product notebook file. You can open and work on a product notebook file directly on Coursera. However, it is highly recommended that you download the three files to your local machine so you can work on your own Jupyter Notebook server. You may start your own server, we're Anaconda, which we have talked about in the previous courses. To locate and download the three files, go to the Coursera page and you can find the three files in week one Course Orientation section. You can also open the product notebook on the Coursera server. There is a capstone notebook item within each module. Feel free to add new code or modify existing code in the notebook. There's a copy of the product template file under the same directory, if you need to refer to the original template file you can open it this way. Click file, open to go to the directory reveal after Jupyter Notebook server. There are four files under current server. The data dictionary file, the dataset file, and two notebooks. The first notebook is the notebook you will be working on and the second notebook is a copy of the original template. If you need to refer to the original copy you can open the notebook this way. The product template is designed based on the CRISP-DM framework. As you complete the product, you will follow the steps in the framework from developing business understanding, data understanding, data preparation, modeling, to model evaluation in the template notebook. You will complete business understanding and data understanding in module 1, you will work on data preparation, modeling, and model evaluation in module 2 and in module you will construct a loan portfolio with the help of the analysis you have done in module 1 and module 2. Each module is corresponding to a part in the capstone template notebook. In the notebook under each module, there are some sample code which demonstrates some techniques you may use to complete the tasks in the module. After you complete the tasks in each module, you will be able to answer the quiz questions and complete the peer review assignment on Coursera. There are three modules in this course, each module has lecture video. In the lecture video, I will conduct demonstrations in the product notebook. I will also explain the key concepts and introduce the techniques you can use to complete the tasks. You should have the product notebook open while watching the lecture videos. Be prepared to pause the videos and practice in the notebook frequently. After you finish all the tasks in a module, you will be able to answer quiz questions in module 1 and module 2 and complete the peer review assignment in module 3. To pass the course, you will need to complete module 1 and module 2 quizzes, and module 3 peer review assignment. Your peer review assignment will be graded by your peer. You are also required to grade and give feedback to at least two of your peers. You will be provided with a rubric for the assignment review.
