Task 1: The beginning of any data science project involves understanding the business and the data. Business understanding is focused on setting the business objectives, project assessment, defining the success criteria and project planning. Understanding the business is essential to any data science project. After this, the next phase is Data Understanding. To understand the data, we have to identify, collect, analyze and verify data quality from one or multiple data sources to accomplish the business goals. These tasks can be done in SQL. SQL is a crucial skill to have as a data scientist, as you will be required to work with structured data stored in relational databases. To complete this task, answer the multiple-choice quiz. This multiple choice consists of daily tasks data scientists complete using SQL. Start the quiz by clicking 'Click here to start the task' in section 5 below. Please note there are 5 multiple choice questions to complete in this task. Please be patient as each question loads.
Task 2: In this task, you will make use of automated machine learning as well as traditional machine learning. The manager of the home loans department, who has provided us with sample data, wants to know a few things about the data. The questions about the data can be found in the notebook provided in the Resources section below.The manager is also interested in understanding what machine learning really is with particular use case. We will use Python and its extensive collection of libraries to derive valuable insights from the data, prepare the data and train machine learning models - the old fashioned way and in newer, automated ways. We have provided two datasets in the form of CSVs and Jupyter Notebook. The notebook has been annotated to assist you.
Task 3: In this task, you will prepare to present not only to the manager of the home loans department but also to your manager/team. To cover the above considerations, we recommend you structure the presentation in a similar manner to the template provided in Resources. Other considerations include the following:
a. Keep it simple by using business terms b. Visuals make things easier to understand c. Define any terms that might be too technical before (or right after) you use/mention them d. Try to tell a story